U.S. patent application number 11/315053 was filed with the patent office on 2006-05-18 for linguistically aware link analysis method and system.
This patent application is currently assigned to ORACLE INTERNATIONAL CORPORATION. Invention is credited to Shamim A. Alpha.
Application Number | 20060106784 11/315053 |
Document ID | / |
Family ID | 25457091 |
Filed Date | 2006-05-18 |
United States Patent
Application |
20060106784 |
Kind Code |
A1 |
Alpha; Shamim A. |
May 18, 2006 |
Linguistically aware link analysis method and system
Abstract
Example, systems, methods, computer media, and other embodiments
for determining relevance rankings for pages identified in a search
query is provided. In one example, a computer program product can
be configured to identify a candidate set of pages in response to a
search query. A content-based relevance rank can be determined for
at least one page of the candidate set of pages based on a content
of the at least one page. The content-based relevance rank can be
adjusted for one or more selected pages from the candidate set of
pages by distributing a relevance rank from one or more pages that
point to the one or more selected pages.
Inventors: |
Alpha; Shamim A.; (Foster
City, CA) |
Correspondence
Address: |
MCDONALD HOPKINS CO., LPA
600 SUPERIOR AVE., E.
SUITE 2100
CLEVELAND
OH
44114
US
|
Assignee: |
ORACLE INTERNATIONAL
CORPORATION
Redwood Shores
CA
|
Family ID: |
25457091 |
Appl. No.: |
11/315053 |
Filed: |
December 22, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
09928962 |
Aug 13, 2001 |
7010527 |
|
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11315053 |
Dec 22, 2005 |
|
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Current U.S.
Class: |
1/1 ;
707/999.003; 707/E17.108 |
Current CPC
Class: |
Y10S 707/99945 20130101;
Y10S 707/99933 20130101; G06F 16/951 20190101; Y10S 707/99935
20130101; Y10S 707/99937 20130101; Y10S 707/959 20130101 |
Class at
Publication: |
707/003 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1. A computer program product for determining a relevance rank for
a plurality of pages identified by a search query, computer program
product, when executed, causes one or more computers to perform a
method, the method comprising: identifying a candidate set of pages
in response to the search query; determining a content-based
relevance rank for at least one page of the candidate set of pages
based on a content of the at least one page; and adjusting the
content-based relevance rank for one or more selected pages from
the candidate set of pages by distributing a relevance rank from
one or more pages that point to the one or more selected pages.
2. The computer program product of claim 1 where the relevance rank
from a page that points to the selected page is based on, at least
in part, a content-based relevance rank for the page.
3. The computer program product of claim 1 further including
obtaining a link structure of the candidate set of pages to
determine in-coming and out-going page links.
4. The computer program product of claim 1 where the adjusting
includes, for a first page, combining the content-based relevance
rank of the first page with the relevance ranks distributed from
the one or more pages that point to the first page.
5. The computer program product of claim 1 where the computer
program product is embodied in an information retrieval system.
6. A method of determining a relevance rank for a selected page in
response to a search query, the method comprising: identifying a
candidate set of pages in response to the search query; determining
a content-based relevance rank for at least one page of the
candidate set of pages based on a content of the at least one page;
and adjusting the content-based relevance rank for a selected page
from the at least one page of the candidate set of pages by using
at least part of a relevance rank from at least one of one or more
pages that point to the selected page.
7. The method of claim 6 where the relevance rank includes a
content-based relevance rank.
8. The method of claim 6 where the at least part of the relevance
rank from the at least one of one or more pages that point to the
selected page is distributed to out-going links.
9. The method of claim 6 where the distributing is based on a link
structure of the pages including link rank values from in-coming
links where the link rank values are determined from distributed
values of at least part of the content-based relevance from the one
or more pages that point to the selected page.
10. A system for determining a relevance ranking for pages obtained
from a network search query, the system comprising: link structure
logic for obtaining a link structure of the pages which identifies
out-going links from each of the pages which become in-coming links
to other pages; a content analyzer for determining a content of
each page; a content relevance ranking logic for determining a
content relevance rank for each page based on a content of the page
in relation to the network query; link analysis logic for
determining a link ranking for each of the out-going links for each
of the pages, where the link ranking of an out-going link from a
selected page being based, at least in part, on a content-based
relevance rank of the selected page; and a relevance rank adjuster
for determining and adjusting a relevance rank of a page by
combining the content relevance rank of the page with the link
rankings associated to in-coming links for the page.
