JP6971209B2 - Information processing equipment, information processing methods, and programs - Google Patents

Description

The present invention relates to an information processing apparatus, an information processing method, and a program.

Conventionally, a crawler that collects data (documents, images, etc.) from the Web and automatically creates a database of the collected data is known (see Patent Document 1). Crawlers collect data from web pages with various IP addresses by following links in web pages. The data collected by the crawler is stored in the web information database.

On the other hand, when a search engine receives a search word input by a user (hereinafter, also referred to as a "query"), the information on a web page or application page related to the received search word (for example, URL: Uniform Resource Locator) is sent to the web. It is acquired from the information database and the list of acquired information is output as a search result.

Japanese Unexamined Patent Publication No. 2012-69171

The search engine may be operated to output the data related to the query entered by the user together with the search result in order to improve the user satisfaction. In order to realize this, a database in which text data (hereinafter, also referred to as “keyword”) and an entity are associated with each other and stored may be used. Here, when the text data associated with a specific entity is input as a query in the database, the information about the specific entity is output together with the search result.

However, if the text data is not appropriate as information about the entity, inappropriate information may be output together with the search result. In addition, a method for evaluating the appropriateness of the association between the text data and the entity has been proposed, but this evaluation is not performed properly and the text data is appropriate as information about the entity. In some cases, information about the entity was not provided to the user.

The present invention has been made in consideration of such circumstances, and one of the objects of the present invention is to provide an information processing apparatus capable of appropriately evaluating the relationship between an entity and a keyword.

One aspect of the present invention is a first type of relationship between a selection unit that selects target text data among content information in which an entity and text data are associated with each other and the target text data selected by the selection unit. The first probability that the link destination to the first service including the entity associated with the target text data in the content information in the content information is selected in the collection unit that collects the web pages and the related web page of the first type. And, based on the second probability that the link destination to the second service including the entity associated with the target text data in the content information is selected as text, the entity associated with the target text data is evaluated. It is an information processing device including an evaluation unit.

According to one aspect of the present invention, the relationship between an entity and a keyword can be appropriately evaluated.

It is a figure which shows the use environment and the configuration of the knowledge data server which concerns on this embodiment. It is a figure which shows an example of the knowledge graph which concerns on this embodiment. It is a figure which shows an example of the web page of "Tokyo Tower" which concerns on this embodiment. It is a figure which shows an example of the content information which concerns on this embodiment. It is a figure which shows an example of the search result web page which concerns on this embodiment. It is a figure which shows an example of the related web page which concerns on this embodiment. It is a figure which shows another example of the related web page which concerns on this embodiment. It is a figure which shows an example of the content information and the total score which concerns on this embodiment. It is a flow chart which shows an example of the operation which calculates the total score of the knowledge data server of this embodiment. It is a flow chart which shows an example of the operation based on the total score of the knowledge data server of this embodiment.

Hereinafter, embodiments of the information processing apparatus, information processing method, and program of the present invention will be described with reference to the drawings. In the present embodiment, the information processing apparatus will be described as forming a part of the knowledge data server. The knowledge data server is, for example, a server that collects data to be collected (for example, image or text data) and generates a knowledge graph based on the collected data. In the present embodiment, the knowledge graph is data in which an entity, a class, and a property are associated with the location information of a web page in which related data related to the entity is described.

An entity may represent, for example, an entity of an object (eg, an object that exists in the real world) or a concept of an object (eg, defined in the real world or virtual world). It may represent a concept). For example, when the object is the concept of "building", the entity may represent an entity such as "○○ tower" or "○○ building". Further, for example, when the object is the concept of "economics", the entity may represent an insubstantial abstract concept such as "microeconomics" or "macroeconomics". Hereinafter, embodiments will be described in detail.

<Embodiment>
<1-1. Knowledge data server usage environment>
FIG. 1 is a diagram showing a usage environment and configuration of the knowledge data server 100 according to the present embodiment. The knowledge data server 100 is communicably connected to the terminal device 200, the web server 300, the crawl target device DV, and the network NW. The network NW means the World Wide Web, and is a system that uses HTML (HyperText Markup Language) documents that are standardly used on the Internet and intranets. The network NW may further include a radio base station, a provider device, a dedicated line, and the like.

