U.S. patent application number 16/044247 was filed with the patent office on 2019-01-24 for method and apparatus for providing information by using degree of association between reserved word and attribute language.
The applicant listed for this patent is MYCELEBS CO., LTD.. Invention is credited to Jun Woong DOH.
Application Number | 20190026282 16/044247 |
Document ID | / |
Family ID | 65018723 |
Filed Date | 2019-01-24 |
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United States Patent
Application |
20190026282 |
Kind Code |
A1 |
DOH; Jun Woong |
January 24, 2019 |
METHOD AND APPARATUS FOR PROVIDING INFORMATION BY USING DEGREE OF
ASSOCIATION BETWEEN RESERVED WORD AND ATTRIBUTE LANGUAGE
Abstract
Disclosed herein are a method and apparatus for providing
information by using an attribute language. The method includes
storing the degree of object-keyword association corresponding to
an object item-representative attribute keyword pair; storing the
degree of basic reserved word-keyword association corresponding to
a reserved word-representative attribute keyword pair; acquiring a
received reserved word; acquiring the degree of reserved
word-object association corresponding to a pair of the received
reserved word and each object item by using the degree of
object-keyword association and the degree of basic reserved
word-keyword association; and providing an object item based on the
degree of reserved word-object association corresponding to the
pair of the received reserved word and the object item.
Inventors: |
DOH; Jun Woong; (Seoul,
KR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MYCELEBS CO., LTD. |
Seoul |
|
KR |
|
|
Family ID: |
65018723 |
Appl. No.: |
16/044247 |
Filed: |
July 24, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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PCT/KR2017/007964 |
Jul 24, 2017 |
|
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16044247 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/93 20190101;
G06F 16/313 20190101; G06F 16/3344 20190101; G06F 16/951 20190101;
G06F 16/285 20190101; G06F 16/338 20190101; G06F 16/248 20190101;
G06F 16/24578 20190101 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Foreign Application Data
Date |
Code |
Application Number |
Aug 7, 2017 |
KR |
10-2017-0099828 |
Sep 20, 2017 |
KR |
10-2017-0121289 |
Mar 22, 2018 |
KR |
10-2018-0033093 |
Claims
1. A method of providing information, the method comprising:
storing a degree of object-keyword association corresponding to an
object item-representative attribute keyword pair; storing a degree
of basic reserved word-keyword association corresponding to a
reserved word-representative attribute keyword pair; acquiring a
received reserved word; acquiring a degree of reserved word-object
association corresponding to a pair of the received reserved word
and each object item by using the degree of object-keyword
association and the degree of basic reserved word-keyword
association; and providing an object item based on the degree of
reserved word-object association corresponding to the pair of the
received reserved word and the object item.
2. The method of claim 1, wherein acquiring the degree of reserved
word-object association comprises: for the object
item-representative attribute keyword pair, acquiring an adjusted
degree of object-keyword association corresponding to each object
item-representative attribute keyword pair for the received
reserved word by applying the degree of basic reserved word-keyword
association corresponding to the pair of the received reserved
word-representative attribute keyword to the degree of
object-keyword association corresponding to the object
item-representative attribute keyword pair; and acquiring the
degree of reserved word-object association corresponding to the
pair of the received reserved word and each object item by using
the adjusted degree of object-keyword association.
3. The method of claim 2, wherein acquiring the adjusted degree of
object-keyword association comprises: setting an adjusted degree of
object-keyword association corresponding to each object
item-representative attribute keyword pair for the received
reserved word so that the adjusted degree of object-keyword
association has a positive correlation with the degree of
object-keyword association corresponding to the object
item-representative attribute keyword pair and also has a positive
correlation with the degree of basic reserved word-keyword
association corresponding to the pair of the received reserved word
and the representative attribute keyword.
4. The method of claim 3, wherein acquiring the degree of reserved
word-object association comprises: setting a degree of reserved
word-object association corresponding to the pair of the received
reserved word and a specific object item so that the degree of
reserved word-object association has a positive correlation with a
cumulative value of the adjusted degree of object-keyword
association for the specific object item.
5. The method of claim 2, wherein setting the adjusted degree of
object-keyword association comprises: acquiring an adjusted degree
of object-keyword association corresponding to each object
item-representative attribute keyword pair for the received
reserved word by using a value obtained by multiply the degree of
object-keyword association corresponding to the object
item-representative attribute keyword pair by the degree of basic
reserved word-keyword association corresponding to the pair of the
received reserved word and the representative attribute keyword for
each object item-representative attribute keyword pair.
6. The method of claim 5, wherein acquiring the degree of reserved
word-object association comprises: acquiring a degree of reserved
word-object association corresponding to a pair of the received
reserved word and a specific object item by using a cumulative
value of the adjusted degrees of object-keyword association for the
specific object item.
7. An apparatus for providing information, the apparatus
comprising: a storage unit configured to store a degree of
object-keyword association corresponding to an object
item-representative attribute keyword pair, and to store a degree
of basic reserved word-keyword association corresponding to a
reserved word-representative attribute keyword pair; a
communication unit configured to acquire a received reserved word;
and a control unit configured to acquire a degree of reserved
word-object association corresponding to a pair of the received
reserved word and each object item by using the degree of
object-keyword association and the degree of basic reserved
word-keyword association; wherein the communication unit provides
an object item based on the degree of reserved word-object
association corresponding to the pair of the received reserved word
and the object item.
8. The method of claim 7, wherein the control unit: acquires an
adjusted degree of object-keyword association corresponding to each
object item-representative attribute keyword pair for the received
reserved word by, for the object item-representative attribute
keyword pair, applying the degree of basic reserved word-keyword
association corresponding to the pair of the received reserved word
and the representative attribute keyword to the degree of
object-keyword association corresponding to the object
item-representative attribute keyword pair; and acquires the degree
of reserved word-object association corresponding to the pair of
the received reserved word and each object item by using the
adjusted degree of object-keyword association.
9. The apparatus of claim 8, wherein the control unit: sets an
adjusted degree of object-keyword association corresponding to each
object item-representative attribute keyword pair for the received
reserved word so that the adjusted degree of object-keyword
association has a positive correlation with the degree of
object-keyword association corresponding to the object
item-representative attribute keyword pair and also has a positive
correlation with the degree of basic reserved word-keyword
association corresponding to the pair of the received reserved word
and the representative attribute keyword.
10. The apparatus of claim 9, wherein the control unit: sets a
degree of reserved word-object association corresponding to the
pair of the received reserved word and a specific object item so
that the degree of reserved word-object association has a positive
correlation with a cumulative value of the adjusted degree of
object-keyword association for the specific object item.
11. The apparatus of claim 8, wherein the control unit: acquires an
adjusted degree of object-keyword association corresponding to each
object item-representative attribute keyword pair for the received
reserved word by using a value obtained by multiply the degree of
object-keyword association corresponding to the object
item-representative attribute keyword pair by the degree of basic
reserved word-keyword association corresponding to the pair of the
received reserved word and the representative attribute keyword for
each object item-representative attribute keyword pair.
12. The apparatus of claim 11, wherein the control unit: acquires a
degree of reserved word-object association corresponding to a pair
of the received reserved word and a specific object item by using a
cumulative value of the adjusted degrees of object-keyword
association for the specific object item.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims priority under 35 U.S.C. .sctn. 119
to Korean Patent Application No. 10-2017-0099828 filed on Aug. 7,
2017, Korean Patent Application No. 10-2017-0121289 filed on Sep.
20, 2017, PCT/KR2017/007964 filed on Jul. 24, 2017 and Korean
Patent Application No. 10-2018-0033093 filed on Mar. 22, 2018, in
the Korean Intellectual Property Office (KIPO), the disclosure of
which is incorporated by reference herein in its entirety.
1. TECHNICAL FIELD
[0002] At least some exemplary embodiments of the present
disclosure relate to a method and apparatus for providing
information by using the degree of association between a reserved
word and an attribute language.
2. DISCUSSION OF RELATED ART
[0003] According to conventional search methods, a user can search
for a desired web document or the like by entering a search keyword
into a search box. For example, a user may retrieve information
about the movie "Interstellar" by entering the title of the movie
"Interstellar" into the search box. However, if a user cannot
remember the title of a movie which he or she desires to search
for, he or she needs to provide another type of information. For
example, a user may attempt a search by entering an actor,
director, producer, or the like of a movie which he or she desires
to search for. There are many cases where movie information sites
and movie review sites provide cast information as well as movie
information, and thus the user can search for a desired movie by
using an actor, a director, a producer, or the like as a keyword
unless he or she is unlucky.
[0004] Meanwhile, the conventional search methods cannot be used if
information to be used is information based on an atypical
language, for example, an emotional language, rather than typical
information. For example, responses provided by conventional search
engines for a search term, such as "a funny movie" or "a movie
which is viewed when a viewer is sad," are merely search results,
including documents which have been written to include the keyword
"a funny movie" or "a movie viewed when a viewer is sad." However,
an atypical language requires an approach different from that for
typical information, such as a starring actor, a (typical) movie
genre, and a year of release. Even if documents have not been
written to include the keyword "a funny movie" or "a movie viewed
when a viewer is sad," there could be a lot of movies for which
many people might feel is "fun" or "sad." Furthermore, for other
fields than film, a different approach may be required for
requesting information by using an atypical language.
SUMMARY
[0005] At least some exemplary embodiments of the present
disclosure are directed to a method and apparatus for efficiently
providing information by using a reserved word.
[0006] According to an exemplary embodiment of the present
disclosure, there is provided a method of providing information,
the method including: extracting a representative attribute keyword
candidate set including representative attribute keywords; setting
a reserved word set including reserved words; storing the degrees
of object-keyword association corresponding to object
item-representative attribute keyword pairs; storing the degrees of
basic reserved word-keyword association corresponding to reserved
word-representative attribute keyword pairs by using association
weights corresponding to representative attribute
keyword-subordinate keyword pairs and the degrees of basic reserved
word-subordinate keyword association corresponding to reserved
word-subordinate keyword pairs; acquiring a received reserved word;
acquiring the degree of reserved word-object association
corresponding to a pair of the received reserved word and each
object item by using the degrees of object-keyword association and
the degrees of basic reserved word-keyword association; and
providing an object item based on the degree of reserved
word-object association corresponding to the pair of the received
reserved word and each object item.
[0007] According to an exemplary embodiment of the present
disclosure, there is provided an apparatus for providing
information, the apparatus including: a control unit configured to
extract a representative attribute keyword candidate set including
representative attribute keywords, to set a reserved word set
including reserved words, store the degrees of object-keyword
association corresponding to object item-representative attribute
keyword pairs, and to store the degrees of basic reserved
word-keyword association corresponding to reserved
word-representative attribute keyword pairs by using association
weights corresponding to representative attribute
keyword-subordinate keyword pairs and the degrees of basic reserved
word-subordinate keyword association corresponding to reserved
word-subordinate keyword pairs; a storage unit configured to store
the degrees of object-keyword association and the degrees of basic
reserved word-keyword association; and a communication unit
configured to acquire a received reserved word. The control unit
acquires the degree of reserved word-object association
corresponding to a pair of the received reserved word and each
object item by using the degrees of object-keyword association and
the degrees of basic reserved word-keyword association. The control
unit provides an object item based on the degree of reserved
word-object association corresponding to the pair of the received
reserved word and each object item.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] A more complete appreciation of the present invention will
become more apparent by describing in detail exemplary embodiments
thereof with reference to the accompanying drawings, wherein:
[0009] FIG. 1 is a view showing the network configuration of a
system for providing information by using an attribute language
according to an exemplary embodiment of the present disclosure;
[0010] FIG. 2 is a block diagram of a terminal according to an
exemplary embodiment of the present disclosure;
[0011] FIG. 3 is a block diagram of an information provision
apparatus according to an exemplary embodiment of the present
disclosure;
[0012] FIG. 4 is a flowchart of a process of providing information
via an information provision interface according to an exemplary
embodiment of the present disclosure;
[0013] FIG. 5 is a detailed flowchart of step 910 according to an
exemplary embodiment of the present disclosure;
[0014] FIG. 6 is a detailed flowchart of step 510 according to an
exemplary embodiment of the present disclosure;
[0015] FIG. 7 is a detailed flowchart of step 530 according to an
exemplary embodiment of the present disclosure;
[0016] FIG. 8 is a flowchart of a process of providing information
according to another exemplary embodiment of the present
disclosure;
[0017] FIG. 9 is a flowchart of a process of providing information
according to another exemplary embodiment of the present
disclosure;
[0018] FIG. 10 shows an example of the stored degrees of
object-keyword association according to an exemplary embodiment of
the present disclosure;
[0019] FIG. 11 shows an example of the degrees of basic reserved
word-keyword association according to an exemplary embodiment of
the present disclosure;
[0020] FIG. 12 is a detailed flowchart of step 940 according to an
exemplary embodiment of the present disclosure;
[0021] FIG. 13 is a flowchart of a process of providing information
according another exemplary embodiment of the present
disclosure;
[0022] FIG. 14 is a flowchart of a process of providing information
according to still another exemplary embodiment of the present
disclosure;
[0023] FIG. 15 is a detailed flowchart of step 1340 according to a
first exemplary embodiment of the present disclosure;
[0024] FIG. 16 is a detailed flowchart of step 1340 according to
another exemplary embodiment of the present disclosure;
[0025] FIG. 17 is a detailed flowchart of step 1340 according to
still another exemplary embodiment of the present disclosure;
[0026] FIG. 18 is a detailed flowchart of step 1320 according to a
modified exemplary embodiment of the present disclosure;
[0027] FIG. 19 is an example of an interface generated based on the
interface information provided at step 1840; and
[0028] FIG. 20 is a view of a terminology hierarchy according to an
exemplary embodiment of the present disclosure.
