U.S. patent application number 16/184995 was filed with the patent office on 2019-07-04 for content item selection criteria generation.
The applicant listed for this patent is GOOGLE LLC. Invention is credited to Hongji Bao, Ian James Leader, Clemens Lombriser.
Application Number | 20190205948 16/184995 |
Document ID | / |
Family ID | 52827033 |
Filed Date | 2019-07-04 |
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United States Patent
Application |
20190205948 |
Kind Code |
A1 |
Lombriser; Clemens ; et
al. |
July 4, 2019 |
CONTENT ITEM SELECTION CRITERIA GENERATION
Abstract
Selection of content selection criteria based on entities
related by relationship dimensions. In one aspect, a method
receives a selection of a seed entity described in entity relation
data, the entity relation data defining instances of entities, and
for each entity one or more relationship dimensions; generating a
set of selected entities; iteratively updating the set of selected
entities, each iteration comprising: determining a set of
relationship dimensions from the entities in the set of selected
entities, each relationship dimension in the set being selected
from the one or more relationship dimensions of the entities in the
set of selected entities, receiving a selection of one of the
relationship dimensions and in response: determining a set of
candidate entities from the relationship dimensions and in response
to receiving a selection of one or more candidate entities,
updating the set of selected entities to include the one or more
candidate entities.
Inventors: |
Lombriser; Clemens; (Zurich,
CH) ; Leader; Ian James; (Zurich, CH) ; Bao;
Hongji; (Sunnyvale, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
GOOGLE LLC |
Mountain View |
CA |
US |
|
|
Family ID: |
52827033 |
Appl. No.: |
16/184995 |
Filed: |
November 8, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14870321 |
Sep 30, 2015 |
10248976 |
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16184995 |
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14060325 |
Oct 22, 2013 |
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14870321 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0277
20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1-18. (canceled)
19. A computer-implemented method to provide suggested keywords to
associate with content items, comprising: receiving, by one or more
computing devices and from a content item provider, a content item
to be displayed in conjunction with one or more electronic
documents on user computing devices; providing, by the one or more
computing devices, a request to the content item provider to input
one or more keywords to associate with the content item; providing,
by the one or more computing devices, one or more recommended
keywords to the content item provider; providing, by the one or
more computing devices, content indicating traffic associated with
each recommended keyword; receiving, by the one or more computing
devices, the input of one or more keywords to associate with the
content item; receiving, by the one or more computing devices, the
request to serve the content item in connection with an electronic
document, the request comprising one or more keyword search terms
associated with a search query; determining, by the one or more
computing devices, that the one or more keyword search terms
comprises at least one of the inputted keywords based on a
comparison of the one or more keyword search terms to the inputted
one or more keywords; and based on the determination that the
keyword search terms comprise at least one of the one or more
inputted keywords, providing, by the one or more computing devices,
the content item to the user computing device to display in
conjunction with the electronic document.
20. The computer implemented method of claim 19, wherein the
content indicating traffic associated with each recommended keyword
is based on an estimated number of impressions each recommended
keyword will generate.
21. The computer implemented method of claim 19, wherein the
content indicating traffic associated with each recommended keyword
is based on an estimated number of user searches that will include
keyword search terms comprising each recommended keyword.
22. The computer implemented method of claim 19, wherein the
content indicating traffic associated with each recommended keyword
is based on an estimated number of conversions the inputted keyword
will generate.
23. The computer implemented method of claim 22, wherein a
conversion comprises an action taken by a user in response to the
content item.
24. The computer implemented method of claim 19, wherein the
content indicating traffic associated with each recommended keyword
is based on an estimated number of clicks the inputted keyword will
generate.
25. The computer implemented method of claim 19, wherein the one or
more recommended keywords are based on an analysis of previously
entered keywords associated with the content item.
26. The computer implemented method of claim 19, wherein the one or
more recommended keywords are based on a received input of
features.
27. The computer implemented method of claim 19, wherein the
inputted keywords are received via a content item template.
28. The computer implemented method of claim 19, wherein the
electronic document is a website.
29. The computer implemented method of claim 19, wherein the
content indicating traffic associated with each recommended keyword
is based on a number of impressions that each recommended keyword
generated when associated with previous content items.
30. The computer implemented method of claim 19, wherein the
content indicating traffic associated with each recommended keyword
is based on a number of user searches in which each recommended
keyword appeared when associated with previous content items.
31. The computer implemented method of claim 19, wherein the
content indicating traffic associated with each recommended keyword
is based on a number of conversions that each recommended keyword
generated when associated with previous content items.
32. A computer program product, comprising: a non-transitory
computer-readable storage device having computer-executable program
instructions embodied thereon that when executed by one or more
computing devices cause the computer to provide suggested keywords
to associate with content items, the computer-readable program
instructions comprising: computer-executable instructions to
receive from a content item provider, a content item to be
displayed in conjunction with one or more electronic documents on
user computing devices; computer-executable instructions to provide
a request to the content item provider to input one or more
keywords to associate with the content item; computer-executable
instructions to provide one or more recommended keywords to the
content item provider; computer-executable instructions to provide
content indicating traffic associated with each recommended
keyword; computer-executable instructions to receive an input of
one or more keywords to associate with the content item;
computer-executable instructions to receive the request to serve
the content item in connection with an electronic document, the
request comprising one or more keyword search terms associated with
a search query; computer-executable instructions to determine that
the one or more keyword search terms comprises at least one of the
inputted keywords based on a comparison of the one or more keyword
search terms to the inputted one or more keywords; and
computer-executable instructions to provide the content item to the
user computing device to display in conjunction with the electronic
document based on the determination that the keyword search terms
comprise at least one of the one or more inputted keywords.