11. A method of ranking a set of candidate pages in response to a
search query, the method comprising: identifying the candidate
pages from a network that potentially match the search query;
assigning a content-based relevance rank to a selected page from
the one or more candidate pages; adjusting the content-based
relevance rank of the selected page where the content-based
relevance rank for the selected page is influenced by a relevance
of one or more candidate pages that point to the selected page; and
ranking the candidate pages based on the adjusted content-based
relevance rank.
12. The method of claim 11 where the adjusting is influenced by the
relevance including at least part of a content-based relevance
rank.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of and claims the benefit
from U.S. patent application entitled "Linguistically Aware Link
Analysis Method and System", Ser. No. 09/928,962 filed Aug. 13,
2001, inventor Shamim Alpha, attorney docket number 27252.4
(OID-2000-152-01), which is also assigned to the present
assignee.
BACKGROUND
[0002] The Internet, including the World Wide Web (the "Web")
allows access to enormous amounts of information which grows in
number daily. This growth, combined with the highly decentralized
nature of the Web, creates a substantial difficulty in locating
selected information content. Prior art Web search services
generally perform an incremental scan of the Web to generate
various, often substantial indexes that can be later searched in
response to a user's query. The generated indexes are essentially
databases of document identification information. Search engines
uses these indexes to provide generalized content based searching
but a difficulty occurs in trying to evaluate the relative merit or
relevance of identified candidate documents. A search for specific
content in documents or web pages in response to a few key words
will almost always identify candidate documents whose individual
relevance is highly variable. Thus, a user's time can be
inefficiently spent on viewing numerous candidate documents that
are not relevant to what they are looking for.
[0003] Some prior search engines attempt to improve relevancy
scores of candidate documents by analyzing the frequency of
occurrence of the query terms on a per document basis. Other
weighing heuristics, such as the number of times that any of the
query terms occur within a document and/or their proximity to each
other, have also been used. These relevance ranking systems
typically presume that increasing occurrences of specific query
terms within a document means that the document is more likely
relevant and responsive to the query. However, this assumption is
not always accurate.
[0004] Another method to determine the relevancy of a document is
by using link analysis. Generally, link analysis assumes a that if
important web pages point to a document, then the document is also
probably important or relevant. However, typical link analysis
models a user's search for information on the Web as fluid moving
between different containers where the webpages are represented by
containers and links out of a webpage are represented by connecting
conduits with the same diameter. What this model assumes is that
users coming to a webpage must leave the webpage by following one
of the links from the webpage and users are equally likely to
follow any of the links from the webpage. If a page does not refer
to any webpage, it is assumed to refer to all the webpages. By
solving a steady state solution of the system, the model finds the
relative likelihood of finding the user on a webpage if a snapshot
of the system was taken. The basic problem with the model is that
people are not like fluids.
[0005] Rather, people can evaluate the relevance of a webpage for a
query. That has two implications on the behavior of the user in the
system: 1) users will be likely to stop searching based on the
relevance of a webpage, and 2) choosing between two links, users
will be more likely to follow a link to the more relevant page.
[0006] Based on these implications, there is a need for a relevance
ranking system where the probability of not leaving a webpage is a
function of the relevance of the webpage, and the probability of
following an outgoing link from a webpage is a function of the
relevance of all referred webpages and the relevance of the
webpage.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] In the accompanying drawings which are incorporated in and
constitute a part of the specification, embodiments of the
invention are illustrated, which, together with a general
description of the invention given above, and the detailed
description given below, serve to example the principles of this
invention.
[0008] FIG. 1 is one example of an overall system diagram of a
relevance ranking system;
[0009] FIG. 2 is one example diagram showing three example
candidate pages and their link structure including probabilities of
following each link and probabilities of being on a given page
based on its content relevance;
[0010] FIG. 3 is one exemplary methodology of determining the
relevance rank for candidate pages identified by a search query;
and
[0011] FIG. 4 is an exemplary methodology of generating a link
analysis rank.
DETAILED DESCRIPTION
[0012] One or more embodiments described herein relate to network
information retrieval and relevance ranking. In one example,
methods and systems can be configured to combine link analysis from
web pages and linguistic characteristics of the web pages to obtain
relevance rankings for search query results. Relevance rankings can
be improved to provide more relevant page information to a user in
response to a search query.