The terminal device 200 is a device used by a user, and is, for example, a mobile phone such as a smartphone, a tablet computer, a notebook computer, a desktop computer, or the like. The web server 300 generates a web page for displaying the search result by using the search engine, and provides the terminal device 200 with the web page.

The knowledge data server 100 includes, for example, a control unit 110 and a storage unit 120. The storage unit 120 is realized by, for example, a RAM (Random Access Memory), an HDD (Hard Disk Drive), a flash memory, or a hybrid storage device in which a plurality of these are combined. Further, a part or all of the storage unit 120 may be an external device such as NAS (Network Attached Storage) or an external storage server that can be accessed by the knowledge data server 100. The knowledge graph D1 and the content information D2 are stored in the storage unit 120.

FIG. 2 is a diagram showing an example of Knowledge Graph D1 according to the present embodiment. The things described in Knowledge Graph D1 are defined by the ontology. An ontology is a definition of a class and a property of an object, and is a collection of constraints that hold between the class and the property.

A class is a group of things that have the same properties in an ontology. What the nature of an object is, that is, which class the object belongs to, is determined by the properties described below.

For example, things that have a beak, an oviparous vertebrate, and forelimbs that are wings are classified in the "bird" class. Also, in the class of "birds", things that have the property of not being able to fly are classified into lower classes such as "penguins" and "ostriches". In this way, the class system may have a hierarchical structure having a higher-lower relationship. The nature of the upper class is inherited by the lower class. In the example above, the "bird" class's "beak-bearing, oviparous vertebrate with winged forelimbs" property is a lower class property of "penguins" and "ostriches". Will also be included. For example, if the class name for identifying the class is a class "bird", the class name may be represented by the character string "bird". The class name does not necessarily have to represent a meaning, and for example, even in the class "bird", a character string indicating mere identification information such as "information 1" or "C1" may be assigned. .. The above-mentioned entities, or things, shall belong to any of the classes contained within the class system defined by the ontology.

Properties are attributes that describe the nature and characteristics of things and the relationships between classes. For example, a property may be an attribute that has the property of "having ~ as a component of the body" or the property of "living in ...", or "a class is a higher class and a certain class is a lower class". It may be an attribute indicating the relationship between the upper and lower levels of "class". As for the property name for identifying the property, the property name itself may or may not represent the meaning, as in the class name described above.

The Knowledge Graph D1 is a directed graph in which the above-mentioned classes are represented as nodes and the above-mentioned properties are represented as labeled and directional edges. With such a graph structure, information about things can be discriminated by nodes, and relationships between things can be discriminated by edges.

In the knowledge graph D1 shown in FIG. 2, the entity E1 and the entity E2 belong to the class of the property name "location". In the present embodiment, each entity is associated with information that can identify each entity (hereinafter referred to as "entity identification information EID"), the entity name, and the location information of the web page of the entity. The location information is information for specifying a position on the Web, and is, for example, a URL. The web page indicated by the location information contains the relevant data of the entity.

In the knowledge graph D1 shown in FIG. 2, the entity E1 has the entity identification information EID "0001", the entity name "Tokyo Tower", and the location information of the web page in which the related data related to the entity is described. Is associated with the URLs "http: // encyclopedia web page / Tokyo Tower" and "http: // regional information web page / Tokyo Tower". Further, in the entity E2, the entity identification information EID "0002", the entity name "Tokyo", and the location information of the web page in which the related data related to this entity are described are described as "http: // encyclopedia". It is associated with the URL "Encyclopedia Web Page / Tokyo". In the following description, the related data related to the entity will be described as the related data of the entity. In addition, the web page in which the data related to the entity is described is also described as the web page of the entity.