33. The computer program product of claim 32, wherein the content
indicating traffic associated with each recommended keyword is
based on an estimated number of impressions each recommended
keyword will generate.
34. The computer program product of claim 32, wherein the content
indicating traffic associated with each recommended keyword is
based on an estimated number of user searches that will include
keyword search terms comprising each recommended keyword.
35. The computer program product of claim 32, wherein the content
indicating traffic associated with each recommended keyword is
based on an estimated number of conversions the inputted keyword
will generate.
36. The computer program product of claim 35, wherein a conversion
comprises an action taken by a user in response to the content
item.
37. The computer program product of claim 32, wherein the content
indicating traffic associated with each recommended keyword is
based on an estimated number of clicks the inputted keyword will
generate.
38. A system to provide suggested keywords to associate with
content items, comprising: a storage device; and a processor
communicatively coupled to the storage device, wherein the
processor executes application code instructions that are stored in
the storage device to cause the system to: receive from a content
item provider, a content item to be displayed in conjunction with
one or more electronic documents on user computing devices; provide
a request to the content item provider to input one or more
keywords to associate with the content item; provide one or more
recommended keywords to the content item provider; provide content
indicating traffic associated with each recommended keyword;
receive an input of one or more keywords to associate with the
content item; receive the request to serve the content item in
connection with an electronic document, the request comprising one
or more keyword search terms associated with a search query;
determine that the one or more keyword search terms comprises at
least one of the inputted keywords based on a comparison of the one
or more keyword search terms to the inputted one or more keywords;
and provide the content item to the user computing device to
display in conjunction with the electronic document based on the
determination that the keyword search terms comprise at least one
of the one or more inputted keywords.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of and claims priority to
U.S. patent application Ser. No. 14/870,321, filed Sep. 30, 2015,
and entitled "Content Item Selection Criteria Generation," which is
a continuation of U.S. patent application Ser. No. 14/060,325,
filed Oct. 22, 2013, and entitled "Content Item Selection Criteria
Generation." The complete disclosure of the above-identified
priority application is hereby fully incorporated herein by
reference.
BACKGROUND
[0002] This specification relates to generating selection criteria
for selecting content. The Internet provides access to a wide
variety of resources. For example and/or audio files, as well as
web pages for particular subjects, are accessible over the
Internet. Access to these resources presents opportunities for
content items, such as advertisements (or other content items) to
be provided with the resources or with search results that identify
the resources. For example, a web page can include "slots" (i.e.,
specified portions of the web page) in which advertisements (or
other content items) can be presented. These slots can be defined
in the web page or defined for presentation with a web page, for
example, in a separate browser window. Advertisements or other
content items that are presented in slots of a resource are
selected for presentation by a content distribution system.
SUMMARY
[0003] In general, one innovative aspect of the subject matter
described in this specification can be embodied in methods that
include the actions of receiving a selection of a seed entity
described in entity relation data, wherein the entity relation data
defines instances of entities, and for each entity one or more
relationship dimensions, each relationship dimension defining a
relationship between the entity and one or more other entities;
generating a set of selected entities, the set of selected entities
being the seed entity; iteratively updating the set of selected
entities, each iteration comprising: determining a set of
relationship dimensions from the entities in the set of selected
entities, each relationship dimension in the set being selected
from the one or more relationship dimensions of the entities in the
set of selected entities; receiving a selection of one of the
relationship dimensions and in response: determining a set of
candidate entities, each candidate entity in the set being an
entity related to one of the entities in the set of selected
entities by selected relationship dimension; and in response to
receiving a selection of one or more candidate entities, updating
the set of selected entities to include the one or more candidate
entities. Other embodiments of this aspect include corresponding
systems, apparatus, and computer programs, configured to perform
the actions of the methods, encoded on computer storage
devices.
[0004] Particular embodiments of the subject matter described in
this specification can be implemented so as to realize one or more
of the following advantages. The subject matter described in this
specification facilitates exploration of relationships of entities
among multiple different relationships. The different relationships
are presented in a user interface, and are determined from a set of
selected entities. Additional entities are identified by selected
relationships to the selected entities and entity relation data. A
user may add and remove entities from a set of selected entities,
and iteratively revise the selected relationships and selected
entities. The iterative process allows for the user to explore
non-intuitive relationships among various entities and to define a
concept focus from these various relationships and selected
entities. In the case of advertisers, for example, these features
allow the advertisers to define a concept focus that can be used to
generate a robust but focused set of selection criteria for the
concept focus. Because the concept focus is derived from the
selected entities, and because the selected selection criteria are
identified from emergent and possibly non-intuitive relationships,
the selection criteria that are selected based on the concept focus
will include selection criteria that an advertiser may have
otherwise overlooked or failed to derive. A user interface
facilitates the exploration of entity relations in an intuitive and
fluid manner, which, in turn, allows the advertiser to concentrate
on concept focus creation and exploration and to create and explore
the concept focus quickly and efficiently.
[0005] Another advantage is that key metrics, e.g. estimates of
what adding an entity to a candidate set would offer in terms of
impressions, clicks, conversions, marginal cost per conversion,
etc., can be shown for each addition to the selected set of
entities so the advertiser can add only entities that meet certain
metric targets, as well as concepts, instead of first having to add
the selection criterion to the selected set of selection criteria
to determine the estimated performance.
[0006] The details of one or more embodiments of the subject matter
described in this specification are set forth in the accompanying
drawings and the description below. Other features, aspects, and
advantages of the subject matter will become apparent from the
description, the drawings, and the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 is a block diagram of an example environment in which
content is distributed to user devices.
[0008] FIG. 2 is a block diagram of a portion of an example
knowledge graph representation of entity relationship data.
[0009] FIG. 3 is a flow diagram of example processes for generating
content item selection criteria.
