JP6971210B2 - 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 the search engine receives the search word input by the user, the information of the web page or application page related to the received search word (for example, URL: Uniform Resource Locator) is acquired from the web information database and acquired. Output a list of information as a search result.

Japanese Unexamined Patent Publication No. 2012-69171

Matthew D. Hoffman et al., “Online Learning for Latent Dirichlet Allocation”, [online], [Searched February 27, 2018], Internet <URL: https://papers.nips.cc/paper/3902-online-learning -for-latent-dirichlet-allocation.pdf ＞

When the search engine is operated to output the data related to the search word (hereinafter, also referred to as "query" or "search query") entered by the user together with the search result in order to improve the user satisfaction. There is. In order to achieve this, a database in which text data and entities are associated with each other and stored may be used.

In this database, when text data associated only with a specific entity is input as a query, that specific entity is naturally output together with the search result. On the other hand, when one text data is associated with a plurality of entities, information about an inappropriate entity not intended by the user may be output together with the search result. Also, if the query entered in one search request processing contains multiple words, it cannot be determined which word indicates the entity, and information about the inappropriate entity is output together with the search results. May be done.

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 search intention of a query input by a user.

One aspect of the present invention includes each of two or more words input as a query, a first index value calculation unit that calculates a first index value indicating the degree of association with an entity, and an entity that includes the word as a context. A second index value calculation unit that calculates a second index value indicating the degree of association with each of the words, a list generation unit that generates a list that defines context candidates for each entity, and the first index. The word of the word is based on the first index value calculated by the value calculation unit, the second index value calculated by the second index value calculation unit, and the list generated by the list generation unit. An information processing device including a occurrence probability calculation unit for calculating the occurrence probability of each combination of a word assumed as the name of the entity, the entity, and the word assumed as the context of the word. Is.

According to one aspect of the present invention, the search intention of the query input by the user 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 "baseball player A" which concerns on this embodiment. It is a figure which shows an example of the web page of "baseball team B" 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 collected by the collection part which concerns on this embodiment. It is a figure which shows another example of the related web page collected by the collection part which concerns on this embodiment. It is a figure which shows another example of the related web page collected by the collection part which concerns on this embodiment. It is a figure which shows another example of the web page of "baseball player A" which concerns on this embodiment. It is a figure which shows an example of the content information and the 1st probability which concerns on this embodiment. It is a figure explaining an example of the context collection process by the context collection part which concerns on this embodiment. It is a figure explaining another example of the context collection process by the context collection part which concerns on this embodiment. It is a figure which shows an example of the context information which concerns on this embodiment. It is a figure explaining an example of the calculation processing of the frequency information of a context by the 2nd probability calculation part which concerns on this embodiment. It is a flow chart which shows an example of the operation which calculates the 1st probability of the knowledge data server of this embodiment. It is a flow chart which shows an example of the operation which calculates the 2nd probability of the knowledge data server of this embodiment. It is a flow chart which shows an example of the detailed operation which calculates the 2nd probability of the knowledge data server of this embodiment. It is a flow diagram which shows an example of the operation which generates the white list of the knowledge data server of this embodiment. It is a figure which shows an example of the white list information of this embodiment. It is a flow diagram which shows an example of the operation which calculates the context grant rate of the knowledge data server of this embodiment. It is a figure which shows an example of the context grant rate information of this embodiment. It is a flow chart which shows an example of the online operation of the knowledge data server of this embodiment. It is a figure explaining an example of the online operation 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 using HTML documents and the like 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 is a server that uses a search engine to generate a web page for displaying search results and provides it to the terminal device 200.