[0013] In one example, a computer-implemented process/product can
be configured to assume that the probability that a user will
follow a selected out-going link is not equal between all out-going
links from a given page. Rather, some are more likely to be
followed than others if the user believes the destination page is
relevant to their query. Even if the website does not provide any
clue (the text associated with link or url itself) to the visitors
about which links are more likely to be relevant, users are still
more likely to follow a link that points to a more relevant
webpage. If upon following a bad (with inferior content) link,
visitors will immediately bounce back to the referrer page and
follow another link. Users will be effectively spending more time
on a page with better content. That will likely mean that we will
find the user on a more relevant referred page even in the absence
of a visible clue on the referrer page. Thus, the relevance ranking
of one or more examples herein combines link analysis rankings with
content relevance rankings to obtain page rankings.
[0014] In some example embodiments as described herein, since they
combine link analysis rankings with content relevance rankings, the
relevance rank of a page will increase based on the number of
relevant pages that point to it. In other words, if many highly
relevant pages point to a selected page, then the selected page
must also be highly relevant.
[0015] The following includes definitions of exemplary terms used
throughout the disclosure. Both singular and plural forms of all
terms fall within each meaning:
[0016] "Page", as used herein, includes but is not limited to one
or more web pages, an electronic document, network addresses or
links, database addresses or records, or other objects that are
identifiable using a search query. "Page" and "document" are used
interchangeably.
[0017] "Software", as used herein, includes but is not limited to
one or more computer executable instructions, routines, algorithms,
modules or programs including separate applications or from
dynamically linked libraries for performing functions as described
herein. Software may also be implemented in various forms such as a
servlet, applet, stand-alone, plug-in or other type of application
as known to those skilled in the art.
[0018] "Logic", as used herein, includes but is not limited to
hardware, software and/or combinations of both to perform a
function.
[0019] "Network", as used herein, includes but is not limited to
the internet, intranets, Wide Area Networks (WANs), Local Area
Networks (LANs), and transducer links such as those using
Modulator-Demodulators (modems).
[0020] "Internet", as used herein, includes a wide area data
communications network, typically accessible by any user having
appropriate software. This includes the World Wide Web. "Intranet"
includes a data communications network similar to an internet but
typically having access restricted to a specific group of
individuals, organizations, or computers.
[0021] Illustrated in FIG. 1 is an exemplary overall system diagram
in accordance with the present invention. A computer system 100
executes software and processes information. The computer system
100 generally may take many forms, from a configuration including a
variety of processing units, networked together to function as a
integral entity, to a single computer, e.g., a personal computer,
operational in a stand-alone environment. The present invention can
be embodied in any of these computer system configurations. As
known in the art, computer systems may include a variety of
components and devices such as a processor, memory, data storage,
data communications buses, and a network communications device. The
computer system 100 is connected to a network 105, for example, the
Internet.
[0022] With further reference to FIG. 1, an information retrieval
system 110 receives and processes search queries from a user that
is trying to locate information on the network 105. The information
retrieval system 110 is for example a search engine which is a
remotely accessible software program that lets a user perform
searches including but not limited to_keyword/concept searches for
information on the network. The present invention and the
techniques described herein are not limited to text searching. One
skilled in the art will appreciate that the technique applies to
any information retrieval task. Additionally, the technique can be
applied in data mining tasks of determining populist views on
different topics because link analysis is serving as a popularity
contest. In that manner, the retrieval system 110 may include a
pre-generated database of indexes that identify web pages,
addresses, documents or other objects accessible through the
network 105 as is known in the art. In response to a search query,
the retrieval system 110 identifies a candidate set of pages that
match or possibly match the criteria of the search query.
[0023] With further reference to FIG. 1, before the candidate pages
are displayed to the user, the pages are processed by a relevance
ranking system 115 of the present invention. The relevance ranking
system 115 generates a relevance rank for each page such that the
most relevant pages are displayed first based on the relevance
rank. To briefly summarize, the relevance ranking of a web page is
based on combined functions of a content-based relevance ranking
for the web page and the link structure of the candidate web pages.