[0010] FIGS. 4A-4H are illustrations of a user interface that
facilitates the generation of content item selection criteria
[0011] FIG. 5 is an entity relationship diagram of a selected
entity set and relationship dimensions.
[0012] FIG. 6 is block diagram of an example computer system.
[0013] Like reference numbers and designations in the various
drawings indicate like elements.
DETAILED DESCRIPTION
[0014] Overview
[0015] FIG. 1 is a block diagram of an example environment 100 in
which content is distributed to user devices 106. The example
environment 100 includes a network 102, such as a local area
network (LAN), a wide area network (WAN), the Internet, or a
combination thereof. The network 102 connects websites 104, user
devices 106, advertisers 108, and a content distribution system
110. The example environment 100 may include many different
websites 104, user devices 106, and advertisers 108.
[0016] A website 104 is one or more resources 105 associated with a
domain name and hosted by one or more servers. An example website
is a collection of web pages formatted in hypertext markup language
(HTML) that can contain text, images, multimedia content, and
programming elements, such as scripts. Each website 104 is
maintained by a publisher, which is an entity that controls,
manages and/or owns the website 104.
[0017] A resource 105 is any data that can be provided over the
network 102. A resource 105 is identified by a resource address
that is associated with the resource 105. Resources include HTML
pages, documents, images, video, and feed sources, to name only a
few. The resources can include content, such as words, phrases,
images and sounds, that may include embedded information (such as
meta-information in hyperlinks) and/or embedded instructions (such
as scripts). Units of content that are presented in (or with)
resources are referred to as content items.
A user device 106 is an electronic device that is capable of
requesting and receiving resources over the network 102. Example
user devices 106 include personal computers, mobile communication
devices, and other devices that can send and receive data over the
network 102. A user device 106 typically includes a user
application, such as a web browser, to facilitate the sending and
receiving of data over the network 102.
[0018] A user device 106 can submit a resource request 107 that
requests a resource 105 from a website 104. In turn, data
representing the requested resource 105 can be provided to the user
device 106 for presentation by the user device 106. The requested
resource 105 can be, for example, a page of a website 104, web page
from a social network, or another type of resource. The resource
105 includes resource content 116 that is presented on the user
device 106. The resource 105 can also specify portions, e.g.,
content slots 118, in which content items, such as advertisements,
can be presented. In the case of advertisements, the content slots
118 are often referred to as advertisement slots 118.
[0019] When a resource 105 is requested by a user device 106,
execution of code associated with an advertisement slot 118 in the
resource 105 initiates a request for an advertisement to populate
the advertisement slot 118. The advertisement request can include
characteristics of the advertisement slots 118 that are defined for
the requested resource 114. For example, a reference (e.g., URL) to
the requested resource 114 for which the advertisement slot 118 is
defined, a size of the advertisement slot 118, and/or media types
that are eligible for presentation in the advertisement slot 118
can be provided to the content distribution system 110. Similarly,
keywords associated with a requested resource ("resource keywords")
or entities that are referenced by the resource can also be
provided to the content distribution system 110 to facilitate
identification of advertisements that are relevant to the requested
resource 114. The keywords may be derived from the content of the
resource 105, or, in the case of the resource being a search
results page, from the content of a query submitted by a user
device 106. Other ways of deriving keywords for the request may
also be used.
[0020] The advertisements (or other content items) that are
provided in response to an advertisement request (or another
content item request) are selected based on selection criteria for
the advertisements. Selection criteria are a set of criteria upon
which distribution of content items are conditioned. In some
implementations, the selection criteria for a particular
advertisement (or other content item) can include distribution
keywords that must be matched (e.g., by resource keywords) in order
for the advertisement to be eligible for presentation. The
selection criteria can also specify a bid and/or budget for
distributing the particular advertisement. Selection criteria can
also be entity based and refer to entities, as that term is defined
below, or a combination of entities and keywords, or other criteria
that can be used to select content based on features that satisfy
the criteria. For brevity and illustration, the selection criteria
used in the examples that follow are keywords; however, the
generation of content item selection criteria of types different
from keywords can also be done by the processes described in the
sections that follow.
[0021] In the case of advertisements, the content distribution
system 110 includes a stores campaign data 113 and performance data
115. The campaign data 113 stores, for example, advertisements,
selection criteria, and budgeting information for advertisers. The
performance data 115 stores data indicating the performance of the
advertisements that are served and for which selection data the
advertisements were served. Such performance data can include, for
example, click through rates for advertisements, the number of
impressions for advertisements, and the number of conversions for
advertisements, both in the aggregate and on a per-query or
per-keyword basis. Other performance data can also be stored.
[0022] The campaign data 113 and the performance data 114 are used
as input parameters to an advertisement auction. In particular, the
content distribution system 110, in response to each request for
advertisements, conducts an auction to select advertisements that
are provided in response to the request. The advertisements are
ranked according to a score that, in some implementations, is
proportional to a value based on an advertisement bid and one or
more parameters specified in the performance data 115. The highest
ranked advertisements resulting from the auction are selected and
provided to the requesting user device 106 for display in the slots
118.
[0023] In situations in which the systems discussed here collect
personal information about users, or may make use of personal
information, the users may be provided with an opportunity to
control whether programs or features collect user information
(e.g., information about a user's social network, social actions or
activities, profession, a user's preferences, or a user's current
location), or to control whether and/or how to receive content from
the content server that may be more relevant to the user. In
addition, certain data may be treated in one or more ways before it
is stored or used, so that personally identifiable information is
removed. For example, a user's identity may be treated so that no
personally identifiable information can be determined for the user,
or a user's geographic location may be generalized where location
information is obtained (such as to a city, ZIP code, or state
level), so that a particular location of a user cannot be
determined. Thus, the user may have control over how information is
collected about the user and used by a content server.