FIG. 3 is a diagram showing an example of a web page of “Tokyo Tower” according to the present embodiment. The "Tokyo Tower" web page contains data related to "Tokyo Tower".

FIG. 4 is a diagram showing an example of the content information D2 according to the present embodiment. The content information D2 is information in which an entity and text data are associated with each other. The content information D2 is generated based on the collection result of collecting the related data contained in the web page or the search log of the search engine. The content information D2 is, for example, information in which text data included in a web page of an entity is collected and the collected text data is associated with the entity. Further, the content information D2 is, for example, information in which, when an entity is input as a query, the text data input together with the entity is associated with the entity. In the example content information D2 shown in FIG. 4, the entity "Tokyo Tower" is associated with the text data "Japan Radio Tower", and the entity "Tokyo" is associated with the text data "Capital of Japan". Is associated.

<1-2. Search result web page ＞
FIG. 5 is a diagram showing an example of a search result web page according to the present embodiment. As shown in FIG. 5, the display unit 210 of the terminal device 200 displays a search result web page including a query input area 211, a knowledge panel 220, and a search result 230. The knowledge panel 220 includes the entity 221 corresponding to the query, the image 222 associated with the entity 221 and the relevant information 223 associated with the entity 221. The user uses the terminal device 200 to input a query on the search page displayed on the display unit 210 of the terminal device 200. A query is a search word or a combination of multiple search words. The terminal device 200 sends the query entered by the user to the web server 300.

The web server 300 transmits the query received from the terminal device 200 to the knowledge data server 100. The knowledge data server 100 sends information related to the entity corresponding to the received query to the web server 300. The knowledge data server 100 determines an entity associated with a received query (text data), for example, based on the content information D2. Further, the knowledge data server 100 transmits the location information associated with the determined entity to the web server 300 based on the knowledge graph D1. Based on the received location information, the web server 300 extracts the relevant data of the entity from the web pages indicated by the location information. The web server 300 generates a search result web page (eg, FIG. 5) that includes relevant data of the extracted entity (in this example, the information shown in the knowledge panel 220).

In the example shown in FIG. 5, "Japan Radio Tower" is input as a query, and images and various information related to "Japan Radio Tower" are displayed on the knowledge panel 220. On the knowledge panel 220, for example, an image 222 of "Tokyo Tower" indicating "Japan Radio Tower" and related information 223 such as the height and location of "Tokyo Tower" are displayed. In this way, by displaying not only the search result 230 but also the knowledge panel 220 on the display unit 210, the user satisfaction with the search result can be improved.

In order to provide the knowledge panel 220 as described above, the amount of information included in the knowledge graph D1 and the content information D2 can be enriched, and information on an appropriate entity that can respond to various queries can be provided. It is necessary to do so. Therefore, the knowledge data server 100 of the present embodiment derives an index (hereinafter, “score”) indicating whether the text data associated with the entity is appropriate based on information on the usage status of a plurality of services. Hereinafter, the detailed configuration of the knowledge data server 100 will be described.

<1-3. Configuration of Knowledge Data Server 100>
Returning to FIG. 1, the control unit 110 is realized by, for example, a hardware processor such as a CPU (Central Processing Unit) executing a program (software). The control unit 110 includes, for example, a selection unit 111, a collection unit 112, a score calculation unit 113, a location information acquisition unit 114, and a communication I / F (Interface) 115. In addition, some or all of these components (excluding the contained storage unit) are LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), GPU (Graphics Processing). It may be realized by hardware (circuit unit; including circuitry) such as Unit), or it may be realized by the cooperation of software and hardware. The communication I / F 115 mediates communication between each functional unit via the network NW and other devices (for example, a crawl target device (hereinafter, crawl target device DV), a terminal device 200, and a web server 300). ..

The selection unit 111 selects arbitrary text data (hereinafter, “target text data”) to be scored from the content information D2. The collection unit 112 collects a web page (hereinafter, “related web page”) related to the target text data selected by the selection unit 111 via the network NW. The collection unit 112 uses, for example, an encyclopedia web page that can be edited by a user, a search result web page when the target text data is used as a query, and a click log of a link included in the search result web page. Gather information about. Hereinafter, the encyclopedia web page that can be edited by the user is referred to as an encyclopedia web page.