DETAILED DESCRIPTION
[0029] Exemplary embodiments of the present disclosure will be
described in detail below with reference to the accompanying
drawings.
[0030] In the descriptions of the embodiments, descriptions of
techniques which are well known in the art to which this disclosure
belongs and which are not directly related to this disclosure will
be omitted. The reason for this is to more clearly convey the gist
of the present disclosure without making the gist of the present
disclosure obscure by omitting unnecessary descriptions.
[0031] For the same reason, in the accompanying drawings, some
components are exaggerated, omitted, or schematically shown. Also,
the size of each component does not completely reflect the actual
size thereof. Throughout the drawings, the same or corresponding
components are denoted by the same reference symbols.
[0032] The exemplary embodiments of the present disclosure are
described in detail below with reference to the accompanying
drawings.
[0033] FIG. 1 is a view showing the network configuration of a
system for providing information by using an attribute language
according to an exemplary embodiment of the present disclosure.
[0034] Referring to FIG. 1, the information provision system
according to the present exemplary embodiment may include a
terminal 200, an information provision apparatus 300, and a
communication network 150.
[0035] Terminal 200 may be implemented as, e.g., a smartphone, a
PDA, a tablet PC, a notebook computer, a laptop computer, a
personal computer, another electronic device capable of performing
communication, receiving input from a user, and outputting screens,
or a similar device.
[0036] The information provision apparatus 300 may be implemented
as, e.g., a workstation, a server, a general-purpose computer,
another electronic device capable of performing communication, or a
similar device.
[0037] The terminal 200 and the information provision apparatus 300
are connected to and communicate with each other over the
communication network 150.
[0038] The communication network 150 may be implemented using at
least part of Long Term Evolution (LTE), LTE-Advanced (LTE-A),
WI-FI, Local Area Network (LAN), Wide Area Network (WAN), Code
Division Multiple Access (CDMA), Time Division Multiple Access
(TDMA), Wireless Broadband (WiBro), and Global System for Mobile
Communications (GSM), and other communication methods developed in
the past, being currently developed, and to be developed in the
future. In the following, for the sake of convenience, the terminal
200 and the information provision apparatus 300 will be described
as directly communicating with each other without references to the
communication network 150.
[0039] The detailed operations and configurations of the terminal
200 and the information provision apparatus 300 will be described
with reference to FIGS. 2 to 8.
[0040] FIG. 2 is a block diagram of a terminal 200 according to an
exemplary embodiment of the present disclosure.
[0041] Referring to FIG. 2, the terminal 200 according to the
present exemplary embodiment may include an input unit 210, a
display unit 220, a communication unit 230, a storage unit 240, and
a control unit 250.
[0042] The input unit 210 converts an input operation of a user
into an input signal, and transmits the input signal to the control
unit 250. The input unit 210 may be implemented as, e.g., a
keyboard, a mouse, a touch sensor on a touch screen, a touchpad, a
keypad, a voice input device, or another input processing device
developed in the past, being currently developed, or to be
developed in the future. For example, the input unit 210 may
receive information provision request input from a user, and may
transfer the information provision request input to the control
unit 250.
[0043] The display unit 220 outputs a screen under the control of
the control unit 250. The display unit 220 may be implemented as,
e.g., a liquid crystal display (LCD) device, a light-emitting diode
(LED) device, an organic LED (OLED) device, a projector, or another
display device developed in the past, being currently developed, or
to be developed in the future. For example, the display unit 220
may display an interface page or information provision result page
for the provision of information. In an exemplary embodiment, a
component using another method capable of transferring information
to a user, such as voice output or vibration, rather than screen
output, may be used in place of the display unit 220.
[0044] The communication unit 230 exchanges data with the
information provision apparatus 300 and/or other external devices.
The communication unit 230 transfers data, received from the
information provision apparatus 300, to the control unit 250.
Furthermore, the communication unit 230 transfers data to the
information provision apparatus 300 under the control of the
control unit 250. The communication technology used by the
communication unit 230 may vary depending on the type of
communication network 150 or other circumstances.
[0045] The storage unit 240 stores data under the control of the
control unit 250, and transfers requested data to the control unit
250.
[0046] The control unit 250 controls the overall operation of the
terminal 200 and individual components. In particular, the control
unit 250 transmits an information provision request or another type
of data to the information provision apparatus 300 according to
information input from the input unit 210, and displays a result
page and/or an interface page via the display unit 220 according to
page information received from the information provision apparatus
300, as will be described later.
[0047] The operation performed by the control unit 250 may be
distributed and processed by a plurality of arithmetic and logic
units which are physically distributed. There is possible a method
in which part of the operation performed by the control unit 250 is
performed by a first server and the remaining operation is
performed by a second server. In this case, the control unit 250
may be implemented as the sum of the arithmetic and logic units
which are physically distributed.
[0048] The storage unit 240 may be implemented as the sum of
storage devices which are physically separated from each other.
[0049] When the control unit 250 or storage unit 240 is implemented
as the sum of a plurality of devices which are physically separated
from each other, communication is required between the plurality of
devices. In this case, for the sake of simplicity of description,
the following description will be given on the assumption that the
storage unit 240 or control unit 250 is implemented as a single
object.
[0050] In the case where the terminal 200 transmits or receives
data, the communication unit 230 may be described as transmitting
or receiving data under the control of the control unit 250, or the
control unit 250 may be described as transmitting or receiving data
by controlling the communication unit 230, depending on the point
of view of a corresponding situation.
[0051] The detailed operations of the individual components of the
terminal 200 will be described with reference to FIGS. 4 to 8.
[0052] FIG. 3 is a block diagram of an information provision
apparatus 300 according to an exemplary embodiment of the present
disclosure.
[0053] Referring to FIG. 3, the information provision apparatus 300
according to the present exemplary embodiment may include a
communication unit 310, a control unit 320, and a storage unit
330.
[0054] The communication unit 310 exchanges data with the terminal
200 and/or other external devices. The communication unit 310
transfers data, received from the terminal 200, to the control unit
320. Furthermore, the communication unit 310 transfers data to the
terminal 200 under the control of the control unit 320. The
communication technology used by the communication unit 310 may
vary depending on the type of communication network 150 or other
circumstances.
[0055] The storage unit 330 stores data under the control of the
control unit 320, and transfers data, requested by the control unit
320, to the control unit 320.
[0056] The control unit 320 controls the overall operation of the
information provision apparatus 300 and individual components. In
particular, when the control unit 320 receives an interface page
request, an information provision result page request, or another
type of data via the communication unit 310, the control unit 320
retrieves required data from storage unit 330, generates load page
information, and transfers page information to the terminal 200 via
the communication unit 310, as will be described later.
[0057] In the case where the information provision apparatus 300
transmits or receives data, the communication unit 310 may be
described as transmitting or receiving data under the control of
the control unit 320, or the control unit 320 may be described as
transmitting or receiving data by controlling the communication
unit 310, depending on the point of view of a corresponding
situation.
[0058] The detailed operations of the individual components of the
information provision apparatus 300 will be described with
reference to FIGS. 4 to 8.
[0059] According to another exemplary embodiment, data adapted to
provide information by using a voice form or another method may be
transmitted and received in place of a page adapted to visually
provide information.
[0060] FIG. 4 is a flowchart of a process of providing information
via an information provision interface according to an exemplary
embodiment of the present disclosure.
[0061] At step 410, the control unit 320 of the information
provision apparatus 300 generates interface page information. The
interface page is information required to generate an information
interface page. The interface page is a page adapted to prompt the
input of a user, to receive the input of the user, and to transfer
the input of the user to the information provision apparatus 300.
For example, the interface page information may be in the form of
an HTML document or another markup language document. In another
exemplary embodiment, the terminal 200 may have the form
information of the interface page in advance, and only an item
corresponding to content may be transferred from the information
provision apparatus 300 to the terminal 200. In the following, for
the sake of convenience, the following description will be given on
the assumption that the interface page information or another type
of page information is transferred in the form of an HTML document.
However, the scope of the present disclosure is not limited
thereto.
[0062] At step 420, the communication unit 310 of the information
provision apparatus 300 transfers the interface page information to
the terminal 200.
[0063] At step 430, the control unit 250 of the terminal 200
constructs an interface page by using the interface page
information. For example, the control unit 250 may run a web
browser, may interpret an HTML document, and may construct an
interface page in the form of a web page. A separate application
may be used in place of the web browser.
[0064] At step 440, the display unit 220 of the terminal 200
displays the interface page to a user 400. The interface page may
include an interface in which, e.g., the user 400 may request the
provision of information, may input and/or select a keyword for the
provision of the information, and may make other settings for the
provision of the information.
[0065] At step 450, the input unit 210 of the terminal 200 receives
the selection input of the user 400 via the input interface page,
and transfers the selection input to the control unit 250.
[0066] At step 460, the communication unit 230 of the terminal 200
transfers input information adapted to identify the selection input
of the user 400 to the information provision apparatus 300 under
the control of the control unit 250.
[0067] At step 470, the control unit 320 of the information
provision apparatus 300 generates result page information by using
the input (e.g., a keyword and/or another information provision
setting) of the user 400. A preparation process of generating the
result page information and a process of generating the result page
information will be described with reference to FIGS. 5 to 8 later.
The result page information may be constructed, e.g., in the form
of an HTML document and/or in the form of an image.
[0068] At step 480, the communication unit 310 of the information
provision apparatus 300 transfers the result page information to
the terminal 200.
[0069] At step 490, the control unit 250 of the terminal 200
constructs a result page by using the result page information
received by the communication unit 230. For example, the control
unit 250 may construct a result page by interpreting the result
page information in an HTML form.
[0070] At step 495, the display unit 220 of the terminal 200
provides the result page to the user 400.
[0071] Although it is assumed that a page in a visual form is
provided to the user 400 in the exemplary embodiment of FIG. 4, the
interface or result information may be provided by voice. In this
case, a voice output unit may be used in place of the display unit
220. Another type of interface method available currently or in the
future may be used in conjunction with the user 400 in place of the
visual/aural method. In this case, the information provision
apparatus 300 may provide information, obtained through conversion
using another method, to the terminal 200 in place of the page
information in accordance with the interface method.
[0072] In exemplary embodiments shown in the drawings starting from
FIG. 5, the user 400 desires to receive information about an object
in a specific field of interest in which he or she is interested
in. However, the scope of the present disclosure is not limited
thereto.
[0073] A field of interest may be, e.g., the type of objects. For
example, when the field of interest is "Celebrity," objects
corresponding to this field of interest may include "Si-min Yu,"
"Jae-seok Yu," "Taylor Swift," etc. For example, when the field of
interest is "Movie," objects corresponding to this field of
interest may include "Dunkirk," "Spider-Man: Homecoming,"
"Despicable Me 3," etc. For example, when the field of interest is
"Broadcast program," objects corresponding to this field of
interest may include "Muhandogeon (Infinite Challenge)," "American
Idol," "Game of Thrones," etc.
[0074] In the following exemplary embodiments, documents are
collected in order to evaluate the relationship (the degree of
association, weight, and/or the like) between keywords. The
collected documents may be evaluated as having the same value, or a
newer document may be evaluated as having a higher value. In other
words, the degrees of association between the age of a document
based on an evaluation date and keywords appearing in the document
may have a negative correlation.
[0075] In the process starting from FIG. 5, the value may vary
depending on the up-to-dateness of a document. For example, the
degree of association of a case where two keywords appear in a
document which is one day old at evaluation time may be evaluated
as being ten times higher than that of a case where two keywords
appear in a document which is ten days old at the evaluation time.
The age of a document may be evaluated, e.g., on a
second/minute/hour basis or on a day/month/year basis. Although the
control unit 320 is based on a document evaluated before the age of
the document is reflected therein, the control unit 320 may extract
the degree of association between two keywords by extracting the
partial degree of association reflecting the age of the document
through the division of the value of the partial degree of
association by the age of the document and then accumulating the
partial degrees of association.
[0076] The time at which a document was generated, which is used to
determine the age of the document, may be determined using, e.g., a
posting time included inside the document and/or metadata.
Alternatively, when a document which had not been found during
previous crawling is newly found through periodic crawling, it is
determined that a new document is added at new crawling time.