[0024] Related Entity Selection and Content Selection Criteria
Generation
[0025] To help users generate content selection criteria, the
content distribution system includes a related entity selector 120
and a content selection criteria generator 122. The related entity
selector 120 facilitates the generation of a concept focus using
entities. As used herein, entities are concepts such as persons,
places, things, ideas, or features that are distinguishable from
one another, e.g., based on context, and are the bases of an entity
relation construct modeled by entity relation data. In particular,
entities can represent or refer to specific items, such as
particular products, services, companies, places, persons, etc.
[0026] In entity relation data, the relations between any two
entities are represented by at least one relation linking the two
entities, or multiple relations linking the two entities by one or
more intermediate entities. Entities, as represented by the entity
relation data, can be referenced by selection criteria or even be
included in the selection criteria, depending on the types of
selection criteria being used. For example, in the case of
keywords, a keyword may refer to an entity, e.g., the keyword
"beverages" and "soda" may derived from the entity "beverage" in
the entity relation data.
[0027] A concept focus is a collection of entities selected from
the entity relation construct. Once a concept focus is defined, the
entities of the concept focus are provided to a content selection
criteria generator 122 to generate content selection criteria.
[0028] To generate a concept focus, one or more seed entities are
used to generate a set of selected entities. The seed entities can
be selected manually by a user, or automatically retrieved from
another source such as by processing a web page document, a web
site, or even processing an advertisement group and advertising
campaign. The selected set of entities is then iteratively updated
by selecting, for each iteration, a relationship dimension that is
identified based on the set of selected entities. For each
iteration, the selected relationship dimension is used to identify
additional entities that are related to one, some or all of the
entities in the selected set of entities. Additional entities are
then selected and added to the set of selected entities, and
another iteration to update the set of selected entities may be
performed.
[0029] Within each iteration the user can choose a new dimension of
relatedness for new entity suggestions that expand or compact the
currently selected set of entities. The related entity selector 120
provides visualizations of suggested new entities and relationship
dimensions. From the visualization, the user may choose any number
of entities to add to the selected set of entities. Entities may
also be selected into a "negative" set that repels entities in
relatedness computation.
[0030] Relationship dimensions are selected based on the entities
in the selected set of entities, and thus differ for different
entities. For example, an automobile may have particular
relationships with other entities, e.g., relationship dimensions
"other cars made by Car Co.," "other SUVs," "other hybrids," etc.
Conversely, a beverage may have different relationship dimensions,
such as "other low calorie drinks," "other carbonated beverages,"
etc. The related entity selector 120 may present all available
relations dimensions for an entity set, or, alternatively, may
present a proper subset of relationship dimensions. The proper
subset may be suggested based on dimensional criteria, such as
strongest relationships as indicated by an edge weight, a maximum
node traversal in an entity relation graph, etc. Alternatively, a
user may also search for dimensions, specify dimensions, or explore
available dimensions by means of a graphical user interface.
[0031] Once the user indicates satisfaction with the set of
selected entities, the set is used to define the concept focus of
the user. The concept focus may then be used, for example, to
generate keywords for advertising targeting.
[0032] The entity relation data can be any data that defines
instances of entities and, for each entity, one or more
relationship dimensions. Each relationship dimension, in turn,
defines a relationship between the entity and one or more other
entities. The relationship can be directly or indirectly defined.
For example, one type of entity relation data that can be used is a
knowledge graph. FIG. 2 is a block diagram of a portion of an
example knowledge graph representation 200 of entity relationship
data. The knowledge graph has nodes and edges. Each node in the
knowledge graph represents a different entity, and pairs of nodes
in the knowledge graph are connected by one or more edges. Each
edge representing a relationship dimension that defines a
relationship between the two entities represented by the pair of
nodes, or several edges represent a series of relationships that
connect two entities by one or more intermediate entities. As shown
in FIG. 2, the edges are unidirectional, but in other variations
the edges may be bidirectional.
[0033] For example, the knowledge graph 200 includes node 210 and
220 representing two car companies, Car Co A and Car Co B; nodes
212, 214, 216, 222, 224, and 226, representing car models, and
nodes 230, 240, 250 and 260, representing the distinct car classes
of Hybrid, Fuel Efficient, SUV, and Electric Vehicle, respectively.
Nodes 212, 214, and 216 are connected to node 210 by the "models"
relationship dimension, which means the cars Mod AA, Mod AB, and
Mod AC are models made by Car Co A. Nodes 222, 224, and 226 are
likewise connected to node 220.
[0034] Nodes 212 and 224 are connected to node 250, which indicated
that car models Mod AA and Mod BB are SUVs; nodes 214, 216 and 222
are connected to node 240, which indicates the car models Mod AB,
Mod AC and Mod BA are fuel efficient; nodes 216 and 222 are
connected to node 230, which indicates the car models Mod AC and
Mod BA are hybrids, and node 226 is connected to node 260, which
indicates the car model Mod BC is an electric vehicle. Various
other relationships dimensions are also shown in the graph 200.
Although a hierarchy is emergent from the small portion shown, the
graph 200 itself may be acyclic, and is not required to have
cycles. Furthermore, the graph need not be a directed graph.
[0035] Generating a concept focus, and resulting concept item
selection criteria, is described with reference to FIGS. 3 and
4A-4H below. In particular, FIG. 3 is a flow diagram of example
processes 300 for generating content item selection criteria, and
FIGS. 4A-4H are illustrations of a user interface 400 that
facilitates the generation of content item selection criteria. The
processes 300 include a first process 310 performed at the content
distribution system 110, and a second process 330 performed at the
user device. The processes 310 and 330, however, may also be
combined and performed by a single computer device or system,
provided the single computer device or system has access to entity
relation data and other data, such as campaign data 113.