Here, the encyclopedia web page is a page provided by the first service (hereinafter, encyclopedia providing service). The encyclopedia web page is an example of a second type of related web page. The search result web page is an example of the first type of related web page. The links included in the search result web page include links to the encyclopedia web page provided by the first service, as well as links to the web page provided by the second service different from the first service. Is included. The second service provides, for example, regional information such as landmark information, store information, tourist destination information, accommodation facility information, movie theater information, hospital / clinic information, beauty salon information, parking lot information, and rental car information. A service or a service that sells goods or services such as books to users. In the following, an example in which the second service is a regional information providing service that provides regional information such as landmark information will be described. In addition, the web page provided by the regional information provision service is referred to as a regional information web page.

FIG. 6 is a diagram showing an example of an encyclopedia web page collected by the collection unit 112. In the example shown in FIG. 6, the collecting unit 112 describes the related web page of the encyclopedia web page in which the target text data "Japan Radio Tower" associated with the entity of "Tokyo Tower" indicated by the content information D2 is described. Collect pages. The collection unit 112 collects the collection target data including the HTML data of the encyclopedia web page from the crawl target device DV via the network NW, for example. The collecting unit 112 is realized by a so-called crawler program. Here, the data to be collected is data that is on the network NW (in the storage area of the device DV to be crawled) and can be viewed by a browser. The data to be collected is not limited to the browser, and may be data reproduced by the application program. The data to be collected is, for example, a related web page containing text data indicating the source of HTML. The collection unit 112 collects all the related web pages in which the target text data is described among the encyclopedia web pages.

Further, FIG. 7 is a diagram showing an example of a search result web page collected by the collection unit 112. In the example shown in FIG. 7, in the search result web page, a query (illustrated text TX21) in which the entity "Tokyo Tower" indicated by the content information D2 is displayed as a search result is input. Shows the search results for the case. The query is an example of target text data.

Returning to FIG. 1, the score calculation unit 113 calculates the score (evaluation result) of the set of the target text data and the entity based on the related web page collected by the collection unit 112. The collecting unit 112 calculates, for example, the value of P (e | s) represented by the equation (1) as a score. Various elements in the formula (1) will be described for each of the formulas (2) and (3) described later and the types of related web pages.

Hereinafter, the details of the score calculation process by the score calculation unit 113 will be described.

<1-4. Score calculation for the first service (encyclopedia service)>
<If the related web page is an encyclopedia web page>
The score calculation unit 113 calculates the probability that the text data indicating the entity associated with the target text data is included as the text (anchor text) indicating the link destination among the text data included in the encyclopedia web page. The related web page contains text data (Fig. 6) indicating the target text data (in this example, "Japan Radio Tower" (text TX11 shown in FIG. 6) and the entity associated with it (in this example, "Tokyo Tower")). The text TX12-14) shown in the text TX12-14), the text TX12 is an anchor text, and the links of other web pages attached to the text TX12 are in the knowledge graph D1. , The location information associated with the entity associated with the target text data (in this example, "Tokyo Tower").

Here, the score calculation unit 113 uses the following equation (2) as an anchor text for the number of text data indicating the entity associated with the target text data in all the encyclopedia web pages including the target text data. The probability obtained by the number of text data indicating a certain entity (hereinafter, "anchor text probability") is calculated. _{A s} in equation (2) shows the anchor text probability. Here, the anchor text probability is an example of the third probability.

Further, the score calculation unit 113 uses the following formula (3) to indicate that the link destination of the anchor text is the web page of the entity corresponding to the location information associated with the target text data in the Knowledge Graph D1 (hereinafter, "specific". Web page ") is calculated (hereinafter," first entity probability "). The e in the equation (3) indicates the first entity probability. Here, the first entity probability is an example of the fourth probability.