[0077] FIG. 9 is a flowchart of a process of providing information
according to an exemplary embodiment of the present disclosure.
[0078] At step 910, the control unit 320 stores the degree of
object-keyword association corresponding to each object
item-representative attribute keyword pair in the storage unit
330.
[0079] FIG. 10 shows an example of the stored degrees of
object-keyword association according to an exemplary embodiment of
the present disclosure.
[0080] In the exemplary embodiment of FIG. 10, object items are all
in m (i.sub.1 to i.sub.m) in number, and representative attribute
keywords are all n (k.sub.1 to k.sub.n) in number.
[0081] For example, the degree of object-keyword association
between the object item is and the representative attribute keyword
k.sub.3 is w.sub.5,3.
[0082] The process of step 910 may be performed, e.g., according to
part of the exemplary embodiments of FIGS. 5 to 8, a similar
process, or an equivalent process. According to another exemplary
embodiment, the process of step 910 may be performed by the input
of an administrator, or by receiving the degree of object-keyword
association, determined by an external system, via a network or
storage medium.
[0083] FIG. 5 is a detailed flowchart of step 910 according to an
exemplary embodiment of the present disclosure.
[0084] Referring to FIG. 5, at step 510, the control unit 320
extracts a representative attribute keyword candidate set from
first set documents. For example, the control unit 320 may collect
keywords, frequently appearing in the documents of the first set
documents corresponding to a field of interest, as a representative
attribute keyword candidate set.
[0085] FIG. 6 is a detailed flowchart of step 510 according to an
exemplary embodiment of the present disclosure.
[0086] The control unit 320 may select keywords appearing in the
same documents as object keywords representative of object items
belonging to a specific field and keywords appearing in the same
documents as field keywords representative of a specific field as a
first attribute keyword candidate set and a second attribute
keyword candidate set.
[0087] For example, when a target field of interest for the
provision of information provision service is "Celebrity," field
keywords may include "celebrity," "entertainer," "movie star,"
"star," "celeb," etc. The field keywords may be set by an
administrator, and may be recommended and set by the control unit
320. The control unit 320 may acquire some field keywords, and may
then recommend and set similar keywords, whose degree of
association with each of the field keywords is analyzed as being
equal to or larger than a preset value, as additional field
keywords.
[0088] When a target field of interest for the provision of
information provision service is "Celebrity," object keywords may
be individual persons belonging to the corresponding field of
interest. For example "Jae-seok Yu," "Taylor Swift," "Stephen
Curry," etc. may be object keywords corresponding to the field of
interest "Celebrity."
[0089] The relationship between a field keyword and an object
keyword is now described. For example, a field keyword may
correspond to the attribute or type of corresponding object
keyword. A field keyword may be representative of a set, whereas an
object keyword may be representative of an element belonging to a
corresponding set.
[0090] Object keywords may be set by an administrator, and may be
selected using a method similar to the method of selecting field
keywords. According to still another exemplary embodiment, the
control unit 320 may select keywords, determined to be elements of
a set represented by a field keyword, as object keywords by
analyzing the contexts of collected documents.
[0091] A popular object keyword and an unpopular object keyword may
be distinguished from each other based on the quantities of the
found/collected corresponding object keywords. The control unit 320
may search for/collect documents containing each object keyword,
and may set an object keyword, for which the quantity of collected
documents is equal to or larger than a specific threshold value, as
a popular object keyword and set an object keyword, for which the
quantity of collected documents is smaller than a specific
threshold value, as an unpopular object keyword.
[0092] A popular field keyword and an unpopular field keyword may
be distinguished from each other based on the quantities of the
found/collected corresponding field keywords. The control unit 320
may search for/collect documents containing each field keyword, and
may set a field keyword, for which the quantity of collected
documents is equal to or larger than a specific threshold value, as
a popular field keyword and set a field keyword, for which the
quantity of collected documents is smaller than a specific
threshold value, as an unpopular field keyword. However, the
threshold value used to distinguish the popular object keyword and
the unpopular object keyword from each other and the threshold
value used to distinguish the popular field keyword and the
unpopular field keyword from each other may be different values. In
the following, for the sake of convenience, a popular object
keyword and a popular field keyword may be collectively called a
popular field/object keyword. Furthermore, for the sake of
convenience, an unpopular object keyword and an unpopular field
keyword may be collectively called an unpopular field/object
keyword.
[0093] In a modified exemplary embodiment, only a popular field
keyword or popular object keyword may be used in place of a popular
field/object keyword. In a modified exemplary embodiment, only an
unpopular field keyword or unpopular object keyword may be used in
place of an unpopular field/object keyword.
[0094] At step 610, the control unit 320 sets keywords, appearing
in the same documents as a popular field/object keyword, for a
first attribute keyword candidate set.
[0095] The control unit 320 may search for/collect documents
containing a popular field/object keyword, and may set keywords,
included in the collected documents, for a first attribute keyword
candidate set. According to another exemplary embodiment, the
control unit 320 may exclude field keyword and object keywords
among the keywords included in the collected documents from the
first attribute keyword candidate set. Furthermore, the control
unit 320 may exclude a preset insignificant keyword, e.g., a
postpositional particle/article, from the first attribute keyword
candidate set. Furthermore, according to another exemplary
embodiment, the control unit 320 may include a keyword, registered
in a preset dictionary, among the keywords included in the
collected documents in a first attribute keyword candidate set.
[0096] Furthermore, according to another exemplary embodiment, the
control unit 320 may search for/collect documents containing a
popular field/object keyword, and may include keywords, disposed
within a preset distance from a popular field/object keyword or a
sentence containing the keyword in the collected documents, in a
first attribute keyword candidate set. Furthermore, according to
another exemplary embodiment, the control unit 320 may search
for/collect documents containing a popular field/object keyword,
and may include keywords, used to describe and modify the popular
field/object keyword, in a first attribute keyword candidate set by
analyzing the contexts of the collected documents.
[0097] The distance between keywords or the distance between a
keyword and a sentence may be determined based on, e.g., any one or
more of the number of sentences located between the two keywords or
between the keyword and the sentence, the number of words located
between the two keywords or between the keyword and the sentence,
the number of phases located between the two keywords or between
the keyword and the sentence, and the number of letters located
between the two keywords or between the keyword and the
sentence.
[0098] The control unit 320 may first perform morpheme analysis in
order to perform keyword analysis.
[0099] At step 620, the control unit 320 sets keywords, appearing
in the same documents as an unpopular field/object keyword, for a
second attribute keyword candidate set.
[0100] The control unit 320 may search for/collect documents
containing an unpopular field/object keyword, and may set keywords,
included in the collected documents, for a second attribute keyword
candidate set. According to another exemplary embodiment, the
control unit 320 may exclude a field keyword and an object keyword
among keywords included in the collected documents from the second
attribute keyword candidate set. Furthermore, the control unit 320
may exclude a preset insignificant keyword, e.g., a postpositional
particle/article and/or the like, from the second attribute keyword
candidate set. Furthermore, according to another exemplary
embodiment, the control unit 320 may include a keyword, registered
in a preset dictionary, among the keywords included in the
collected documents in a second attribute keyword candidate
set.
[0101] Furthermore, according to another exemplary embodiment, the
control unit 320 may search for/collect documents containing an
unpopular field/object keyword, and may include keywords, disposed
within a preset distance from an unpopular field/object keyword or
a sentence containing the keyword in the collected documents, in a
second attribute keyword candidate set. Furthermore, according to
another exemplary embodiment, the control unit 320 may search
for/collect documents containing an unpopular field/object keyword,
and may include keywords, used to describe and modify the unpopular
field/object keyword, in a second attribute keyword candidate set
by analyzing the contexts of the collected documents.
[0102] The distance between keywords or the distance between a
keyword and a sentence may be determined based on, e.g., any one or
more of the number of sentences located between the two keywords or
between the keyword and the sentence, the number of words located
between the two keywords or between the keyword and the sentence,
the number of phases located between the two keywords or between
the keyword and the sentence, and the number of letters located
between the two keywords or between the keyword and the
sentence.
[0103] The control unit 320 may first perform morpheme analysis in
order to perform keyword analysis.
[0104] At step 630, the control unit 320 may set keywords belonging
to both the first attribute keyword candidate set and the second
attribute keyword candidate set for a representative attribute
keyword candidate set. In other words, keywords used to modify both
a popular field/object keyword and an unpopular field/object
keyword may be collected as the representative attribute keyword
candidate set.
[0105] According to another exemplary embodiment, at step 510, the
control unit 320 may include keywords each appearing along with an
object keyword and/or a field keyword in the representative
attribute keyword candidate set regardless of the
popularity/unpopularity thereof.
[0106] Referring back to FIG. 5, at step 520, the control unit 320
extracts two or more subordinate keywords, associated with each
representative attribute keyword included in the representative
attribute keyword candidate set, from the second set documents.
[0107] The second set documents used for the subordinate keyword
extraction of step 520 and the first set documents used for the
representative attribute keyword candidate set extraction of step
510 may be different document sets, or may be the same document
set. For example, the first set documents may be a set including
all collectable documents, and the second set documents may be a
set including only documents in which a specific target field of
interest for the provision of information provision service is used
as a main keyword. The control unit 320 may analyzes whether or not
each document is a document in which a specific target field of
interest for the provision of information provision service is used
as a main keyword based on frequently appearing keywords by
analyzing collectable documents. According to another exemplary
embodiment, the first set documents and the second set documents
may be all sets each including all collectable related documents.
Furthermore, according to another exemplary embodiment, the first
set documents may be a set including all collectable related
documents, and the second set documents may be a set including only
documents related to a specific target field of interest for the
provision of information provision service. Furthermore, according
to another exemplary embodiment, the second set documents may be a
set including all collectable related documents, and the first set
documents may be a set including only documents related to a
specific target field of interest for the provision of information
provision service.
[0108] For step 520, the control unit 320 may collect documents
including a keyword representative of a specific target field of
interest itself and/or documents each including an object keyword
belonging to the corresponding field of interest, e.g., in order to
generate a set including only documents related to the specific
field of interest for the provision of information provision
service, extracts documents in which the weight of a field
keyword/object keyword is equal to or larger than a preset value,
from among the collected documents, and may generate a set
including only documents related to the specific field of interest.
The weight of the field keyword/object keyword may be determined
based on the appearing frequency or appearing locations of the
field keyword/object keyword, context, or the like. For example, a
document in which the field keyword/object keyword appears
frequently, is used as the title of the corresponding document, or
is described in large letters or emphasizing fonts may be
classified as a document related to the specific field of
interest.
[0109] At step 520, the control unit 320 may extract a preset
number of subordinate keywords each having a high degree of
association with each representative attribute keyword by, e.g.,
analyzing at least part of the second set documents, thereby
extracting two or more subordinate keywords associated with each
representative attribute keyword.
[0110] The control unit 320 may determine the degree of association
between a representative attribute keyword and a subordinate
keyword, e.g., by taking into account the frequency at which the
subordinate keyword appears in the same or similar context as the
representative attribute keyword. For example, words appearing near
keyword A in a specific sentence may be viewed as also appearing
near a word associated with keyword A in another document.
[0111] "I went on a trip after making a hard decision, but it was
July and, thus, the weather was so hot that I suffered."
[0112] "I went on a trip after making a hard decision, but it was
July and, thus, the weather was so humid that I suffered."
[0113] Referring to the above two sentences, the word "hot" is
replaced with the word "humid" in the same context. The control
unit 320 may infer that "hot" and "humid" are associated words.
[0114] "I went on a trip after making a hard decision, but it was
July and, thus, the weather was so hot that I suffered."
[0115] "I went on vacation after making a hard decision, but it was
July and, thus, the weather was so hot that I suffered."
[0116] In the same manner, the control unit 320 may infer from the
above two sentences that "trip" and "vacation" are associated
words.
[0117] "I went on a trip after making a hard decision, but it was
July and, thus, the weather was so hot that I suffered."
[0118] "I went on a trip after making a hard decision, but it was
August and, thus, the weather was so hot that I suffered."
[0119] In the same manner, the control unit 320 may infer that
"July" and "August" are associated words.
[0120] The control unit 320 may stores information in which "hot"
and "humid" are associated words, "July" and "August" are
associated words, and "trip" and "vacation" are associated words
via previously collected documents. Thereafter, it is assumed that
the following sentences are collected.
[0121] "I went on vacation after making a hard decision, but it was
July and, thus, the weather was so hot that I suffered."
[0122] "I went on a trip after making a hard decision, but it was
August and, thus, the weather was so hot that I went through
hardship."
[0123] When the two sentences do not have the same context but it
is known that "hot" and "humid" are associated words, "July" and
"August" are associated words, and "trip" and "vacation" are
associated words, the control unit 320 may learn that "suffer" and
"hardship" are also associated words via the above sentences.
[0124] It may be determined that a keyword pair having a high
appearing frequency in the same/similar contexts has a high degree
of association. Furthermore, it is determined that the higher the
similarity between contexts in which two keywords appear is, the
higher the degree of association between the two keywords is. The
control unit 320 may increase the accuracy of the determination of
the degrees of association between keywords in such a manner as to
set the degrees of association keywords by performing learning by
using collected documents and then setting the degrees of
association between keywords appearing in a corresponding sentence
by using the set degrees of association between keywords and the
context of the sentence.