[0036] In operation, the content distribution system 110 provides
an application, or a web page, to a user device 106. The user
device 106 performs operations by executing instructions in the
application or the web page to generate the user interface 400 of
FIG. 4A. The user interface 400 includes an entity selection pane
410, a related entities pane 430, and a content selection criteria
pane 450. In FIG. 4A, the user interface 400 is empty, indicating
the user has not yet made any selections.
[0037] The entity selection pane 410 facilitates the selection of a
seed entity and the adjustment of a selected set of entities. Input
field 412 allows a user to search for an entity; input field 414
allows a user to specify a web page that can be processed to
identify entities; and input field 416 allows a user to specify an
advertising campaign or advertising group to identify entities.
Other ways to initially identify one or more seed entities can also
be used. Furthermore, the input fields 412, 414 and 416 can also be
used during any iteration to add to a set of selected entities
displayed in the selected entity field 418.
[0038] The related entities pane 430 includes a get related
entities command 432, a relationship dimension field 434, and a
candidate entity field 436. As will be explained below, a user can
select a relationship dimension by use of the relationship
dimension field 434, then invoke the get related entities command
432 to populate the candidate entity field 436 with candidate
entities. Candidate entities in the candidate entity field 436 can
then be selected for inclusion in the selected entities field
418.
[0039] The content selection criteria pane 450 is used to display
content selection criteria, e.g., keywords, generated from the
entity names of the related entities in the selected entities field
418. In the case of keyword selection criteria, the keywords may be
generated from the entity names, aliases e.g., acronyms or other
commonly used names for the entity, such as Sport Utility Vehicle,
SUV, etc., common misspellings, or other associated strings. The
user may accept or reject the individual criterion of the
criteria.
[0040] In operation, the process 310 receives a selection of a seed
entity (312). As described above, the seed entity may be selected
in a variety of ways. In FIG. 4B, for example, the user has entered
a search for an entity. The user has entered the text "Mod A," and
an entity search box 413 has appeared. The user selects the entity
"Mod AA," as indicated by the cursor over the search result "Mod
AA." The user device 106 sends data to the content distribution
system 110 indicating the selection.
[0041] The process 310 generates a set of selected entities (314).
The related entity selector 120, for example, generates a set of
selected entities that includes only the seed entity. Because the
first iteration populates the set of selected entities, only the
seed entity is included in the set. Additional seed entities can
also be selected, but for brevity the example description will use
one seed entity, which, in this case, is the entity Mod AA, shown
in the selected entity field 418 of FIG. 4C.
[0042] The process 310 determines a set of relationship dimensions
from the entities in the set of selected entities and provides the
set of relationship dimensions to a user device (316). At the user
device, the process 330 displays relationship dimensions (332) and
displays them. For example, as shown in FIG. 4C, a selection box
435 lists a set of relationship dimensions selected from the one or
more relationship dimensions of the entities in the set of selected
entities.
[0043] To select the relationship dimensions, the related entity
detector 120, in some implementations, processes the entity
relation data beginning at the node (or nodes) of the selected
entities. For example, as shown in FIG. 2, the entity Mod AA is
represented by node 212. Because the entity Mod AA is related to
the "SUV" node 250 by a "Type of` relationship, the related entity
detector 120 identifies the relationship "Type of SUV" as a
relationship dimension. This is represented by the "Other SUVs"
option displayed in the selection box 435. Likewise, because the
entity Mod AA is related to the "Car Co A" node 210 by a "Model"
relationship, the related entity detector 120 identifies the
relationship "Car models of Car Co A" as a relationship dimension.
This is represented by the "Other Car Co A Models" option displayed
in the selection box 435.
[0044] These two relationship dimensions--"Car Co A" and "Other Car
Co A Models"--are identified by direction relations to the node
212. However, additional relations can also be identified by
traversing one or more nodes, up to a maximum of N nodes, where
N=2, 3, or 4, for example. For example, the relationship dimensions
"Cars by Competitor Car Co B" is identified by traversing the node
210 to node 220 by the "competitor" edge and the "Models" edges
from nodes 210 to nodes 222, 224 and 226. Additional relations,
such as "Fuel Efficient Cars" and "Hybrid" cars are identified by a
similar process. Note that because the entity Mod AA, according to
the knowledge graph 200, is neither a "Fuel Efficient" car nor a
"Hybrid" car, the relationship dimensions as presented do not
indicate that the Mod AA has a direct relation to either of these
entities, i.e., the word "Other" is omitted from the relationship
dimension in the selection box 435, while the word "Other" is
included with the relationship dimension for SUVs.
[0045] In some implementations, the number N may vary by the
type(s) of edges being traversed or the type of starting entity.
For example, for geographic edges, the number N may be relatively
larger, e.g., Alcatraz--7 San Francisco--7 San Francisco Bay
Area--7 Northern California--7 California--7 USA--7 North
America--7 America. Conversely, for related products, the number of
nodes may be fewer (e.g., 3) so as to avoid subject matter drift,
e.g., Mod AA--7 SUV--7 Minivan for "related products" edges,
etc.
[0046] Other types of relationship dimensions can also be
presented. For example, common attributes of the selected entities
can be specified, and the resulting entities that are selected are
attributes of automobiles that are common to each entity in the
selected entity field 418, such as "SUV."
[0047] Abstractions of the selected entities may include one or
more abstractions of one or more entities to a larger class. For
example, suppose the knowledge graph 200 includes the following
relations, indicated by the notation [Relation], where: [0048] Mod
AA [Type of] SUV [Type of] Vehicle [0049] [Engine] Six Cylinder
[Type of] Gasoline Powered
[0050] The entities for abstractions may thus include SUV, Vehicle,
Six Cylinder, and Gasoline Powered, for example. Other types of
relationship dimensions can also be used.