When the related web page collected by the collecting unit 112 is only the web page shown in FIG. 6, the score calculation unit 113 uses the target text data as an anchor text among the target text data included in all the related web pages. A certain probability (that is, anchor text probability) is calculated as "1/3". This is because only one link is added to the illustrated target text data. Further, the score calculation unit 113 calculates the probability (first entity probability) that the link destination of the anchor text is a specific web page of the entity associated with the target text data as "1".

<If the related web page is a search result web page>
The score calculation unit 113 calculates the probability that the link destination including the entity associated with the target text data included in the search result web page is selected as the text. The target text data is "Japan Radio Tower" (text TX21 in the figure), and the entity associated with the target text data is "Tokyo Tower". In the following description, the text related to the entity associated with the target text data will be referred to as the target entity text data. In the example of FIG. 7, the related web page includes text data (texts TX22 to 24 shown) to which a link of the related web page of the target text data is added. Here, the link destination given to the text TX22 is, for example, the location information associated with the entity (in this example, “Tokyo Tower”) associated with the target text data in the Knowledge Graph D1. That is, the link of another web page of the text TX22 is a specific web page, for example, an encyclopedia web page. It should be noted that the search result web page may include text TX25, which is text without a link and indicates an entity.

The score calculation unit 113 clicks the link destination related to the encyclopedia web page including the target entity text data for the number of times each link destination included in all the search result web pages is clicked by the formula (2). Calculate the probability obtained as the ratio of the number of times (hereinafter, entity text click probability). The "click" is an example of selection, and may be selected by touch, tap, or other modes. In the following description, it is collectively referred to as click. A _s in equation (2) shows the entity text click probability. Here, the entity text click probability is an example of the first probability.

Further, the score calculation unit 113 calculates the probability that the link destination including the target entity text data is a specific web page related to the entity (hereinafter, “second entity probability”) by the equation (3). E in the equation (3) indicates the second entity probability. Here, the second entity probability is another example of the fourth probability.

<Weighting for each related web page>
Further, the score calculation unit 113 further multiplies the value obtained by multiplying the anchor text probability calculated when the related web page is the encyclopedia web page by the first entity probability by the following equation (4). You may. Further, the score calculation unit 113 further applies the following equation (4) to the value obtained by multiplying the entity text click probability calculated when the related web page is the search result web page by the second entity probability. You may multiply. Then, the score calculation unit 113 calculates a score (first score) by, for example, adding both to obtain a logarithm.

Equation (4) is the number of samples for which equations (2) and (3) are calculated using the encyclopedia web page, and the number of samples for which equations (2) and (3) are calculated using the search result web page. The ratio with and is shown.

The score calculation unit 113 further multiplies the ratio of the number of samples for which equations (2) and (3) are calculated using the encyclopedia web page by the value obtained by multiplying the anchor text probability and the first entity probability, and searches. By further multiplying the ratio of the number of samples calculated in equations (2) and (3) using the result web page by the value obtained by multiplying the entity text click probability and the second entity probability, and adding both. Weight the score.

<1-5. Score calculation for the second service (regional information provision service)>
<If the related web page is a search result web page>
The score calculation unit 113 calculates the probability that the link destination including the entity associated with the target text data included in the search result web page is selected as the text. The target text data is "Japan Radio Tower" (text TX21 in the figure), and the entity associated with the target text data is "Tokyo Tower". In the example of FIG. 7, the related web page includes text data (texts TX22 to 24 shown) to which a link of the related web page of the target text data is added. Of these, the text TX24 includes the target entity text data related to the regional information providing service (“Tokyo Tower” in this example). The link destination given to the text TX24 is, for example, the location information associated with the entity (“Tokyo Tower” in this example) associated with the target text data in the Knowledge Graph D1. That is, the link of the other web page of the text TX24 is a specific web page, for example, a regional information web page.

According to the above formula (2), the score calculation unit 113 sets the link destination related to the regional information providing service including the target entity text data to the number of times each link destination included in all the search result web pages is clicked. The probability obtained as the ratio of the number of clicks (hereinafter, "entity text click probability") is calculated. Here, the entity text click probability is an example of the second probability.