The system models the assumptions that a user will be likely to
stop searching based on the relevance of a web page and that
choosing between two links, a user will be more likely to follow a
link to a more relevant page. In that regard, the relevance ranking
system 115 determines the probability that a user will stay on a
web page and the probability that a user will follow an out-going
link from the web page as a function of the relevance of the web
page and the relevance of all referred web pages according to the
link structure. With these values, the system determines a
probability distribution between the candidate pages that reflects
a probability that a user will be on a page at any instance of
time.
[0024] The relevance ranking system 115 is embodied as software and
includes software components as described below. The relevance
ranking system may be a component within the information retrieval
system 110 or may be called and executed externally. Once a
candidate set of pages is retrieved, a link structure logic 120
determines the link structure of the pages including the out-going
links from each page which become in-coming links to another page.
This may be performed by using a spider or web crawler as is known
in the art and may be performed dynamically for each candidate set
of pages or may be obtained from predetermined link structure
information.
[0025] With reference to FIG. 2, an exemplary link structure is
shown for three web pages, namely, page A, page B and page C. For
exemplary purposes, we assume that pages A, B and C were retrieved
as a candidate set of pages from a search query. Determining the
link structure includes visiting each page and identifying links
contained therein that refer to other pages. These links are
referred to herein as out-going links. As shown in FIG. 2, page A
refers to page B and thus has an out-going link A-B. Similarly,
other out-going links include B-C, C-A and C-B.
[0026] With reference again to FIG. 1, and using the candidate
pages from FIG. 2, a content analyzer 125 analyzes the content
and/or subject matter of each page and determines its relevance to
the keywords from the user's search query. In its simplest form,
the content analyzer 125 includes logic that obtains the relevance
rank for each page that already has been assigned by the
information retrieval system 110 in its ordinary course of
retrieval. This may include processing the candidate pages using
for example, Oracle Text which is a software tool made by Oracle
Corporation that uses natural language processing technology to
identify themes and discourse in the text of a page. Pages may also
be analyzed for other types of media such as images, audio, video
and geographic location information to determine the content of a
page. In general, a content relevance rank can be anything that
represents the relevance of a page based on an assessment of its
content. For example, content relevance values can be between 0 and
100.
[0027] Once the content relevance values are obtained for each
page, a probability logic 130 determines a probability that a user
will stay on a given page as a function of the content relevance
values. For example, if the content relevance values are between 0
and 100, these values can be directly translated into a
corresponding percentage value to give the probability of staying
on a given page. For example, if the content relevance value for
page C is 30, then the probability of a user staying on page C is
set to 30% (0.3). Of course, many different transformations can be
used including non-linear relationships between the relevance
values for the page and linked pages and the probability of staying
on a page.
[0028] With reference again to FIG. 2, let's assume that the
content relevance value for pages A, B and C are 70, 50 and 30,
respectively. Using a linear relationship, the probability of a
user staying "Prob(staying)" on pages A, B and C are 70%, 50% and
30%, respectively. The probability logic 130 then computes the
probability of leaving each page A, B and C as 30%, 50% and 70%,
respectively. This is determined as 1-Prob(staying).
[0029] With further reference to FIG. 1, a link analysis logic 135
is invoked to determine probability values that a user will leave a
given page using a certain out-going link. In general, the
probability of a user following an out-going link is a function of
the link structure of other out-going links and the relevance value
of the page being linked to. Stated another way, the probability of
leaving "Prob(leaving)" a page is distributed to its out-going
links based on the relevance of the child page as compared to the
relevance of all child pages and relevance of the parent page. For
example, given that the relevance of page C is 0.3 (30% probability
of a user staying on page C) and the probability of leaving page C
is 0.7, then 0.7 is distributed among its out-going links. The
amount that each link receives is influenced by or otherwise
weighted by the relevance of its connecting page. For example,
out-going link C-A obtains a value of the probability of leaving
page C multiplied by the relevance value of page A normalized by
the relevance of all child pages linked from page C (e.g. child
pages A and B). In other words, the probability of a user following
link C-A is Prob(link C-A): .times. Prob .function. ( link .times.
.times. C .times. - .times. A ) = Prob .function. ( leaving .times.
.times. C ) Relevance .times. .times. A .times. Relevance .times.
.times. of .times. .times. Child .times. .times. Pages ( 1 )
.times. Prob .function. ( link .times. .times. C .times. - .times.