[0051] The number of relationship dimensions can, in some
implementations, be limited to a maximum number, e.g., 5, 8, or 10.
The order of the dimensions can, in some implementations, be based
on prior selections by other users. For example, a potential
relationship dimension is "CEO of Car Co A" based on the
relationship dimension of "Models" linking node 212 to 210.
However, this relationship dimension may be selected so
infrequently that it is not shown in the selection box 435. In
other implementations, the selection box 435 can be scrollable, and
can show all relationship dimensions derived from traversing up to
N maximum nodes from the nodes of the selected entity or entities.
Other ways of ordering the relationship dimensions can also be
used.
The order of the dimensions can also be based on edge weights (if
included in the knowledge graph 200) that indicate a confidence in
the accuracy of the relationship. For example, a relationship
dimension corresponding to an edge weight of 0.98 would be rated
higher than a relationship dimension corresponding to an edge
weight of 0.58.
[0052] The process 330 receives selection of relationship dimension
and sends selection data to server (334). For example, as shown in
FIG. 4D, the user selected the "Other Car Co A Modes" relationship
dimension, and has requested related entities for this relationship
dimension, as indicated by the cursor over the get related entities
command 432. Data indicating the selection is provided to the
related entity selector 120, wherein the process 310 receives the
selection of one of the relationship dimensions (318).
[0053] In response, the process 310 determines a set of candidate
entities and provides the candidate entities to the user device
(320). Each candidate entity in the set is an entity related to one
of the entities in the set of selected entities by selected
relationship dimension. For example, the related entity selector
120 selects all other entities connected to the node 210 by a
"Models" link. In this case, entities Mod AB, Mod AC, Mod AD, Mod
AE, and Mod AF are identified by traversing from the node 210 for
each "Models" edge. Data describing the candidate entities are sent
to the user device, where the process 330 displays candidate
entities (334). For example, in FIG. 4D, the candidate entities Mod
AB, Mod AC, Mod AD, Mod AE, and Mod AF are displayed in the
candidate entity field 436.
[0054] The process 330 receives selection of one or more candidate
entities and sends selection data to server (336). For example, as
shown in FIG. 4E, a user has selected the graphical representation
of the entity Mod AE and is dragging it to the selected entities
field 418. When the user deposits the entity Mod AE into the
selected entities field 418, the action is interpreted as a
selection of the candidate entity for inclusion in the selected
entities. The user device sends data to the server indicating the
selection of the candidate entity. As an alternative, the user may
also select entities using checkboxes and a button that copies the
selected entities to the selected entities field, or some other
user interface selection feature.
[0055] At the content distribution system, the process 310 receives
the selection of one or more candidate entities and updates the set
of selected entities to include the one or more candidate entities
(322). The process 310 then determines whether additional updates
to the set of selected entities are to be made (324). For example,
if the user device sends a request for additional relationship
dimensions based on the updated set of selected entities, then the
process 310 returns to operation 316. Otherwise, the process 310
causes the generation of content selection data (326).
[0056] FIGS. 4F-4H illustrate a final iteration being performed
after one or more prior iterations. In FIG. 4F, the user has
selected the entities Mod AA, Mod AE and Mod BA, and is browsing
available relationship dimensions in the selection box 435. The
user selects the "Search for abstractions of the selected entities"
in FIG. 4F, and then selects the "Get Related Entities" command
432. The resulting user interface 400, and the set of candidate
entities, is shown in FIG. 4G. In FIG. 4G, the candidate set now
includes entities such as "Fuel Efficient," "SUVs," and other
car-related entities. However, additional entities, such as
"Computer Games" and "Vehicle Safety Report," and potentially more
entities, are also shown. The entity "Computer Games," may have
been identified because one of the selected entities is the subject
of a computer game, and this relationship is modeled in the
knowledge graph as:
[0057] Mod AA [Includes] Mountain Racer 7.0 [Instance of] Computer
Game
[0058] Other related entities are also shown, such as the computer
game "Mountain Racer 7.0," and other entities that relate to one or
more of the selected entities in the selected entity field 418.
[0059] Suppose that the user is an advertiser that is attempting to
identify keywords for the Mod AA SUV. The advertiser, however, was
not aware that the Mod AA SUV was modeled in the game "Mountain
Racer 7.0." By examining additional related entities, the
advertiser discovers that the SUV was also the vehicle driven by a
recent winner of Pike's Peak Hill Climb, which is also represented
by an entity in the knowledge graph 200.
[0060] The advertiser is designing an ad group for placement of
advertisements on outdoors and sporting related websites, and thus
selects the "Pike's Peak Hill Climb" entity, along with several
other entities, as shown in FIG. 4H. The advertiser has also
removed the entity Model AE from the selected set of entities.
Thereafter, the advertiser selects the "Generate keywords" command
454. In response, the user device 106 sends data to the content
distribution system 110, which, in turn, causes the related entity
selector 120 to submit the description of the selected set of
entities to the content selection criteria generator 122. In
response, the content selection criteria generator 122 generates a
set of candidate content selection criterion based on the set of
selected entities. In this case, a set of candidate keywords are
generated based on the terms "Mod AA," "Mod BB," "Car Co A," "Car
Co B," "Vehicle Safety Report," and "Pike's Peak Hill Climb." The
advertiser can select some of the keywords, such as a subset, or
all of the keywords, for inclusion in the content selection
criteria for use by the content management system 110 to select and
provide advertisements to user devices. Alternatively or in
addition, the advertiser can continue to revise the set of related
entities as described with reference to FIGS. 4A-4F.
[0061] In some implementations, relationship dimensions can be
defined as either positive or negative by the user, and multiple
dimensions can be selected for determining candidate entities.