[0125] As similar learning methods, Neural Net Language Model
(NNLM), Recurrent Neural Net Language Model (RNNLM), word2vec,
skipgram, and Continuous Bag-of-Words (CBOW) methods are known. In
particular, when the word2vec method is used, the word2vec method
can map individual keywords to vectors by performing learning by
using documents, and can determine the similarity between two
keywords through the cosine similarity calculation of two
vectors.
[0126] By means of such a method or a similar method, the control
unit 320 may extract a preset number of subordinate keywords having
the highest degree of association with each representative
attribute keyword by analyzing at least part of the second set
documents.
[0127] At step 530, the control unit 320 may extract an association
weight corresponding to a pair of each representative attribute
keyword within the representative attribute keyword candidate set
and each subordinate keyword from the second set documents.
[0128] FIG. 7 is a detailed flowchart of step 530 according to an
exemplary embodiment of the present disclosure.
[0129] At step 710, the control unit 320 may extract the degrees of
association between the subordinate keywords by analyzing at least
part of the second set documents. For example, it is assumed that
subordinate keywords collected as subordinate keywords associated
with representative attribute keyword A1 are 50 subordinate
keywords B1.sub.1 to B1.sub.50. In this case, the control unit 320
may extract the degree of association between two subordinate
keywords by using the frequency at which the two subordinate
keywords appear in the same document, for these 50 subordinate
keywords. The degree of association between B1.sub.1 and B1.sub.2
is determined based on the frequency at which B1.sub.1 and B1.sub.2
appear in the same document. According to another exemplary
embodiment, the frequency at which B1.sub.1 and B1.sub.2 appear in
the same document influences the degree of association, and,
additionally, in the case where B1.sub.1 and B1.sub.2 appear in the
same document, as the distance between the two keywords B1.sub.1
and B1.sub.2 (or the distance between the sentences in which two
keyword appear) is closer, a higher degree of association may be
recognized. In a similar method, the degrees of association between
subordinate keywords may be extracted. The distance between
keywords or the distance between a keyword and a sentence may be
determined based on, e.g., any one or more of the number of
sentences located between the two keywords or between the keyword
and the sentence, the number of words located between the two
keywords or between the keyword and the sentence, the number of
phases located between the two keywords or between the keyword and
the sentence, and the number of letters located between the two
keywords or between the keyword and the sentence.
[0130] At step 720, the control unit 320 may extract association
weights between each representative attribute keyword and the
subordinate keywords based on the degrees of association between
the subordinate keywords. For example, for a subordinate keyword
set corresponding to each representative attribute keyword, the
control unit 320 may set a specific subordinate keyword within the
subordinate keyword set and the representative attribute keyword so
that the degree of association between the specific subordinate
keyword within the subordinate keyword set and another subordinate
keyword within the subordinate keyword set and an association
weight between the specific subordinate keyword and the
representative attribute keyword have a positive correlation
therebetween.
[0131] For example, the higher the degrees of association between
the subordinate keyword B1.sub.1 of the representative attribute
keyword A1 and other subordinate keywords B1.sub.2 to B1.sub.50 of
the representative attribute keyword A1 are, the higher value the
association weight between A1 and B1.sub.1 may be set to. For
example, the arithmetic mean (or sum) of the degrees of association
between B1.sub.1 and the other subordinate keywords B1.sub.2 to
B1.sub.50 of A1 may become the association weight between B1.sub.1
and A1. A geometric mean/harmonic mean may be used in place of a
simple arithmetic mean. There may be used a truncated mean designed
to calculate a mean with the two highest ones (examples) of the
degrees of association between B1.sub.1 and the other subordinate
keywords B1.sub.2 to B1.sub.50 of A1 and the two lowest ones
(examples) thereof excluded from the calculation. A median may be
used in place of the arithmetic mean of the degrees of
association.
[0132] According to some exemplary embodiments, "the frequency at
which B1.sub.1 and B1.sub.2 appear in the same document" used to
calculate the association weight of B1.sub.1 for A1 does not vary
simply depending on the number of documents in which B1.sub.1 and
B1.sub.2 appear together (in which B1.sub.1 and B1.sub.2 appear in
the same sentence, or in which B1.sub.1 and B1.sub.2 appear in
close proximity to each other), but may be obtained by dividing the
number of documents in which B1.sub.1 and B1.sub.2 appear together
(in which B1.sub.1 and B1.sub.2 appear in the same sentence, or in
which B1.sub.1 and B1.sub.2 appear in close proximity to each
other) by the number of documents in which B1.sub.1 appears and/or
the number of documents in which B1.sub.2 appears. In a similar
manner, "the frequency at which B1.sub.1 and B1.sub.2 appear in the
same document" may be set such that it has a positive correlation
in connection with the number of documents in which B1.sub.1 and
B1.sub.2 appear together (in which B1.sub.1 and B1.sub.2 appear in
the same sentence, or in which B1.sub.1 and B1.sub.2 appear in
close proximity to each other) and has a negative correlation in
connection with the number of documents in which B1.sub.1 appears
and/or the number of documents in which B1.sub.2 appears. This is a
kind of normalization intended to prevent a frequently used word
from simply having a high association weight in connection with the
representative attribute keyword A1.
[0133] Referring back to FIG. 5, at step 540, the control unit 320
may extract the degrees of subordinate association between an
object item and subordinate keywords from the first set
documents.
[0134] It may be determined that subordinate keywords frequently
appearing in the same document, the same sentence or a close
sentence as an object keyword (for example "Taylor Swift")
representative of an object item in the first set documents are
associated with the corresponding object item. The control unit 320
may collect documents in which the object keyword of the
corresponding object item appears, and may extract the degree of
subordinate association between each subordinate keyword and the
object keyword based on the frequency at which they appear together
within the documents. In particular, when a subordinate keyword
appears in the same sentence as the object keyword, the control
unit 320 may set the degree of association between the subordinate
keyword and the object item to a higher value than when the
subordinate keyword appears in a sentence different from that in
which the object keyword appears.
[0135] The control unit 320 may set the degree of association
between the subordinate keyword and the object item of the
corresponding object keyword to a higher value in proportion to the
proximity between a sentence in which the subordinate keyword
appears and a sentence in which the object keyword appears. The
proximity between two sentences may be determined based on, e.g.,
any one or more of the number of sentences located between the two
sentences, the number of words located between the two sentences,
the number of phases located between the two sentences, and the
number of letters located between the two sentences.
[0136] The control unit 320 may set the degree of association
between the subordinate keyword and the object item of the
corresponding object keyword to a higher value in proportion to the
proximity between a location at which the subordinate keyword
appears and a location at which the object keyword appears. The
proximity between the subordinate keyword and the object keyword
may be determined based on, e.g., any one or more of the number of
sentences located between the subordinate keyword and the object
keyword, the number of words located between the subordinate
keyword and the object keyword, the number of phases located
between the subordinate keyword and the object keyword, and the
number of letters located between the subordinate keyword and the
object keyword.
[0137] At step 550, the control unit 320 may extract the degree of
object-keyword association between the object item and the
representative attribute keyword by using the degrees of
subordinate association of step 540 and the association weights of
step 530.
[0138] For example, the degree of object-keyword association
between object item C and the representative attribute keyword A1
may be extracted using the degrees of subordinate association
between C and the subordinate keywords (e.g., B1.sub.1 to
B1.sub.50) of A1 and the association weights of the respectively
subordinate keywords. For example, the degree of object-keyword
association between the object item C and the representative
attribute keyword A1 may be set to a higher value in proportion to
the degrees of subordinate association between the object item C
and the subordinate keywords B1.sub.1 to B1.sub.50.
[0139] When the degree of subordinate association with the object
item C is higher for a subordinate keyword having a higher
association weight in the relationship with A1, the degree of
object-keyword association between the object C and the
representative attribute keyword A1 may be set to a higher value
for a subordinate keyword having a lower association weight than a
case having a higher degree of subordinate association. For
example, the degree of subordinate association of a keyword
B1.sub.1 having a higher association weight is higher in table 1
than in table 2, and thus the degree of object-keyword association
between the object C and the representative attribute keyword A1
may be set to a higher value in table 1 than in table 2.
TABLE-US-00001 TABLE 1 Association weight in Degree of subordinate
connection with A1 association with C B1.sub.1 0.5 0.5 B1.sub.2 0.2
0.2
TABLE-US-00002 TABLE 2 Association weight in Degree of subordinate
connection with A1 association with C B1.sub.1 0.2 0.5 B1.sub.2 0.5
0.2
[0140] According to an exemplary embodiment, the degree of
object-keyword association between the object C and the
representative attribute keyword A1 may be obtained based on (or
using) the sum of values obtained by multiplying association
weights and the degrees of subordinate association corresponding to
the individual subordinate keywords. In table 1,
0.5.times.0.5+0.2.times.0.2=0.29, and in table 2,
0.2.times.0.5+0.5.times.0.2=0.20. Accordingly, the degree of
object-keyword association between the object C and the
representative attribute keyword A1 may be set to a higher value in
table 1 than in table 2.
[0141] The above-described method of calculating the degree of
object-keyword association is merely an example. As long as the
degree of subordinate association in connection with C obtained at
step 540 and the association weight in connection with A1 obtained
at step 530 have a positive correlation with the degree of
object-keyword association between C and A1, another method may be
used.
[0142] Thereafter, when the communication unit 310 receives a
request for the provision of information associated with the
specific representative attribute keyword, the control unit 320 may
provide a result item via the communication unit 310 based on the
degree of object-keyword association extracted at step 550. For
example, when receiving a request for the provision of information
including any one representative attribute keyword, the control
unit 320 may provide information about object items in descending
order of the degree of object-keyword association in the
relationship with the corresponding representative attribute
keyword.
[0143] In another exemplary embodiment, when receiving a request
for the provision of information including two or more
representative attribute keywords and corresponding weights, the
control unit 320 may provide information about object items in
descending order of the sum (or mean) of values obtained by
multiplying the degrees of object-keyword association with the
representative attribute keywords included in the request for the
provision of information by weights (or adding weights to the
degrees of object-keyword association) for each object item.
[0144] FIG. 8 is a flowchart of a process of providing information
according to another exemplary embodiment of the present
disclosure.
[0145] The exemplary embodiment of FIG. 8 further includes two
steps 523 and 526 between steps 520 and 530 in addition to
processes identical to those of the exemplary embodiment of FIG. 5.
In this case, redundant descriptions will be omitted, and only
steps 523 and 526 will be described.
[0146] At step 523, the control unit 320 determines whether each of
the subordinate keywords extracted at step 520 corresponds to an
emotional word. For this purpose, the storage unit 330 or external
server may hold an emotional word dictionary. The emotional word
dictionary is a tool for determining whether or not a word
(keyword) is an emotional word, and may hold, e.g., an emotional
word list. It may be determined that a keyword included in the
emotional word list is an emotional word and a keyword not included
in the emotional word list is not an emotional word. However, these
determinations are based on dictionary meanings, and may not
reflect the use of words by the public, which varies over time.
Accordingly, the control unit 320 determines whether to use a
representative attribute keyword based on whether or not
subordinate keywords associated with the representative attribute
keyword are emotional words without determining whether or not the
representative attribute keyword itself is an emotional word.
[0147] In another exemplary embodiment, the control unit 320 may
add another word, having a high degree of association (equal to or
larger than a preset value) with a preset or larger number of words
registered in the emotional word dictionary as emotional words, to
the emotional word dictionary.
[0148] At step 526, the control unit 320 may leave a preset number
of representative attribute keywords in a representative attribute
keyword candidate set in descending order of the emotional word
percentage (or number) of associated subordinate keywords, and may
eliminate the remainder. Through this process, a keyword distant
from an emotional word may be prevented from being treated as an
emotional word.
[0149] Referring back to FIG. 9, at step 920, the control unit 320
stores the degree of basic reserved word-keyword association
corresponding to each reserved word-representative attribute
keyword pair in the storage unit 330.
[0150] Reserved words may include expressions which can be
presented by the weights of representative attribute keywords. For
example, "boring" may be a reserved word, and "pretty" may be a
reserved word.
[0151] Representative attribute keywords each having a high degree
of basic reserved word-keyword association in connection with the
reserved word "boring" may include representative attribute
keywords related to the resolution of a boring situation, such as
"interesting," "exciting," "time-killing," etc.
[0152] Representative attribute keywords having a high degree of
basic reserved word-keyword association in connection with the
reserved word "pretty" may include representative attribute
keywords similar to "pretty" and describing "pretty," such as
"beautiful," "cute," "attractive," etc.