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. For example, the knowledge graph (hereinafter referred to as knowledge graph D1), the content information D2, the context information D3, the white list information D4, and the content addition rate information D5 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 example knowledge graph D1 shown in FIG. 2, the entity E1 and the entity E2 belong to a class having a property name of "belonging team". In the present embodiment, each entity is associated with information that can identify each entity (hereinafter, "entity identification information EID"), an entity name, and location information of a 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 is a web page in which the entity identification information EID "0001", the entity name "baseball player A", and related data related to the entity are described. It is associated with the URL "http: // encyclopedia web page / baseball player A" which is the location information. Further, in the entity E2, the entity identification information EID "0002", the entity name "baseball team B", and the location information of the web page in which the related data related to the entity are described are described as "http: //". It is associated with the URL "Encyclopedia Web Page / Baseball Team B". 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 “baseball player A” according to the present embodiment. The web page of "baseball player A" contains data related to "baseball player A". Further, FIG. 4 is a diagram showing an example of a web page of “baseball team B” according to the present embodiment. The web page of "Baseball Team B" contains data related to "Baseball Team B".

FIG. 5 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. 5, the entity "baseball player A" is associated with the text data "3000 hits", and the entity "baseball team B" is referred to as "2017 championship". Text data is associated.

<1-2. Search result web page ＞
FIG. 6 is a diagram showing an example of a search result web page according to the present embodiment. As shown in FIG. 6, 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. For example, when the received query is included in the entity of the content information D2, the knowledge data server 100 determines that the entity is the entity associated with the received query. 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. 6) 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. 6, "3000 hits" is input as a query, and an image and various information related to "3000 hits" are displayed on the knowledge panel 220. On the knowledge panel 220, for example, an image 222 of "baseball player A" who achieved "3000 hits" and related information 223 such as the date of birth and birthplace of "baseball player A" 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.

<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 is, for example, a selection unit 111, a related page collection unit 112, a first probability calculation unit 113 (first index value calculation unit), a context collection unit 114, and a second probability calculation unit 115 (second probability calculation unit). Index value calculation unit, frequency information calculation unit), location information acquisition unit 116 (occurrence probability calculation unit), communication I / F (Interface) 117, white list generation unit 118, and context assignment rate calculation unit 119. Be prepared. 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 117 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”) for which the probability is to be calculated from the content information D2. Further, the selection unit 111 selects the entity to be whitelisted from the content information D2. The related page collecting 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 web page collected by the related page collection unit 112 is, for example, a web page of an encyclopedia that can be edited by a user (hereinafter, "encyclopedia web page") or a search result when the target text data is used as a query. It is a web page.

FIG. 7 is a diagram showing an example of an encyclopedia web page collected by the related page collection unit 112. In the example shown in FIG. 7, the related page collecting unit 112 describes the target text data of "3000 hits" associated with the entity of "baseball player A" indicated by the content information D2 in the encyclopedia web page. Collect related web pages. The related page collection unit 112 collects collection target data including HTML (HyperText Markup Language) data of an encyclopedia web page from a crawl target device DV via a network NW, for example. The related page collecting unit 112 is realized by a so-called crawler program. 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 related page collection unit 112 collects all the related web pages in which the target text data is described among the encyclopedia web pages, if any.

Further, FIG. 8 is a diagram showing an example of a search result web page collected by the related page collection unit 112. In the example shown in FIG. 8, in the search result web page, the query "3000 hits" (text TX21 shown) in which the entity "baseball player A" indicated by the content information D2 is displayed as the search result is input. , The search result when the search button 311 is clicked is shown. The "click" is an example of selection, and may be a touch, tap, or other mode. In the following description, it is collectively referred to as click. The query is an example of target text data.

Returning to FIG. 1, the first probability calculation unit 113 performs the probability calculation process for the set of the target text data and the entity based on the related web page collected by the related page collection unit 112. The first probability calculation unit 113 calculates a probability (P (entity | name)) (hereinafter, “first probability”) indicating the degree of association of each entity when the target text data (name of the entity) is given. calculate. The first probability calculation unit 113 calculates, for example, the value of P (e | s) represented by the equation (1) as the first probability. 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. The first probability is an example of the first index value.

Hereinafter, the details of the probability calculation process when the related web page is an encyclopedia web page will be described, and then the details of the probability calculation process when the related web page is a search result web page will be described.