Then, when determining a set of candidate entities, the related
entity selector 120 identifies only candidate entities that are
related to one or more of the entities in the set of selected
entities by the positive relationship dimensions and not related to
any entities in the set of selected entities by any of the negative
relationship dimensions.
Additional Features And Variations
[0062] FIG. 5 is an entity relationship diagram 500 of a selected
entity 510 set and relationship dimensions. The diagram 500, in
some implementations, is used to visualize a most relevant set of
relatedness dimensions and related entities for a selected entity.
This can be presented instead of, or in addition to, the candidate
entities in the candidate entity field 436. A user may select an
entity from the graph to include it in the set of related
entities.
[0063] Optionally, a user may traverse the diagram 500, and explore
additional relationship dimensions and additional entities by
moving the focus of the graph to a candidate entity. For example, a
user may click on the node 546 for "Mod HF," and the node may move
to the center of the graph 300. Thereafter, relationship dimensions
up to N nodes, e.g., 2 nodes, separate from the node 546, may be
explored.
[0064] Other visualizations can also be used.
[0065] In some implementations, the processes described above are
language independent. In particular, a knowledge graph derived from
relations discovered in a document corpus facilitates related
entity exploration in a variety of different languages. Because a
knowledge graph for a language may reflect the concept of
relatedness by culture, the same process can be implemented in
different languages yet at the same time avoid cultural biases.
[0066] The examples above are described in the context of a
knowledge graph. However, other entity relation data can also be
used instead of a knowledge graph. For example, in some
implementations, the entity data can model class-instance pairs and
attribute relations.
Nodes of a first node type, each representing a distinct class of
entities, are linked to nodes of a second type, each representing
an instance of an entity that belongs to the class. Nodes of a
third node type, each representing attributes of either an instance
and/or a class, may link to one or more of the nodes of the first
or second types. Each instance of an entity is thus related to one
or more other entities by common attributes to which the entities
are linked, by common attributes to which their respective classes
are linked, and by common classes to which the entities belong.
[0067] FIG. 6 is block diagram of an example computer system 600
that can be used to perform operations described above. The system
600 includes a processor 610, a memory 620, a storage device 630,
and an input/output device 640. Each of the components 610, 620,
630, and 640 can be interconnected, for example, using a system bus
650. The processor 610 is capable of processing instructions for
execution within the system 600. In one implementation, the
processor 610 is a single-threaded processor. In another
implementation, the processor 610 is a multi-threaded processor.
The processor 610 is capable of processing instructions stored in
the memory 620 or on the storage device 630.
[0068] The memory 620 stores information within the system 600. In
one implementation, the memory 620 is a computer-readable medium.
In one implementation, the memory 620 is a volatile memory unit. In
another implementation, the memory 620 is a non-volatile memory
unit.
[0069] The storage device 630 is capable of providing mass storage
for the system 600. In one implementation, the storage device 630
is a computer-readable medium. In various different
implementations, the storage device 630 can include, for example, a
hard disk device, an optical disk device, a storage device that is
shared over a network by multiple computing devices (e.g., a cloud
storage device), or some other large capacity storage device.
[0070] The input/output device 640 provides input/output operations
for the system 600. In one implementation, the input/output device
640 can include one or more of a network interface devices, e.g.,
an Ethernet card, a serial communication device, e.g., and RS-232
port, and/or a wireless interface device, e.g., and 802.11 card. In
another implementation, the input/output device can include driver
devices configured to receive input data and send output data to
other input/output devices, e.g., keyboard, printer and display
devices 660. Other implementations, however, can also be used, such
as mobile computing devices, mobile communication devices, set-top
box television client devices, etc.
[0071] Although an example processing system has been described in
FIG. 6, implementations of the subject matter and the functional
operations described in this specification can be implemented in
other types of digital electronic circuitry, or in computer
software, firmware, or hardware, including the structures disclosed
in this specification and their structural equivalents, or in
combinations of one or more of them.
[0072] Embodiments of the subject matter and the operations
described in this specification can be implemented in digital
electronic circuitry, or in computer software, firmware, or
hardware, including the structures disclosed in this specification
and their structural equivalents, or in combinations of one or more
of them. Embodiments of the subject matter described in this
specification can be implemented as one or more computer programs,
i.e., one or more modules of computer program instructions, encoded
on computer storage medium for execution by, or to control the
operation of, data processing apparatus. Alternatively or in
addition, the program instructions can be encoded on an
artificially-generated propagated signal, e.g., a machine-generated
electrical, optical, or electromagnetic signal, that is generated
to encode information for transmission to suitable receiver
apparatus for execution by a data processing apparatus. A computer
storage medium can be, or be included in, a computer-readable
storage device, a computer-readable storage substrate, a random or
serial access memory array or device, or a combination of one or
more of them. Moreover, while a computer storage medium is not a
propagated signal, a computer storage medium can be a source or
destination of computer program instructions encoded in an
artificially-generated propagated signal. The computer storage
medium can also be, or be included in, one or more separate
physical components or media (e.g., multiple CDs, disks, or other
storage devices).
[0073] The operations described in this specification can be
implemented as operations performed by a data processing apparatus
on data stored on one or more computer-readable storage devices or
received from other sources.
[0074] The term "data processing apparatus" encompasses all kinds
of apparatus, devices, and machines for processing data, including
by way of example a programmable processor, a computer, a system on
a chip, or multiple ones, or combinations, of the foregoing The
apparatus can include special purpose logic circuitry, e.g., an
FPGA (field programmable gate array) or an ASIC
(application-specific integrated circuit). The apparatus can also
include, in addition to hardware, code that creates an execution
environment for the computer program in question, e.g., code that
constitutes processor firmware, a protocol stack, a database
management system, an operating system, a cross-platform runtime
environment, a virtual machine, or a combination of one or more of
them. The apparatus and execution environment can realize various
different computing model infrastructures, such as web services,
distributed computing and grid computing infrastructures.