[0153] For example, the process of step 920 may be performed by
input of an administrator, or by receiving the degree of basic
reserved word-keyword association, determined by an external
system, via a network or storage medium. According to another
exemplary embodiment, the process of step 920 may be performed by
analyzing collectable documents, such as Internet information, SNS
information, news, etc., and using a method similar to the
processes of FIGS. 5 to 8. Furthermore, the process of step 920 may
include a process of reflecting the feedback of a user, as will be
described later.
[0154] The process of step 920 may be performed using a method
which will be described later with reference to any one of FIGS. 15
to 17.
[0155] FIG. 11 shows an example of the degrees of basic reserved
word-keyword association according to an exemplary embodiment of
the present disclosure.
[0156] In the exemplary embodiment of FIG. 11, reserved words are
all q (C.sub.1 to C.sub.q) in number, and representative attribute
keywords are all n (k.sub.1 to k.sub.n) in number.
[0157] For example, the degree of basic reserved word-keyword
association between the reserved word C.sub.5 and the
representative attribute keyword k.sub.3 is v.sub.3,5.
[0158] At step 930, the communication unit 310 receives and
acquires a received reserved word from the terminal 200, and
transfers the received reserved word to the control unit 320.
[0159] A received reserved word is a reserved word received by the
terminal 200 from a search user. The terminal 200 may convert a
voice input into an electrical signal (a voice signal), and may
transfer the voice signal to the information provision apparatus
300. The control unit 320 of the information provision apparatus
300 may analyze the voice signal, may convert the voice signal into
a text, and may match the text to a reserved word. Furthermore, the
control unit 320 may analyze the intonation, pitch, tempo,
respiration state, etc. of a voice by analyzing the voice signal,
and may use analysis results as contextual information.
[0160] According to another exemplary embodiment, the terminal 200
may convert the voice input into a text, and may transfer the text
to the information provision apparatus 300. The terminal 200 may
analyze the intonation, pitch, tempo, respiration state of a voice,
etc., and may transfer analysis information to the information
provision apparatus 300. The information provision apparatus 300
may use the analysis information as a type of contextual
information.
[0161] At step 940, the control unit 320 may acquire the degree of
reserved word-object association corresponding to a pair of the
received reserved word and each object item by using the degree of
object-keyword association and the degree of basic reserved
word-keyword association.
[0162] FIG. 12 is a detailed flowchart of step 940 according to an
exemplary embodiment of the present disclosure.
[0163] Referring to FIG. 12, at step 1210, the control unit 320
acquires the adjusted degree of object-keyword association
corresponding to a pair of each object item and the representative
attribute keyword for the received reserved word.
[0164] According to an exemplary embodiment, for a pair, of each
object item and a representative attribute keyword, the control
unit 320 may acquire the adjusted degree of object-keyword
association corresponding to a pair of each object item and a
representative attribute keyword for the received reserved word by
applying the degree of basic reserved word-keyword association
corresponding to a pair of the received reserved word and the
representative attribute keyword to the degree of object-keyword
association corresponding to the pair of the object item and the
representative attribute keyword.
[0165] In particular, for a pair of each object item and a
representative attribute keyword, the control unit 320 may acquire
the adjusted degree of object-keyword association corresponding to
a pair of each object item and a representative attribute keyword
for the received reserved word by using a value obtained by
multiplying the degree of object-keyword association corresponding
to the pair of the object item and the representative attribute
keyword by the degree of basic reserved word-keyword association
corresponding to a pair of the received reserved word and the
representative attribute keyword.
[0166] Furthermore, for a pair of each object item and a
representative attribute keyword, the control unit 320 may set the
adjusted degree of object-keyword association corresponding to a
pair of each object item and a representative attribute keyword for
the received reserved word so that the adjusted degree of
object-keyword association has a positive correlation with the
degree of object-keyword association corresponding to the pair of
the object item and the representative attribute keyword and has a
positive correlation with the degree of basic reserved word-keyword
association corresponding to a pair of the received reserved word
and the representative attribute keyword.
[0167] In the present disclosure, it is assumed that the degree of
object-keyword association, the degree of basic reserved
word-keyword association, the adjusted degree of object-keyword
association, the degree of basic reserved word-subordinate keyword
association, and other values each representative of the degree of
association are values which are each representative of a closer
correlation in proportion to the size of the value. In another
exemplary embodiment, in the case where the value of a type of the
degree of association is representative of a closer correlation in
inverse proportion to the size of the value and the value of
another type of the degree of association value is representative
of a closer correlation in proportion to the size of the value, a
positive correlation and a negative correlation are appropriately
replaced with each other and then used in accordance with the
case.
[0168] For example, in order to acquire the adjusted degree of
object-keyword association corresponding to a pair of object item
i.sub.4 and representative attribute keyword k.sub.3 when the
received reserved word is C.sub.2, the control unit 320 may acquire
the adjusted degree of object-keyword association by applying the
degree of basic reserved word-keyword association v.sub.3,4
corresponding to a pair of the received reserved word C.sub.2 and
the representative attribute keyword k.sub.3 to the degree of
object association w.sub.4,3 corresponding to the pair of the
object item i.sub.4 and the representative attribute keyword
k.sub.3.
[0169] In particular, a method of applying the degree of
association may be a method of multiplying the degree of object
association and the degree of basic reserved word-keyword
association. For example, in order to acquire the adjusted degree
of object-keyword association corresponding to a pair of object
item i.sub.4 and representative attribute keyword k.sub.3 when the
received reserved word is C.sub.2, the control unit 320 may acquire
the adjusted degree of object-keyword association by using the
value (w.sub.4,3.times.v.sub.3,2) obtained by multiplying the
degree of object association w.sub.4,3 corresponding to the pair of
the object item i.sub.4 and the representative attribute keyword
k.sub.3 and the degree of basic reserved word-keyword association
v.sub.3,2 corresponding to a pair of the received reserved word
C.sub.2 and the representative attribute keyword k.sub.3. In
another exemplary embodiment, the control unit 320 may acquire the
adjusted degree of object-keyword association by using function
f(w.sub.4,3, v.sub.3,2) based on another calculation/utilization
method adapted to allow the adjusted degree of object-keyword
association to have a positive correlation with w.sub.4,3 and
v.sub.3,2 in place of the multiplication. Furthermore, both a
method of using (w.sub.4,3.times.v.sub.3,2) as the adjusted degree
of object-keyword association and a method of applying another
factor-based correction to (w.sub.4,3.times.v.sub.3,2) and then
using a resulting value as the adjusted degree of object-keyword
association may be used.
[0170] At step 1220, the control unit 320 may acquire the degree of
reserved word-object association by using a value obtained by
accumulating the adjusted degrees of object-keyword association for
a specific object item. For example, the control unit 320 may set
the degree of reserved word-object association corresponding to a
pair of the received reserved word and the specific object item so
that the degree of reserved word-object association has a positive
correlation with the cumulative value of the adjusted degrees of
object-keyword association for a specific object item. The degree
of reserved word-object association corresponding to a pair of
object item i.sub.4 and received reserved word C.sub.2 may be
acquired using, e.g., .SIGMA..sub.j=1.sup.nf(w.sub.4,j,v.sub.j,2).
f(w.sub.4,j,v.sub.j,2) is the adjusted degree of object-keyword
association corresponding to object item i.sub.4, received reserved
word C.sub.2, and keyword k.sub.j.
[0171] For example, the degree of reserved word-object association
corresponding to a pair of object item i.sub.4 and received
reserved word C.sub.2 may be
.SIGMA..sub.j=1.sup.n(w.sub.4,j.times.v.sub.j,2)=(w.sub.4,1.times.v.sub.1-
,2)+(w.sub.4,2.times.v.sub.2,2)+ . . .
+(w.sub.4,n.times.v.sub.n,2). In another example, the degree of
reserved word-object association corresponding to a pair of object
item i.sub.4 and received reserved word C.sub.2 may be a value
obtained by applying another factor-based correction to
.SIGMA..sub.j=1.sup.n(w.sub.4,j,v.sub.j,2).
[0172] Referring back to FIG. 9, at step 950, the control unit 320
may provide an object item according to the degree of reserved
word-object association corresponding to the received reserved
word. For example, when the degrees of reserved word-object
association corresponding to received reserved word C.sub.2 are as
shown in table 3, the control unit 320 may provide object items in
the order shown in table 4.
TABLE-US-00003 TABLE 3 Degree of reserved word-object Object item
association with received reserved word i.sub.1 0.23 i.sub.2 0.33
i.sub.3 0.99 i.sub.4 0.84
TABLE-US-00004 TABLE 4 Degree of reserved word- object association
with Order Object item received reserved word 1 i3 0.99 2 i4 0.84 3
i2 0.33 4 i1 0.23
[0173] In other words, the control unit 320 may provide object
items in descending order of the degree of reserved word-object
association corresponding to the received reserved word. The
terminal 200 having received the object items may provide
information about object item i.sub.3 to a user via the display
unit 220. The terminal 200 may provide information about another
object item at a lower order position when necessary. The terminal
200 may provide information about object item i3 to a user by voice
through a speaker in place of the display unit 220.
[0174] FIG. 13 is a flowchart of a process of providing information
according another exemplary embodiment of the present
disclosure.
[0175] The processes of FIGS. 13 to 17 may be performed by using
some of the processes of FIGS. 5 to 12 or modifying some of the
processes of FIGS. 5 to 12. When the processes of FIGS. 13 to 17
are described, descriptions of FIGS. 5 to 12 may be quoted when
necessary.
[0176] Referring to FIG. 13, at step 1310, the control unit 320
extracts a representative attribute keyword candidate set from
first set documents. For example, the control unit 320 may collect
keywords, frequently appearing in the documents of the first set
documents corresponding to a field of interest, as a representative
attribute keyword candidate set. The process of step 1310 may be
performed in a manner identical or similar to, e.g., that of the
process of step 510 of FIG. 5. The process of step 1310 may be
performed in a manner identical and similar to that of the process
of FIG. 6. The description of the process of FIG. 6 is not
repeated.
[0177] At step 1320, the control unit 320 sets a reserved word set.
For example, an administrator may set a reserved word set through
manual input. According to a modified exemplary embodiment, the
control unit 320 may set word phrases/passages, etc. suitable for
reserved words as reserved word candidates, and may provide an
interface configured to allow one or more of the reserved word
candidates as reserved words.
[0178] FIG. 18 is a detailed flowchart of step 1320 according to a
modified exemplary embodiment of the present disclosure.
[0179] At step 1810, the control unit 320 acquires the appearing
frequency of one linguistic unit or two or more consecutive
linguistic units within the document set. In this case, the
document set may be the same as or different from the document set
used in the process of step 510 of FIG. 5.
[0180] The linguistic unit may be, e.g., any one of a word phrase,
a word, a morpheme, a syllable, and a letter. Other units defined
to segment a sentence based on various criteria may be used as the
linguistic unit in the present exemplary embodiment.
[0181] Before step 1810, the control unit 320 may segment
documents, included in the individual documents of the document
set, into word phrase units and store the word phrase units in the
form of an array or list. In an exemplary embodiment, the control
unit 320 may delete insignificant words, e.g., some postpositional
particles and demonstrative adjective, such as "," "," etc., in
Korean, and other words not requiring analysis from each word
phrase, or may remove from the array or list. Furthermore, in an
exemplary embodiment, when a word phrase is composed of one word,
the control unit 320 may convert the corresponding word into a
basic or preset form.
[0182] According to a modified exemplary embodiment, before step
1810, the control unit 320 may segment documents, included in the
individual documents of the document set, into word units and store
the word units in the form of an array or list. In an exemplary
embodiment, the control unit 320 may convert each word into a basic
(or preset) form. In an exemplary embodiment, the control unit 320
may remove insignificant words, e.g., some postpositional particles
and demonstrative adjective, such as "," "," etc., in Korean, and
other words not requiring analysis from the array or list.
[0183] There may be possible a modified exemplary embodiment in
which the control unit 320 segments documents into morpheme units,
syllable units, or letter units.
[0184] In the following exemplary embodiment, for the sake of
convenience, it is assumed that the control unit 320 segments the
documents into word phrase units and the word phrases become
linguistic units.
[0185] A single linguistic unit may become a reserved word.
According to a modified exemplary embodiment, two or more
consecutive linguistic units also become a reserved word. For
example, "neat" (a single linguistic unit) may become a reserved
word, "nicely atmospheric" (two consecutive linguistic units) may
become a reserved word. However, two or more consecutive linguistic
units appear less frequently than a single linguistic unit.
Accordingly, in an exemplary embodiment, a weight or an additional
score may be given to two or more consecutive linguistic units upon
the selection of reserved words so that the two or more consecutive
linguistic units can be selected as a reserved word. According to a
modified exemplary embodiment, upon the selection of reserved
words, a reference value for the selection of a reserved word may
be set to a lenient value for two or more consecutive linguistic
unit. For example, in the case where a single linguistic unit may
be recommended as a reserved word candidate only when it appears at
least "a" times, settings may be made such that two consecutive
linguistic units may be recommended as a reserved word candidate
even when they appear "b" times considerably less than "a" times
and such that three consecutive linguistic units may be recommended
as a reserved word candidate even when they appear "c" times less
than "b" times. In the following, two or more consecutive
linguistic units are called consecutive linguistic units.