A ) = 0.7 0.7 0.7 + 0.5 .apprxeq. 0.4 ( 2 ) ##EQU1## Determining
the link rankings for the remaining links is as follows: Prob
.function. ( link .times. .times. C .times. - .times. B ) = 0.7 0.5
0.7 + 0.5 .apprxeq. 0.3 ( 3 ) Prob .function. ( link .times.
.times. A .times. - .times. B ) = 0.3 0.5 0.5 = 0.3 ( 4 ) Prob
.function. ( link .times. .times. B .times. - .times. C ) = 0.5 0.3
0.3 = 0.5 . ( 5 ) ##EQU2##
[0030] As shown in Equations (3)-(5), the probability of following
out-going link C-B equals 0.7*0.5/1.2 which is approximately 0.3.
Doing a similar analysis for the remaining out-going links, the
probability of following link A-B is approximately 0.3, and
following link B-C is 0.5. Thus, the probability of following an
outgoing link from a parent page is a function of the relevance of
all referred child pages and the relevance of the parent page. It
will be appreciated that there are many ways to distribute
probabilities based on probabilities of parent and child pages.
Other distributions can reflect the page relevance of a parent.
[0031] Once an initial determination of page relevance values and
out-going link values are determined, a relevance rank adjuster 140
adjusts the content relevance values for each page based on the
probability values of the link analysis. For example, the relevance
rank for page A is modified based on the relevance rank of pages
that refer to page A as a function of the probability of going to
page A from any of those pages. In other words, if more relevant
pages point to page A, then page A is probably more relevant. Thus,
there should be a greater probability that a user will be on page A
at any given time in relation to the other candidate pages. Using
FIG. 2 as an example, the relevance rank of page A becomes
"P.sub.A(being)" representing the probability of a user being on
page A at a given point in time is determined as follows:
P.sub.A(being)=P.sub.A(staying)*P.sub.A(being)+P(link
C-A)*P.sub.C(being) (6) which becomes
P.sub.A(being)=0.7P.sub.A(being)+0.4P.sub.C(being)=20/56 and for
the other candidate pages:
P.sub.B(being)=0.5P.sub.B(being)+0.3P.sub.A(being)+0.3P.sub.C(being)=21/5-
6 (7) P.sub.C(being)=0.3P.sub.C(being)+0.5P.sub.B(being)=15/56 (8)
where P.sub.A(being)+P.sub.B(being)+P.sub.C(being)=1 (9)
[0032] The set of four equations have three unknowns that are
solved using known linear algebra techniques. As shown in Equations
(6-9), the probability of being on a page is based on the relevance
of the page weighted by the probability of being on that page and a
sum of the values from all in-coming links weighted by the
probability of being on the parent page. The probability of a user
being on a page "Prob(being)" is a probability distribution to all
candidate pages, thus, the sum of probabilities is one (1). The
"Prob(being)" is an absolute probability whereas the probability of
staying on a page is conditional since it is assumed that a user
must be on that page.
[0033] Of course, there are other ways to use content-based
relevance values to vary or adjust the probability of being on or
leaving a page other than by the given examples. The fundamental
approach includes determining the relevance of a page based on a
combination of its content-based relevance value and the relevance
of links that point to the page. Thus, if more relevant pages point
to a page, its relevance value will be increased.
[0034] Illustrated in FIG. 3 is an exemplary computer-implemented
methodology of determining a relevance ranking for a page in
accordance with the present invention. The blocks shown represent
functions, actions or events performed therein. It will be
appreciated that computer software applications involve dynamic and
flexible processes such that the illustrated blocks can be
performed in other sequences different than the one shown. It will
also be appreciated by one of ordinary skill in the art that the
software of the present invention may be implemented using various
programming approaches such as procedural, object oriented or
artificial intelligence techniques.
[0035] With reference to FIG. 3, the process is shown as it applies
once a user issues a search query to locate relevant pages from the
network. When processing is completed, candidate pages are
sequentially listed to the user in an order of most relevant to
least relevant based on their relevance value. Since page linking
structure influences page relevance values, the link structure for
the web pages are determined (blocks 300 and 305). Using the
Internet as the exemplary network, software tools such as spiders
or web crawlers are used to visit web pages and determine links
referred to therein to determine the link structure. It will be
appreciated that the link structure can be predetermined prior to
receiving search queries or determined on the fly after candidate
pages for the search are retrieved and the link analysis can be
limited to those pages.