<1-4. If the related web page is an encyclopedia web page>
Among the text data included in the encyclopedia web page, the first probability calculation unit 113 includes text data indicating an entity associated with the target text data as text indicating a link destination (hereinafter, “anchor text”). Calculate the probability. The related web page contains text data (illustrated text TX12) indicating the target text data (in this example, "3000 hits" (illustrated text TX11) and the entity associated with it (in this example, "baseball player A")). ~ 14) is included. Further, among the text TX12-14, the text TX12 is an anchor text. Further, the link of another web page given to the text TX12 is included in the target text data in the knowledge graph D1. The location information associated with the associated entity (in this example, "baseball player A").

Here, the first probability calculation unit 113 obtains the number of target texts in all the encyclopedia web pages including the target text data by the number of target text data which is the anchor text by the following formula (2). Calculate the probability of being generated (hereinafter, "anchor text probability"). As in equation (2) indicates the anchor text probability.

Further, the first probability 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, "" The probability of being a "specific web page") (hereinafter, "first entity probability") is calculated. The e in the equation (3) indicates the first entity probability.

When the related web page collected by the related page collecting unit 112 is only the web page shown in FIG. 7, the first probability calculation unit 113 uses the target text among the target text data included in all the related web pages. The probability that the data is anchor text (that is, the anchor text probability) is calculated as "1/3". This is because, in the target text data shown in the figure, only one link is given (even if two or more links are given, it may be counted as one). Further, the first probability 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".

<1-5. If the related web page is a search result web page>
The first probability calculation unit 113 calculates the probability that a link destination including an entity associated with the target text data included in the search result web page is selected as text. The target text data is "3000 hits" (text TX21 in the figure), and the entity associated with the target text data is "baseball player A". 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 shown in FIG. 8, 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 TX22 contains the target entity text data (in this example, "baseball player A"). The link destination given to the text TX 22 is, for example, the location information associated with the entity (“baseball player A” in this example) 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 first probability calculation unit 113 is the number of times the link destination including the target entity text data is clicked with respect to 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 a ratio (hereinafter, "entity text click probability"). As in the equation (2) indicates the entity text click probability.

In addition, the first probability 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.

<1-6. When the same entity pointing to different things is included>
Here, an entity may refer to different things even if they have the same wording. FIG. 9 is a diagram showing an example of linking to a web page of a different entity by the same wording. In the example shown in FIG. 9, the text TX22 indicates a link destination of a web page (for example, FIG. 3) of "baseball player A". On the other hand, the text TX26 indicates a link destination of a web page of a player who is a baseball player with the same surname and the same name as "baseball player A" and belongs to a baseball team C different from "baseball player A". The first probability calculation unit 113 also includes the click of the text TX26 in the entity click probability in the equation (2).

FIG. 10 is a diagram showing an example of a web page relating to another entity linked from the wording indicating the entity. The web page of "baseball player A" shown in FIG. 10 is a baseball player different from "baseball player A" ("baseball player A" shown in FIG. 3) associated with the text data "3000 hits". Related data related to a certain "baseball player A" is described.

When calculating the first entity probability, the first probability calculation unit 113 does not count the web page as the web page of the entity when the link destination is the web page shown in FIG. Further, even when the first probability calculation unit 113 calculates the second entity probability, if the link destination is the web page shown in FIG. 10, the web page is not counted as the web page of the entity.

<1-7. Weighting for each related web page>
Further, the first probability calculation unit 113 applies the following equation (4) to 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. You may further multiply. Further, the first probability calculation unit 113 has the following equation (4) for a 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. May be further multiplied. Then, the first probability calculation unit 113 calculates the first probability (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 first probability 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. , Multiply the ratio of the number of samples for which equations (2) and (3) are calculated using the search result web page to the value obtained by multiplying the entity text click probability and the second entity probability, and add both. Then, the score is weighted.

The first probability calculation unit 113 stores the calculated score in association with the content information D2. FIG. 11 is a diagram showing an example of the content information D2 and the score according to the present embodiment.

Returning to FIG. 1, the context collecting unit 114 collects the context from the knowledge graph D1 stored in the storage unit 120, the related web page collected by the related page collecting unit 112, and the like. A context is a character string indicating arbitrary information that characterizes an entity such as an entity's attributes and types. The context collecting unit 114 stores the collected context as the context information D3 in the storage unit 120.