[0186] Furthermore, in the case where consecutive linguistic units
are recommended/selected as a reserved word, it is included in a
linguistic unit included in the reserved word candidate or in the
reserved word candidate itself, and consecutive linguistic units
shorter than the reserved word candidate may be prevented from
being recommended as reserved words, or a deduction in a score may
be made during the calculation of a score used to recommend the
units as a reserved word. The reason for this is to prevent a
plurality of similar reserved words from being selected or
recommended. In the following, for the sake of simplicity of
description, although descriptions of consecutive linguistic units
will be omitted, descriptions of a single linguistic unit may be
applied to consecutive linguistic units in an identical or similar
manner.
[0187] At step 1810, the appearing frequency of a linguistic unit
may be, e.g., the number of documents in which the corresponding
linguistic unit appears. Even when a corresponding linguistic unit
appears in a single document a plurality of times, only an
appearing frequency of 1 is recognized. In contrast, according to
another exemplary embodiment, in the case where a corresponding
linguistic unit appears in a single document a plurality of times,
the number of times may be all recognized as an appearing
frequency, and the appearing frequency may become the appearing
frequency of the linguistic unit.
[0188] According to still another exemplary embodiment, in the case
where a corresponding linguistic unit appears in a single document
two or more times, second and later appearances may be evaluated as
being lower than a first appearance. Furthermore, in the case where
the appearance of a corresponding linguistic unit is repeated in a
single document, later appearances may be evaluated as being lower.
Although a score increases as the number of appearances increases,
the gradient of scores gradually becomes gentle. For example, an
appearance frequency to the power of 1/r (where r is a real number
larger than 1) may be used as the appearance score of a
corresponding linguistic unit in a corresponding document. For
example, (the log value of an appearing frequency)+1 (where when
the appearing frequency is 0, a corresponding appearance score is
0) or the like may be used. Furthermore, the appearance score of a
linguistic unit in a single document may be limited to a value
equal to or smaller than a preset upper limit value. A value
obtained by accumulating the appearance scores of a corresponding
linguistic unit for all documents may become an appearance score
based on the appearing frequencies of the corresponding linguistic
unit. Furthermore, this appearance score may be used at step
1830.
[0189] In the following, for the sake of convenience, the following
description will be given on the assumption that the appearing
frequency of a linguistic unit is the number of documents in which
the corresponding linguistic unit appears.
[0190] At step 1820, the control unit 320 acquires the frequency at
which an emotional word is located within a preset distance from a
linguistic unit. The distance between the linguistic unit and the
emotional word may be determined based on, e.g., any one or more of
the number of words located between the linguistic unit and the
emotional word, the number of word phrases located between the
linguistic unit and the emotional word, and the number of letters
located between the linguistic unit and the emotional word.
[0191] Furthermore, when a linguistic unit and an emotional word
belong to different sentences, the control unit 320 may determine
that the emotional word is not located within the preset distance
from the linguistic unit regardless of the number of words, word
phrases and/or letters located between the linguistic unit and the
emotional word. According to another modified exemplary embodiment,
when a linguistic unit and an emotional word belong to different
sentences, the control unit 320 may calculate the distance
therebetween by adding a predetermined numerical value to a
calculated distance without taking into account the sentences. The
reason for this is that when a linguistic unit and an emotional
word belong to different sentences, probability that they have no
correlation is stronger, and thus it is preferable that the
distance therebetween is evaluated as being longer than the number
of words, word phrases and/or letters located between the
linguistic unit and the emotional word.
[0192] Whether a specific word (a word phrase) is an emotional word
may be determined by referring to the previously registered
emotional word dictionary.
[0193] The frequency at which an emotional word is located within a
predetermined distance from a linguistic unit may be, e.g., the
number of documents in which the corresponding linguistic unit and
the corresponding emotional word are located together within a
predetermined distance. Even when a corresponding linguistic unit
and a corresponding emotional word appear within the predetermined
distance a plurality of times in a single document, only an
appearing frequency of 1 is recognized. According to another
exemplary embodiment, when an emotional word is located within a
predetermined distance from a plurality of linguistic units, all
cases where the emotional word is located within the predetermined
distance from the individual linguistic units may be recognized as
a frequency. In the following, a linguistic unit located within the
predetermined distance from an emotional word is called an
emotional word location linguistic unit.
[0194] According to still another exemplary embodiment, in the case
where an emotional word location linguistic unit appears in a
single document two or more times, second and later appearances may
be evaluated as being lower than a first appearance. Furthermore,
in the case where the appearance of a corresponding emotional word
location linguistic unit is repeated in a single document, later
appearances may be evaluated as being lower. Although a score
increases as the number of appearances increases, the gradient of
scores gradually becomes gentle. For example, an appearance
frequency to the power of 1/r (where r is a real number larger than
1) may be used as the appearance score of a corresponding emotional
word location linguistic unit in a corresponding document. For
example, (the log value of an appearing frequency)+1 (where when
the appearing frequency is 0, a corresponding appearance score is
0) or the like may be used. Furthermore, the appearance score of an
emotional word location linguistic unit in a single document may be
limited to a value equal to or smaller than a preset upper limit
value. A value obtained by accumulating the appearance scores of a
corresponding emotional word location linguistic unit for all
documents may become an appearance score based on the appearing
frequencies of the corresponding emotional word location linguistic
unit. Furthermore, this appearance score may be used at step
1830.
[0195] Furthermore, according to another exemplary embodiment, a
higher appearance score may be recognized in proportion to the
number of emotional words located within a predetermined distance
from one linguistic unit. Furthermore, when an emotional word is
located within a shorter distance from one linguistic unit, a
higher appearance score may be recognized. Furthermore, only when
preset two or more emotional words are located within a
predetermined distance from one linguistic unit may an appearance
score (an appearing frequency) be recognized.
[0196] For the sake of convenience, the following description will
be given on the assumption that the appearing frequency of an
emotional word location linguistic unit is the number of documents
in which an emotional word appears within a preset distance from a
corresponding linguistic unit.
[0197] At step 1830, the control unit 320 selects a reserved word
candidate by taking into account the appearing frequency of a
corresponding linguistic unit and the frequency at which an
emotional word is located within a predetermined distance from the
linguistic unit.
[0198] For example, the control unit 320 obtains an emotional word
score by multiplying the appearing frequency of a corresponding
linguistic unit and the frequency at which an emotional word is
located within a predetermined distance from the linguistic unit
(or by using a calculation method which has a positive correlation
for the two variables). Furthermore, a preset number of reserved
word candidates may be set in descending order of the score.
Alternatively, linguistic units corresponding to a preset score or
more may be set as reserved word candidates.
TABLE-US-00005 TABLE 5 Appearing Emotional word Linguistic unit
frequency location frequency Score First linguistic unit 3003 1122
3369366 Second linguistic 2001 1820 3641820 unit Third linguistic
unit 3121 1300 4057300 Fourth linguistic 200 110 22000 unit
[0199] In the example of table 5, the linguistic units may become
reserved word candidates in order of the third linguistic
unit->the second linguistic unit->the first linguistic
unit->the fourth linguistic unit. When the control unit 320
recommends two reserved word candidates, the third linguistic unit
and the second linguistic unit will be recommended. When the
control unit 320 recommends linguistic units equal to or larger
than a perfect score of 300 as reserved word candidates, the third
linguistic unit, the second linguistic unit, and the first
linguistic unit will be recommended as reserved word candidates in
order thereof.
[0200] According to another exemplary embodiment, the control unit
320 may extract a first number of linguistic units in order of the
appearing frequencies of the linguistic units, and may then extract
a predetermined number of reserved word candidates in descending
order based on the frequency at which an emotional word appears
within a predetermined distance from the extracted linguistic units
(or an emotional word score). In the example of table 5, when three
linguistic units are extracted in descending order of their
appearing frequency, the first to third linguistic units may be
extracted. Based on the appearing frequencies of emotional words,
reserved word candidates may be recommended in order of the second
linguistic unit, the third linguistic unit, and the first
linguistic unit.
[0201] Furthermore, according to another exemplary embodiment, the
control unit 320 may extract a first number of linguistic units in
descending order of the appearing frequencies of the linguistic
units, and may then extract a first number of reserved word
candidate (where the second number is less than the first number)
in descending order based on the frequency at which an emotional
word appears within a predetermined distance from the extracted
linguistic units (or an emotional word score). In the example of
table 5, when three linguistic units are extracted in descending
order of their appearing frequency, the first to third linguistic
units may be extracted. When two linguistic units are extracted
based on an emotional word appearing frequency, reserved word
candidates may be recommended in order of the second linguistic
unit and the third linguistic unit.
[0202] Using another method similar or slightly different from the
above-described method, the control unit 320 may set a score for
the selection of reserved word candidates so that the score for the
selection of reserved word candidates has a positive correlation
with the appearing frequencies of linguistic units at step 1810 and
also has a positive correlation with the appearing frequencies of
linguistic units from which an emotional word is located within the
preset distance at step 1820, and may recommend reserved word
candidates by using the score.
[0203] Furthermore, the control unit 320 may perform processing
such that a linguistic unit already included in a reserved word set
can be prevented from being recommended as a reserved word
candidate. Furthermore, the control unit 320 may perform processing
such that a linguistic unit substantially identical to a reserved
word included in the reserved word set can be prevented from being
added to reserved word candidates.
[0204] At step 1840, the information provision apparatus 300
provides the terminal 200 with interface information adapted to
generate a reserved word selection interface including reserved
word candidate information. The interface information may be, e.g.,
a document in an html form. According to another exemplary
embodiment, the interface information may include only dynamic
information (recommended reserved word candidates, etc.) required
to generate the interface, and the terminal 200 may provide a page
including the interface to a user in such a manner as to
incorporate the dynamic information into a page form stored in the
terminal 200 in advance.
[0205] The control unit 320 may generate page information adapted
to generate the page including the reserved word selection
interface inclusive of the reserved word candidate information, and
the communication unit 310 may provide the page information to the
terminal 200. The terminal 200 may display the page including the
corresponding interface to a user through rendering. According to a
modified exemplary embodiment, an interface based on sound or an
interface based on technology known currently or to be known in the
future may be provided in place of the interface included in the
visual page. For the sake of convenience, the following description
will be given on the assumption that the interface included in the
visual page is provided.
[0206] FIG. 19 is an example of an interface 1900 generated based
on the interface information provided at step 1840.
[0207] Referring to FIG. 19, the interface 1900 includes a table,
including a check box column 1910, a reserved word candidate column
1920, and a detailed view column 1930. Furthermore, the interface
1900 may include a reserved word addition button 1940, a candidate
deletion button 1950, and an add-to-storage box button 1960. A user
may select at least one desired reserved word candidate on the
check box column 1910, and may process the reserved word candidate
by selecting any one of the reserved word addition button 1940, the
candidate deletion button 1950, and the add-to-storage box button
1960. Once the any one of the buttons has been selected, the
terminal 200 may transfer input information, obtained by converting
the input of the user, to the information provision apparatus
300.
[0208] The information provision apparatus 300 may process the
reserved word candidate according to the input information received
from the terminal 200. For example, when a user selects the check
boxes 1910 of some reserved word candidates (hereinafter referred
to as the "selected candidates") and also selects the reserved word
addition button 1940, the control unit 320 of the information
provision apparatus 300 having related input information may add
the selected candidates to a reserved word set and also delete the
selected candidates from a reserved word candidate set. The control
unit 320 performs control such that the language units included in
the reserved word set and linguistic units substantially identical
to the language units included in the reserved word set can be
prevented from being recommended as reserved word candidates upon
the future recommendation of reserved word candidates.
[0209] According to another example, when a user selects the check
boxes 1910 of some reserved word candidates and also selects the
reserved word candidate deletion button 1950, the control unit 320
of the information provision apparatus 300 having received related
input information may add the selected candidates to a reserved
word exclusion set and delete the selected candidates from the
reserved word candidate set. The control unit 320 performs control
such that the language units included in the reserved word
exclusion set and linguistic units substantially identical to the
language units included in the reserved word exclusion set can be
prevented from being recommended as reserved word candidates upon
the future recommendation of reserved word candidates.
[0210] Furthermore, according to another example, when a user
selects the check boxes 1910 of some reserved word candidates and
also selects the add-to-storage box button 1960, the control unit
320 of the information provision apparatus 300 having received
related input information may add the selected candidates to a
reserved word candidate storage set and delete the selected
candidates from the reserved word candidate set. The control unit
320 performs control such that the language units included in the
reserved word candidate storage set and linguistic units
substantially identical to the language units included in the
reserved word candidate storage set can be prevented from being
recommended as reserved word candidates upon the future
recommendation of reserved word candidates.
[0211] Another interface similar to the buttons or another
interface capable of replacing the functions of the buttons may be
used in place of the buttons 1940, 1950 and 1960.