[0036] In response to the search query, the information retrieval
system 110 identifies a candidate set of pages from the network
that potentially match what the user is looking for. For example,
the keywords of the query are matched against a pregenerated
database of indexes that point to web pages containing or relating
to the keyword. The candidate pages are then received by the
relevance ranking system 115 for assignment of relevance rankings
(block 310). A content-based analysis is executed for each page to
determine a relevance value in view of the search query (block
315). As mentioned previously, the relevance value can be any value
that reasonably reflects the relevance of the content or subject
matter of a page in relation to the key words of the search query.
There are many software programs known in the art that can be used
to obtain an initial content-based relevance value for a page.
[0037] Once an initial relevance value is assigned for each page,
the relevance values are translated to a probability that a user
will stay on a given page (block 320). If, for example, the initial
relevance values are between 0-100 where 100 means the page is very
relevant, a simple translation includes directly relating the
relevance value of a page to a probability of staying on the page
(e.g. relevance value 70 is translated to a 70% probability of
staying). Depending on the type of relevance values used, they may
directly corresponded to a percentage value as in the above
example, or they may be transformed to fit into percentage values
based on a desired formula if there is no one-to-one
correspondence. The probability of staying on a page depends on a
content-based relevance ranking and topology of the pages (link
structure).
[0038] With further reference to FIG. 3 and FIG. 4, a link analysis
is performed for the candidate pages to generate link rankings by
applying the content-based rankings to the link structure (block
325). Using the probability of a user staying on a page
"Prob(staying)" (block 400), the process determines a probability
value that a user will leave the page because the page is not what
the user is looking for (block 405). As described previously, the
probability of leaving a given page is, for example,
1-Prob(staying). This value is then distributed to the out-going
links for that page (block 410). However, the probability that a
user follows a link is not equivalent for all links. Rather, a user
is more likely to follow a link if the user believes that the link
will take them to a more relevant page. The distribution of values
to links is based on this principle.
[0039] Using the example candidate pages from FIG. 2, a given page
will be referred to as a "parent" page and the pages being linked
to from the parent page will be referred to as "child" pages. Thus,
page C has two child pages, namely, A and B. Also, page B is a
child of page A, and page C is a child of page B. A link ranking,
representing the probability of a user following a link, is based
on the probability of leaving the parent page and the content-based
relevance of the child pages. An exemplary distribution is shown
above in Equations (1)-(5). Thus, the probability of a user
following a link (link value) is a function of the relevance value
of the page,_the relevance values of its child pages and all other
child pages.
[0040] With reference again to FIG. 3, at block 330, after the link
analysis rankings are found, the relevance values for each page are
adjusted based on a combination of a page's current relevance value
and link analysis rankings. The relevance value of a page is
determined as the probability of a user being on that page
"Prob(being)" in relation to the other candidate pages. Exemplary
adjustments are shown above in Equations (6)-(8). When the adjusted
page relevance rankings are obtain, the adjustments can be repeated
using an iterative process until a desire threshold is met (block
335). When complete, the relevance rankings for the candidate pages
are returned to the information retrieval system 110 and the
candidate pages are displayed to the user typically in an order of
most relevant to least relevant.
[0041] With the present teachings, relevance rankings can be based
on linguistically aware link analysis where link values incorporate
content-based relevance values of associated pages as a function of
the page link structure. Link analysis rankings can become
linguistically aware since they can be combined with content-based
relevance values. In one example as described previously, a
probability of not leaving a webpage and the probability of
following an outgoing link from a webpage are functions of the
relevance of referred webpages and the relevance of the webpage. In
this manner, improved relevance rankings for web pages can be
obtained based on a given search query.
[0042] While various examples have been illustrated by the
description of embodiments thereof, and while the embodiments have
been described in considerable detail, it is not the intention of
the applicants to restrict or in any way limit the scope of the
appended claims to such detail. Additional advantages and
modifications will readily appear to those skilled in the art. For
example, the relevance rank system may be a function within the
information retrieval system or an external program. The link
structure logic may perform the structure analysis dynamically or
it may simply obtain link structure information from an external
application or source which is available. The same applies to the
content analyzer logic. Therefore, the example systems and methods,
in their broader aspects, are not limited to the specific details,
the representative apparatus, and illustrative examples shown and
described. Accordingly, departures may be made from such details
without departing from the spirit or scope of the applicant's
general inventive concept.
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