Further, the score calculation unit 113 calculates a score (second score) based on the entity text click probability calculated when the related web page is a search result web page.

<1-6. Total score calculation>
The score calculation unit 113 totals the total score by adding the first score calculated for the first service (encyclopedia providing service) and the second score calculated for the second service (regional information providing service). It is calculated, and the calculated total score is stored in association with the content information D2. FIG. 8 is a diagram showing an example of the content information D2 and the score according to the present embodiment.

Returning to FIG. 1, the location information acquisition unit 114 refers to the content information D2 stored in the storage unit 120, and acquires an entity corresponding to the query received from the web server 300. When the text data matching the received query is associated with a score equal to or higher than a predetermined threshold value, the location information acquisition unit 114 determines the entity associated with the text data as the entity corresponding to the query. The location information acquisition unit 114 acquires the location information associated with the determined entity based on the knowledge graph D1. After that, the location information acquisition unit 114 transmits the acquired location information to the web server 300. The location information acquisition unit 114 is an example of a determination unit.

In this way, when the location information acquisition unit 114 receives the query from the web server 300, the location information acquisition unit 114 identifies the text data corresponding to the query. At this time, not only when the query and the text data are exactly the same, but also when there is a partial match or a difference to the extent that they are considered to be semantically the same, it may be regarded as the corresponding text data.

<1-7. Operation of Knowledge Data Server 100>
Hereinafter, the operation of the knowledge data server 100 will be described. FIG. 9 is a flow chart showing an example of an operation of calculating the total score of the knowledge data server 100 of the present embodiment. The selection unit 111 selects the target text data to be scored from the content information D2 (S100). Next, the collection unit 112 collects related web pages (encyclopedia web page and search result web page) based on the target text data (S102). Next, the score calculation unit 113 calculates the first score for the first service (encyclopedia providing service) (S104).

Next, the score calculation unit 113 calculates the second score for the second service (regional information providing service) (S106). Next, the score calculation unit 113 calculates the total score by adding the calculated first score and the second score (S108), associates the calculated total score with the content information D2, and stores it in the storage unit 120. Let me. This completes the processing of this flowchart.

FIG. 10 is a flow chart showing an example of an operation based on the score of the knowledge data server 100 of the present embodiment. The location information acquisition unit 114 receives a query from the web server 300 (S200). Next, the location information acquisition unit 114 reads out the total score associated with the text data matching the query in the content information D2 (S202). Next, the location information acquisition unit 114 determines whether the total score is equal to or higher than a predetermined threshold value (S204). When the total score is equal to or higher than a predetermined threshold value, the location information acquisition unit 114 transmits (outputs) the location information of the entity associated with the text data in the knowledge graph D1 to the web server 300 (S206). Further, the location information acquisition unit 114 does not transmit (output) the location information to the web server 300 when the total score is less than a predetermined threshold value (S208). This completes the processing of this flowchart.

As described above, the knowledge data server 100 of the present embodiment has a selection unit 111 for selecting target text data and a target selected by the selection unit 111 among the content information in which the entity and the text data are associated with each other. A link to a collection unit 112 that collects the first type of related web pages related to text data and a first service that includes an entity associated with the target text data in the content information as text in the first type of related web pages. It is associated with the target text data based on the first probability that the destination is selected and the second probability that the link destination to the second service that includes the entity associated with the target text data in the content information as text is selected. It includes an evaluation unit (score calculation unit 113) that evaluates an entity. Thereby, the knowledge data server 100 of the present embodiment can appropriately evaluate the relationship between the entity and the keyword.

In this embodiment, the relationship between the entity and the keyword is evaluated based on the information regarding the usage information of a plurality of services. Therefore, for example, even if the information about the entity is not posted in the first service (encyclopedia information providing service) and the information about the usage information of the first service cannot be obtained, the second service If information on the usage information of (regional information provision service) can be obtained, the relationship between the entity and the keyword can be appropriately evaluated.