[0075] A computer program (also known as a program, software,
software application, script, or code) can be written in any form
of programming language, including compiled or interpreted
languages, declarative or procedural languages, and it can be
deployed in any form, including as a stand-alone program or as a
module, component, subroutine, object, or other unit suitable for
use in a computing environment. A computer program may, but need
not, correspond to a file in a file system. A program can be stored
in a portion of a file that holds other programs or data (e.g., one
or more scripts stored in a markup language document), in a single
file dedicated to the program in question, or in multiple
coordinated files (e.g., files that store one or more modules,
sub-programs, or portions of code). A computer program can be
deployed to be executed on one computer or on multiple computers
that are located at one site or distributed across multiple sites
and interconnected by a communication network.
[0076] The processes and logic flows described in this
specification can be performed by one or more programmable
processors executing one or more computer programs to perform
actions by operating on input data and generating output. The
processes and logic flows can also be performed by, and apparatus
can also be implemented as, special purpose logic circuitry, e.g.,
an FPGA (field programmable gate array) or an ASIC
(application-specific integrated circuit).
[0077] Processors suitable for the execution of a computer program
include, by way of example, both general and special purpose
microprocessors, and any one or more processors of any kind of
digital computer. Generally, a processor will receive instructions
and data from a read-only memory or a random access memory or both.
The essential elements of a computer are a processor for performing
actions in accordance with instructions and one or more memory
devices for storing instructions and data. Generally, a computer
will also include, or be operatively coupled to receive data from
or transfer data to, or both, one or more mass storage devices for
storing data, e.g., magnetic, magneto-optical disks, or optical
disks. However, a computer need not have such devices. Moreover, a
computer can be embedded in another device, e.g., a mobile
telephone, a personal digital assistant (PDA), a mobile audio or
video player, a game console, a Global Positioning System (GPS)
receiver, or a portable storage device (e.g., a universal serial
bus (USB) flash drive), to name just a few. Devices suitable for
storing computer program instructions and data include all forms of
non-volatile memory, media and memory devices, including by way of
example semiconductor memory devices, e.g., EPROM, EEPROM, and
flash memory devices; magnetic disks, e.g., internal hard disks or
removable disks; magneto-optical disks; and CD-ROM and DVD-ROM
disks. The processor and the memory can be supplemented by, or
incorporated in, special purpose logic circuitry.
[0078] To provide for interaction with a user, embodiments of the
subject matter described in this specification can be implemented
on a computer having a display device, e.g., a CRT (cathode ray
tube) or LCD (liquid crystal display) monitor, for displaying
information to the user and a keyboard and a pointing device, e.g.,
a mouse or a trackball, by which the user can provide input to the
computer. Other kinds of devices can be used to provide for
interaction with a user as well; for example, feedback provided to
the user can be any form of sensory feedback, e.g., visual
feedback, auditory feedback, or tactile feedback; and input from
the user can be received in any form, including acoustic, speech,
or tactile input. In addition, a computer can interact with a user
by sending documents to and receiving documents from a device that
is used by the user; for example, by sending web pages to a web
browser on a user's client device in response to requests received
from the web browser.
[0079] Embodiments of the subject matter described in this
specification can be implemented in a computing system that
includes a back-end component, e.g., as a data server, or that
includes a middleware component, e.g., an application server, or
that includes a front-end component, e.g., a client computer having
a graphical user interface or a Web browser through which a user
can interact with an implementation of the subject matter described
in this specification, or any combination of one or more such
back-end, middleware, or front-end components. The components of
the system can be interconnected by any form or medium of digital
data communication, e.g., a communication network. Examples of
communication networks include a local area network ("LAN") and a
wide area network ("WAN"), an inter-network (e.g., the Internet),
and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).
[0080] The computing system can include clients and servers. A
client and server are generally remote from each other and
typically interact through a communication network.
The relationship of client and server arises by virtue of computer
programs running on the respective computers and having a
client-server relationship to each other. In some embodiments, a
server transmits data (e.g., an HTML page) to a client device
(e.g., for purposes of displaying data to and receiving user input
from a user interacting with the client device). Data generated at
the client device (e.g., a result of the user interaction) can be
received from the client device at the server.
[0081] While this specification contains many specific
implementation details, these should not be construed as
limitations on the scope of any inventions or of what may be
claimed, but rather as descriptions of features specific to
particular embodiments of particular inventions. Certain features
that are described in this specification in the context of separate
embodiments can also be implemented in combination in a single
embodiment. Conversely, various features that are described in the
context of a single embodiment can also be implemented in multiple
embodiments separately or in any suitable subcombination. Moreover,
although features may be described above as acting in certain
combinations and even initially claimed as such, one or more
features from a claimed combination can in some cases be excised
from the combination, and the claimed combination may be directed
to a subcombination or variation of a subcombination.
[0082] Similarly, while operations are depicted in the drawings in
a particular order, this should not be understood as requiring that
such operations be performed in the particular order shown or in
sequential order, or that all illustrated operations be performed,
to achieve desirable results. In certain circumstances,
multitasking and parallel processing may be advantageous. Moreover,
the separation of various system components in the embodiments
described above should not be understood as requiring such
separation in all embodiments, and it should be understood that the
described program components and systems can generally be
integrated together in a single software product or packaged into
multiple software products.
[0083] Thus, particular embodiments of the subject matter have been
described. Other embodiments are within the scope of the following
claims. In some cases, the actions recited in the claims can be
performed in a different order and still achieve desirable results.
In addition, the processes depicted in the accompanying figures do
not necessarily require the particular order shown, or sequential
order, to achieve desirable results. In certain implementations,
multitasking and parallel processing may be advantageous.
* * * * *