[0212] Furthermore, the control unit 320 may provide an interface
adapted to delete part of reserved words from the reserved word
set. The control unit 320 may provide an interface adapted to
delete a linguistic unit from the reserved word exclusion set such
that part of the linguistic units in the reserved word exclusion
set are prevented from being excluded from recommendation. The
control unit 320 may provide the linguistic unit of the reserved
word candidate storage set in the form of a list interface similar
to that of FIG. 19, and may enable a user to add part of the
linguistic units of the reserved word candidate storage set as a
reserved word via the list interface. Furthermore, a user may
include part of the linguistic units of the reserved word candidate
storage set in the reserved word exclusion set and delete the part
of the linguistic units from the reserved word candidate storage
set via the list interface. In this case, the corresponding
linguistic unit is not provided via the list interface of the
reserved word candidate storage set any longer, and is not
recommended as a reserved word candidate via the interface 1900 of
FIG. 19 any longer. Furthermore, a user may simply delete part of
the linguistic units of the reserved word candidate storage set
from the reserved word candidate storage set via the list
interface. In this case, the corresponding linguistic unit is not
provided via the list interface of the reserved word candidate
storage set any longer, but may be recommended as a reserved word
candidate via the interface 1900 of FIG. 19.
[0213] Furthermore, the interface 1900 may include a previous page
button 1970 and/or a subsequent page button 1980 for switching
between pages in preparation for a case where reserved word
candidates cannot be all displayed within a single page. The
previous page button 1970 and/or the subsequent page button 1980
may be selectively provided depending on the number of actual
candidates and a current page location. Furthermore, there may be
provided an interface adapted to be extendable through scrolling in
place of the previous page button 1970 and/or the subsequent page
button 1980. In some interfaces, only a table including the items
1910, 1920 and 1930 may be scrolled, and the buttons 1940, 1950,
1960, 1970 and 1980 may be excluded from scrolling.
[0214] A user may refer to a background for the recommendation of a
reserved word candidate or related information in detail by
selecting the detailed view 1930. An interface which is provided by
the control unit 320 when the detailed view 1930 is selected may
include an interface adapted to add information about a
corresponding reserved word candidate and the corresponding
reserved word candidate as a reserved word, to the reserved word
candidate storage set, or to the reserved word exclusion set.
[0215] The control unit 320 may provide an interface configured to
manage reserved word candidates to a user via the terminal 200.
[0216] Referring back to FIG. 18, at step 1850, the control unit
320 may add a selected reserved word candidate to the reserved word
set in response to an input adapted to select the reserved
word.
[0217] Referring back to FIG. 13, at step 1330, the control unit
320 stores the degree of object-keyword association corresponding
to each object item-representative attribute keyword pair.
[0218] FIG. 10 shows an example of the stored degrees of
object-keyword association according to an exemplary embodiment of
the present disclosure.
[0219] In the exemplary embodiment of FIG. 10, object items are all
in m (i.sub.1 to i.sub.m) in number, and representative attribute
keywords are all n (k.sub.1 to k.sub.n) in number.
[0220] For example, the degree of object-keyword association
between the object item is and the representative attribute keyword
k.sub.3 is w.sub.5,3.
[0221] The process of step 1330 may be performed according to,
e.g., part of the exemplary embodiments of FIGS. 5 to 8, or a
process similar or corresponding to the part. According to another
exemplary embodiment, the process of step 1330 may be performed by
the input of an administrator or by receiving the degree of
object-keyword association, determined by an external system, via a
network or storage medium.
[0222] Since the exemplary embodiments of FIGS. 5 to 8 have been
described above, redundant descriptions will be omitted. However,
the process of step 510 of FIGS. 5 and 8 is substantially the same
as the process of step 1310 of FIG. 13. Accordingly, when the
process of step 1330 is performed, the process of step 510 may not
be performed again and the result of step 1310 may be reused even
when the exemplary embodiments of FIGS. 5 to 8 are used.
[0223] At step 1340, the control unit 320 stores the degree of
basic reserved word-keyword association corresponding to each pair
of a reserved word and a representative attribute keyword in the
storage unit 330 by using an association weight corresponding to a
pair of the representative attribute keyword and a subordinate
keyword and the degree of basic reserved word-subordinate keyword
association corresponding to a pair of the reserved word and the
subordinate keyword. The process of step 1340 may be performed
according to, e.g., the input of an administrator or the exemplary
embodiments of any one or more of FIGS. 15 to 17.
[0224] Before or during the process of step 1340, the subordinate
keyword needs to be determined, the association weight
corresponding to the pair of the representative attribute keyword
and the subordinate keyword needs to be determined, and the degree
of basic reserved word-subordinate keyword association
corresponding to the pair of the reserved word and the subordinate
keyword needs to be determined.
[0225] The subordinate keyword used in the process of step 1340 may
be determined through the performance of step 520 during the
process of step 1330. In this case, the subordinate keyword of step
520 may be used at step 1340. Unless the subordinate keyword is
determined at step 1330, the subordinate keyword may be determined
through step 520 of FIG. 5 and a process identical or similar to
its previous process.
[0226] The association weight used in the process of step 1340 may
be determined through the performance of step 530 during the
process of step 1330. In this case, the association weight of step
530 may be used at step 1340. Unless the association weight is
determined at step 1330, the association weight may be determined
step 530 of FIG. 5 and a process identical or similar to its
previous process.
[0227] The degree of basic reserved word-subordinate keyword
association may be calculated by, e.g., taking into account the
frequency at which the reserved word and the subordinate keyword
appear in the same context or similar contexts.
[0228] In the following, in the descriptions of FIGS. 15 to 17, an
example of obtaining the degree of basic reserved word-keyword
association v.sub.3,2 between reserved word C.sub.2 and
representative attribute keyword k.sub.3 is described. For example,
it is assumed that the subordinate keywords of the representative
attribute keyword k.sub.3 are B3.sub.1 to B3.sub.50. For the
reserved word, the representative attribute keyword, and the degree
of basic reserved word-keyword association, reference is made to
the example described above with reference to FIG. 11. The degree
of basic reserved word-subordinate keyword association
corresponding to a pair of reserved word C.sub.j and subordinate
keyword B.sub.gh is represented by x.sub.j,h. An association weight
corresponding to a pair of the subordinate keyword B.sub.gh and
representative attribute keyword k.sub.g is represented by
y.sub.g,h. The adjusted degree of reserved word-subordinate keyword
association corresponding to the combination of reserved word
C.sub.j, representative attribute keyword k.sub.g, and subordinate
keyword B.sub.gh is represented by x.sub.j,g,h.
[0229] FIG. 15 is a detailed flowchart of step 1340 according to a
first exemplary embodiment of the present disclosure.
[0230] Referring to FIG. 15, at step 1510, the control unit 320
acquires the adjusted degree of reserved word-subordinate keyword
association by applying the association weight to the degree of
basic reserved word-subordinate keyword association.
[0231] At step 1510, for each reserved word-subordinate keyword
pair, the control unit 320 may obtain the adjusted degree of
reserved word-subordinate keyword association corresponding to the
reserved word-subordinate keyword pair for the representative
attribute keyword by applying an association weight corresponding
to a subordinate keyword-representative attribute keyword pair to
the degree of basic reserved word-subordinate keyword association
corresponding to the reserved word-subordinate keyword pair.
[0232] For example, in order to acquire the adjusted degree of
reserved word-subordinate keyword association between C.sub.2 and
B3.sub.4 corresponding to a pair of subordinate keyword B3.sub.4
and representative attribute keyword k.sub.3 when a reserved word
is C.sub.2, the control unit 320 may obtain the adjusted degree of
reserved word-subordinate keyword association x.sub.2,3,4 by
applying association weight y.sub.3,4 corresponding to the pair of
the subordinate keyword B3.sub.4 and the representative attribute
keyword k.sub.3 to the degree of basic reserved word-subordinate
keyword association x.sub.2,4 corresponding to a pair of the
reserved word C.sub.2 and the subordinate keyword B3.sub.4.
[0233] In particular, a method of applying an association weight
may be a method of multiplying the degree of basic reserved
word-subordinate keyword association x.sub.2,4 by the association
weight y.sub.3,4 corresponding to the pair of the subordinate
keyword B3.sub.4 and the representative attribute keyword k.sub.3.
For example, in order to acquire the adjusted degree of reserved
word-subordinate keyword association x.sub.2,3,4 between C.sub.2
and B3.sub.4 corresponding to a pair of the subordinate keyword
B3.sub.4 and the representative attribute keyword k.sub.3 when the
reserved word is C.sub.2, the control unit 320 may acquire the
adjusted degree of reserved word-subordinate keyword association
x.sub.2,3,4 by using value x.sub.2,4.times.y.sub.3,4 obtained by
multiplying the pair of the reserved word C.sub.2 and the
subordinate keyword B3.sub.4 corresponding to the degree of basic
reserved word-subordinate keyword association x.sub.2,4 by
association weight y.sub.3,4 corresponding to the pair of the
subordinate keyword B3.sub.4 and the representative attribute
keyword k.sub.3. In another exemplary embodiment, the control unit
320 may acquire the adjusted degree of reserved word-subordinate
keyword association x.sub.2,3,4 by using function
f(x.sub.2,4,y.sub.3,4) based on another calculation/utilization
method adapted to allow the adjusted degree of reserved
word-subordinate keyword association x.sub.2,3,4 to have a positive
correlation with x.sub.2,4 and y.sub.3,4 in place of the
multiplication. Furthermore, both a method of using
(x.sub.2,4.times.v.sub.3,4) as the adjusted degree of reserved
word-subordinate keyword association x.sub.2,3,4 and a method of
applying another factor-based correction to
(x.sub.2,4.times.v.sub.3,4) and then using a resulting value as the
adjusted degree of reserved word-subordinate keyword association
x.sub.2,3,4 may be used.
[0234] At step 1520, the control unit 320 may set the degree of
basic reserved word-keyword association by using the cumulative
value of the adjusted degrees of reserved word-subordinate keyword
association x.sub.2,3,f between the reserved word C.sub.2 and the
representative attribute keyword k.sub.3. In other words, the
degree of basic reserved word-keyword association between the
reserved word C.sub.2 and the representative attribute keyword
k.sub.3 may be .SIGMA..sub.f=1.sup.50x.sub.2,3,f. In other words,
the degree of basic reserved word-keyword association between the
reserved word C.sub.2 and the representative attribute keyword
k.sub.3 may be obtained by obtaining the degrees of basic reserved
word-subordinate keyword association x.sub.2,f with reserved word
C.sub.2 for subordinate keywords B3.sub.1 to B3.sub.50, obtaining
x.sub.2,3,f by incorporating an association weight y.sub.3,f for
the corresponding subordinate keyword to each of the degrees of
basic reserved word-subordinate keyword association x.sub.2,f, and
then accumulating x.sub.2,3,f. According to another exemplary
embodiment, the degree of basic reserved word-keyword association
between the reserved word C.sub.2 and the representative attribute
keyword k.sub.3 may be a value obtained by applying another
factor-based correction to .SIGMA..sub.f=1.sup.50x.sub.2,3,f.
According to another exemplary embodiment, the degree of basic
reserved word-keyword association between the reserved word C.sub.2
and the representative attribute keyword k.sub.3 may be a value
having a positive correlation with
.SIGMA..sub.f=1.sup.50x.sub.2,3,f. In this case, it is assumed that
subordinate keywords connected to one representative attribute
keyword are 50 in number. However, when the number of subordinate
keywords connected to a representative attribute keyword varies,
the cumulative range off may become a different value, other than
50, in the formula.
[0235] FIG. 16 is a detailed flowchart of step 1340 according to
another exemplary embodiment of the present disclosure. Since the
processes of steps 1510 and 1520 of FIG. 16 are the same as the
processes described with reference to FIG. 15, redundant
descriptions will be omitted.
[0236] Referring to FIG. 16, at step 1530, the control unit 320 may
delete the degree of basic reserved word-keyword association for at
least one representative keyword, for which the degree of basic
reserved word-keyword association for a pair of the reserved word
and the corresponding representative keyword is equal or lower than
the reference degree of basic reserved word-keyword association,
among representative keywords corresponding to a specific reserved
word. Representative keywords corresponding to a specific reserved
word refer to keywords for which the degrees of basic reserved
word-keyword association have been set in the relationship with the
specific reserved word. The reference degree of basic reserved
word-keyword association may be set in advance. According to
another exemplary embodiment, the reference degree of basic
reserved word-keyword association may be set using the average
value of the degrees of basic reserved word-keyword association
corresponding to the specific reserved word, or may be set using
the degree of basic reserved word-keyword association at a specific
order position when the degrees of basic reserved word-keyword
association corresponding to the specific reserved word are
arranged in the order of their size. Another specific value having
a positive correlation with the degrees of basic reserved
word-keyword association corresponding to the specific reserved
word may become the reference degree of basic reserved word-keyword
association. The reference degree of basic reserved word-keyword
association may vary depending on a reserved word, and may be the
same for all reserved words. The deletion of the degree of basic
reserved word-keyword association means that there is no degree of
association between a reserved word and a representative keyword.