In the above embodiment, the entity and the keyword are used based on the information on the usage information of the two services, the first service (encyclopedia information providing service) and the second service (regional information providing service). An example of evaluating the relationship between However, the relationship between the entity and the keyword may be evaluated based on the information regarding the usage information of three or more services.

Although the embodiments for carrying out the present invention have been described above using the embodiments, the present invention is not limited to these embodiments, and various modifications and substitutions are made without departing from the gist of the present invention. Can be added.

100 ... Knowledge data server 110 ... Control unit 111 ... Selection unit 112 ... Collection unit 113 ... Score calculation unit 114 ... Location information acquisition unit 120 ... Storage unit 200 ... Terminal device 210 ... Display unit 211 ... Query input area 220 ... Knowledge panel 221 ... Entity 222 ... Image 223 ... Related information 230 ... Search result 300 ... Web server D1 ... Knowledge graph D2 ... Content information

Claims (9)

A selection unit that selects the text data to be the target text data from the content information including the entity and the text data associated with the entity.
A collection unit that collects the first type of related web pages related to the target text data selected by the selection unit, and a collection unit.
In the first type of related web page,
A first probability that links to the first service is selected, the anchor text indicating the destination to the first service includes a text indicating the target text data and associated entity in the content information , The first probability and
A second probability that links to the second service is selected, the anchor text indicating the destination to the second service includes text indicating the target text data and associated entity in the content information , Based on the second probability
An evaluation unit that evaluates the entity associated with the target text data,
Information processing device equipped with. The collection unit collects a second type of related web page related to the target text data selected by the selection unit.
The evaluation unit performs the evaluation based on the third probability that the text data indicating the entity associated with the target text data is the anchor text in the second type of related web page collected by the collection unit. conduct,
The information processing apparatus according to claim 1. The evaluation unit is based on a fourth probability that the web page linked to the anchor text in the second type of related web page is the web page of the entity associated with the target text data in the content information. Perform the above evaluation,
The information processing apparatus according to claim 2. The first type of related web page is a web page that displays search results when the target text data is used as a query.
The information processing apparatus according to any one of claims 1 to 3. The second type of related web page is an encyclopedia web page that can be edited by the user.
The information processing apparatus according to claim 2 or 3. The evaluation unit adds the evaluation result of the entity to the content information in association with the entity.
The information processing device is
Further, a determination unit for determining whether or not to output information about the entity by referring to the evaluation result for the entity associated with the text data matching the query in the content information for the input query. Prepare, prepare
The information processing apparatus according to any one of claims 1 to 5. The evaluation unit calculates the score of the entity corresponding to the target text data by adding the first score obtained based on the first probability and the second score obtained based on the second probability. death,
When the score is equal to or higher than a predetermined threshold value, the determination unit outputs information on the entity associated with the score, and when the score is less than the predetermined threshold value, outputs information on the entity associated with the score. Decide not to
The information processing apparatus according to claim 6. The computer
The text data to be the target text data is selected from the content information including the entity and the text data associated with the entity.
Collect the first type of related web pages related to the selected target text data and
In the first type of related web page,
A first probability that links to the first service is selected, the anchor text indicating the destination to the first service includes a text indicating the target text data and associated entity in the content information , The first probability and
A second probability that links to the second service is selected, the anchor text indicating the destination to the second service includes text indicating the target text data and associated entity in the content information , Based on the second probability
Evaluate the entity associated with the target text data,
Information processing method. On the computer
The text data to be the target text data is selected from the content information including the entity and the text data associated with the entity.
Have the selected first type of related web pages related to the target text data collected.
In the first type of related web page,
A first probability that links to the first service is selected, the anchor text indicating the destination to the first service includes a text indicating the target text data and associated entity in the content information , The first probability and
A second probability that links to the second service is selected, the anchor text indicating the destination to the second service includes text indicating the target text data and associated entity in the content information , Based on the second probability
To evaluate the entity associated with the target text data,
program.

Images