The control unit 320 may set the degree of association to 0.
Alternatively, the control unit 320 may delete the degree of basic
reserved word-keyword association in such a manner as to delete
information about the degree of basic reserved word-keyword
association of a pair of the reserved word and the corresponding
representative keyword in a list (which may be replaced with an
array, or another data structure) showing the degrees of basic
reserved word-keyword association. There may be used another method
of adding information indicating that the degree of basic reserved
word-keyword association has been deleted (or an associative
relationship has been deleted).
[0237] Through the process of step 1530, the degree of basic
reserved word-keyword association having a relatively slight degree
of association is deleted (i.e., a setting is made such that there
is no degree of association), and thus excessively complicated
calculation may be prevented from being performed or an associative
relationship substantially meaningless to a user/administrator may
be prevented from being displayed.
[0238] FIG. 17 is a detailed flowchart of step 1340 according to
still another exemplary embodiment of the present disclosure.
[0239] Since the processes of steps 1510 and 1520 of FIG. 17 are
the same as the processes described with reference to FIG. 15,
redundant descriptions will be omitted. Furthermore, since the
process of step 1530 of FIG. 17 is the same as the processes
described with reference to FIG. 16, a redundant description will
be omitted.
[0240] At step 1540, the control unit 320 may normalize the degrees
of basic reserved word-keyword association which have not been
deleted and remain. For example, in order to include the average
value of the degrees of basic reserved word-keyword association,
stored in connection with a specific representative attribute
keyword, in a specific range, the degrees of basic reserved
word-keyword association stored in connection with the specific
representative attribute keyword may be increased or decreased by
multiply the degrees of basic reserved word-keyword association by
a predetermined coefficient. For example, in order to include the
total sum of the degrees of basic reserved word-keyword association
stored in connection with a specific representative attribute
keyword in a specific range, the degrees of basic reserved
word-keyword association stored in connection with the specific
representative attribute keyword may be increased or decreased by
multiplying the degrees of basic reserved word-keyword association
by a predetermined coefficient. In other words, appropriate
adjustment may be performed to prevent a case where only a specific
representative attribute keyword is recommended/used or the
specific representative attribute keyword is rarely used even when
any reserved word is selected because the degree of basic reserved
word-keyword association having a high value is concentrated on the
specific representative attribute keyword.
[0241] According to another exemplary embodiment, at step 1540, the
control unit 320 may perform normalizing by adding a predetermined
coefficient to the degrees of basic reserved word-keyword
association or applying a combination with an arithmetic operation,
such as a log operation, an exponential operation, or the like in
place of multiplying them by the predetermined coefficient.
According to still another exemplary embodiment, the control unit
320 may perform normalizing in such a manner as to decrease only
the degrees of basic reserved word-keyword association equal to or
higher than a specific reference value or increase only the degrees
of basic reserved word-keyword association equal to or lower than a
specific reference value.
[0242] Furthermore, according to another exemplary embodiment, at
step 1540, the control unit 320 may perform normalization in order
to include the average value (or the total sum) of the degrees of
basic reserved word-keyword association stored in connection with a
specific reserved word in a specific range.
[0243] There may be possible a modified exemplary embodiment in
which step 1530 is omitted in the process of FIG. 17 and the
normalization of the degrees of basic reserved word-keyword
association is performed.
[0244] At step 1350, the communication unit 310 acquires a received
reserved word by receiving the received reserved word from the
terminal 200, and transfers the received reserved word to the
control unit 320.
[0245] The received reserved word is a reserved word which is
received by the terminal 200 from a search user. The terminal 200
may convert a voice input into an electrical signal (a voice
signal), and may transfer information to the provision device 300.
The control unit 320 of the information provision apparatus 300 may
convert the voice signal into a text by analyzing the voice signal,
and may match the resulting text to a reserved word. Furthermore,
the control unit 320 may analyze the intonation, pitch, tempo,
respiration state, etc. of a voice by analyzing the voice signal,
and may use analysis results as contextual information.
[0246] According to another exemplary embodiment, the terminal 200
may convert the voice input into a text, and may transfer the text
to the information provision apparatus 300. The terminal 200 may
analyze the intonation, pitch, tempo, respiration state of a voice,
etc., and may transfer analysis information to the information
provision apparatus 300. The information provision apparatus 300
may use the analysis information as a type of contextual
information.
[0247] At step 1360, the control unit 320 acquires the degree of
reserved word-object association corresponding to a pair of the
received reserved word and each object item by using the degree of
object-keyword association and the degree of basic reserved
word-keyword association. The process of step 1360 may be performed
according to the method of step 940 of FIG. 9 or the method of FIG.
12. The same descriptions will be omitted.
[0248] At step 1370, the control unit 320 may provide an object
item according to the degree of reserved word-object association
corresponding to the received reserved word. The process of step
1370 may be performed in the same manner as the process of step
950. The same descriptions will be omitted.
[0249] FIG. 14 is a flowchart of a process of providing information
according to still another exemplary embodiment of the present
disclosure.
[0250] Since steps 1310, 1320, 1330, 1340, 1350, 1360 and 1370 of
FIG. 14 are the same as steps 1310, 1320, 1330, 1340, 1350, 1360
and 1370 of FIG. 13, the same description will not be repeated.
[0251] Step 1333 added to FIG. 14 may be performed at any time
after a representative attribute keyword and a subordinate keyword
have been determined. For example, step 1333 may be performed at
the same time as/in parallel with step 1330, or may be performed
during step 1330.
[0252] At step 1333, the control unit 320 stores an association
weight between the representative attribute keyword and the
subordinate keyword. In the case where the process of setting an
association weight is not performed at step 1330, an association
weight between the representative attribute keyword and the
subordinate keyword may be set through a process identical or
similar to the process of step 530 of FIG. 5. According to another
exemplary embodiment, the control unit 320 may store an association
weight between the representative attribute keyword and the
subordinate keyword in such a manner as to retrieve the association
weight set at step 1330.
[0253] At step 1337, the control unit 320 acquires the degree of
basic reserved word-subordinate keyword association between the
subordinate keyword and the reserved word.
[0254] The control unit 320 may determine the degree of association
between the reserved word and the subordinate keyword, e.g., by
taking into account the frequency at which the subordinate keyword
appears in a context identical or similar to a context in which the
reserved word appears. For example, words appearing in the vicinity
of keyword A in a specific sentence may be viewed as appearing in
the vicinity of words associated with the keyword A in other
documents.
[0255] "I went on a trip after making a hard decision, but it was
July and, thus, the weather was so hot that I suffered."
[0256] "I went on a trip after making a hard decision, but it was
July and, thus, the weather was so humid that I suffered."
[0257] Referring to the above two sentences, the word "hot" is
replaced with the word "humid" in the same context. The control
unit 320 may infer that "hot" and "humid" are associated words.
[0258] "I went on a trip after making a hard decision, but it was
July and, thus, the weather was so hot that I suffered."
[0259] "I went on vacation after making a hard decision, but it was
July and, thus, the weather was so hot that I suffered."
[0260] In the same manner, the control unit 320 may infer from the
above two sentences that "trip" and "vacation" are associated
words.
[0261] "I went on a trip after making a hard decision, but it was
July and, thus, the weather was so hot that I suffered."
[0262] "I went on a trip after making a hard decision, but it was
August and, thus, the weather was so hot that I suffered."
[0263] In the same manner, the control unit 320 may infer that
"July" and "August" are associated words.
[0264] The control unit 320 may stores information in which "hot"
and "humid" are associated words, "July" and "August" are
associated words, and "trip" and "vacation" are associated words
via previously collected documents. Thereafter, it is assumed that
the following sentences are collected.
[0265] "I went on vacation after making a hard decision, but it was
July and, thus, the weather was so hot that I suffered."
[0266] "I went on a trip after making a hard decision, but it was
August and, thus, the weather was so hot that I went through
hardship."
[0267] When the two sentences do not have the same context but it
is known that "hot" and "humid" are associated words, "July" and
"August" are associated words, and "trip" and "vacation" are
associated words, the control unit 320 may learn via the above
sentences that "suffer" and "hardship" are also associated
words.
[0268] It may be determined that a keyword pair having a high
appearing frequency in the same/similar contexts has a high degree
of association. Furthermore, it is determined that the higher the
similarity between contexts in which two keywords appear is, the
higher the degree of association between the two keywords is. The
control unit 320 may increase the accuracy of the determination of
the degrees of association between keywords in such a manner as to
set the degrees of association keywords by performing learning by
using collected documents and then setting the degrees of
association between keywords appearing in a corresponding sentence
by using the set degrees of association between keywords and the
context of the sentence.
[0269] As similar learning methods, Neural Net Language Model
(NNLM), Recurrent Neural Net Language Model (RNNLM), word2vec,
skipgram, and Continuous Bag-of-Words (CBOW) methods are known. In
particular, when the word2vec method is used, the word2vec method
may map individual keywords to vectors by performing learning by
using documents, and may determine the similarity between two
keywords through the cosine similarity computation of two
vectors.
[0270] FIG. 20 is a view of a terminology hierarchy according to an
exemplary embodiment of the present disclosure.
[0271] Once the process of FIG. 13 or 14 has been completed, there
are set the hierarchical relationship between reserved words
C.sub.1 to C.sub.q, representative attribute keywords k.sub.1 to
k.sub.n, and subordinate keywords BX.sub.1 to BX.sub.50.
[0272] The degrees of basic reserved word-keyword association are
set between the reserved words and the representative attribute
keywords, and association weights are set between the
representative attribute keywords and the subordinate keywords.
Using this hierarchical relationship, the control unit 320 may
perform the operation of recommending an appropriate object
according to a reserved word, the operation of selecting a new
reserved word candidate, or the like. Furthermore, the hierarchical
relationship of FIG. 20 may be amended or improved by learning
through the repetition of the process of FIG. 13 or 14, with new
data being incorporated into the hierarchical relationship.
[0273] According to at least some exemplary embodiments of the
present disclosure, there are provided a method and apparatus for
efficiently providing information by using a reserved word.
[0274] In this case, it can be understood that individual blocks of
the flowcharts and/or combinations of the blocks of the flowcharts
may be performed by computer program instructions. Since it is
possible to install these computer program instructions on a
general-purpose computer, a special computer, or the processor of a
programmable data processing device, the instructions executed
through the computer or the processor of the programmable data
processing device generate a means for performing functions which
are described in the blocks of the flowcharts. Furthermore, since
it is possible to store these computer program instructions in
computer-usable or computer-readable memory that can be oriented to
a computer or some other programmable data processing device in
order to implement functions in a specific manner, it is possible
to manufacture products in which instructions stored in
computer-usable or computer-readable memory include means for
performing functions described in the blocks of flowcharts.
Moreover, since it is possible to install computer program
instructions on a computer or another programmable data processing
device, instructions for performing a series of operational steps
on the computer or the programmable data processing device,
generating processes executed by the computer and operating the
computer or the programmable data processing device can provide
steps for performing functions described in the blocks of
flowcharts.
[0275] Furthermore, each block may refer to part of a module, a
segment, or code including one or more executable instructions for
performing one or more specific logical functions. Moreover, it
should be noted that in some alternative embodiments, functions
described in blocks may occur out of order. For example, two
successive blocks may be actually performed at the same time, or
sometimes may be performed in reverse order according to relevant
functions.
[0276] In this case, the term "unit" used herein refers to a
software or hardware component, such as an FPGA or ASIC, which
performs a function. However, the term "unit" is not limited to a
software or hardware component. The unit may be configured to be
stored in an addressable storage medium, or may be configure to run
one or more processors. For example, the unit may include
components, such as software components, object-oriented software
components, class components and task components, processes,
functions, attributes, procedures, subroutines, segments of program
codes, drivers, firmware, microcode, circuits, data, databases,
data structures, tables, arrays, and variables. Functions provided
by components and units may be combined into a smaller number of
components and units, or may be divided into a larger number of
components and units. Furthermore, components and units may be each
implemented to run one or more CPUs within a device or security
multimedia card.
[0277] It will be understood by those having ordinary knowledge in
the art to which the present disclosure pertains that the present
disclosure may be practiced in other specific forms without
changing the technical spirit or essential feature of the present
disclosure. Therefore, the above-described embodiments should be
understood as being illustrative, not limitative, in all aspects.
The scope of the present disclosure is defined based on the
attached claims rather than the detailed description, and the
claims, equivalents to the claims, and all modifications and
alterations derived from the claims and the equivalents should be
construed as being included in the scope of the present
disclosure.
[0278] Meanwhile, although the exemplary embodiments of the present
disclosure have been disclosed in the present specification and the
accompanying drawings and the specific terms have been used, this
is intended merely to easily describe the technical spirit of the
present disclosure and help to understand the present disclosure,
but is not intended to limit the scope of the present disclosure.
It will be apparent to those having ordinary knowledge in the art
to which the present invention pertains that other modified
exemplary embodiments based on the technical spirit of the present
invention may be implemented in addition to the disclosed exemplary
embodiments.
* * * * *