FIG. 12 is a diagram illustrating an example of a context collection process by the context collection unit 114 according to the present embodiment. The context collecting unit 114 collects, for example, a character string indicating a property in the Knowledge Graph, a name of an adjacent entity, and the like as a context. In the example shown in FIG. 12, the context collecting unit 114 collects "Germany", "175 cm", "place of birth", "height" and the like as the context associated with the entity "scholar A". ..

FIG. 13 is a diagram illustrating another example of the context collection process by the context collection unit 114 according to the present embodiment. The context collecting unit 114 collects, for example, a character string indicating the attribute of the object displayed after the character string of the object (entity) in the encyclopedia web page as a context. In the example shown in FIG. 13, the context collecting unit 114 collects "physics" described after the character string of the object "scholar A" as a context. FIG. 14 is a diagram showing an example of the context information D3 according to the present embodiment. In the example shown in FIG. 14, the entity "scholar A" is associated with "physics", "Germany", "175 cm", "place of birth" and the like as contexts.

The second probability calculation unit 115 calculates a probability (P (context | entity)) (hereinafter, "second probability") indicating the degree of relevance to which each context is associated when a certain entity is given. The second probability calculation unit 115 generates a virtual document including frequency information of each context for each entity based on, for example, a click log of a related page, a search log of a search engine, and the like. Further, the second probability calculation unit 115 calculates the second probability by applying an algorithm (see Non-Patent Document 1) such as Latent Dirichlet Allocation (LDA) to the generated virtual document. That is, the second probability calculation unit 115 is based on the group information generated by the method of optimizing the feature amount when the entity and the word that can be the context are divided into virtual groups, and the given frequency information. Then, the second probability is calculated. The second probability is an example of the second index value.

FIG. 15 is a diagram illustrating an example of a context frequency information calculation process by the second probability calculation unit 115 according to the present embodiment. The second probability calculation unit 115 is the query used by the user who clicked the related page (for example, the encyclopedia web page) of the entity "Scholar A" to display the search result page which is the transition source. Based on the frequency of appearance of words (context terms) other than "Scholar A", the frequency information of each context is calculated for each entity. In the example shown in FIG. 15, for example, the frequency of "physics" having a high frequency of appearance is calculated as "120". On the other hand, the frequency of "height", which has a low appearance frequency, is calculated as "10". As shown in FIG. 14, the second probability calculation unit 115 stores the calculated frequency information in the storage unit 120 in association with the context information D3. Next, the second probability calculation unit 115 generates a virtual document by aggregating the contexts including these frequency information for each entity.

The second probability calculation unit 115 calculates the second probability by applying, for example, LDA to the generated virtual document. An entity corresponds to a virtual document, and the frequency information of the context for the entity corresponds to the phrase in the document.

Returning to FIG. 1, the location information acquisition unit 116 determines a set of entity names, entities, and contexts corresponding to the query received from the web server 300. The location information acquisition unit 116 acquires the location information associated with the determined entity based on the knowledge graph D1. After that, the location information acquisition unit 116 transmits the acquired location information to the web server 300.

The white list generation unit 118 generates a white list that defines candidate contexts that can be associated with an entity. The white list is defined for each entity. The white list generation unit 118 stores the generated white list in the storage unit 120 as the white list information D4.

The context grant rate calculation unit 119 calculates the context grant rate indicating the ease of attaching a context, which is a complementary expression to an entity. That is, the context grant rate indicates the degree to which a user inputs a query corresponding to an entity together with a further query (that is, a context which is a complementary expression). The context grant rate calculation unit 119 stores the calculated context grant rate for each entity in the storage unit 120 as the context grant rate information D5.

<1-8. First offline operation of Knowledge Data Server 100>
Hereinafter, the first offline operation of the knowledge data server 100 will be described. In the first offline operation, the first probability is calculated. FIG. 16 is a flow chart showing an example of an operation of calculating the first probability of the knowledge data server 100 of the present embodiment. The selection unit 111 selects the target text data to be calculated from the content information D2 (S110). Next, the related page collecting unit 112 collects related web pages (encyclopedia web page and search result web page) based on the target text data (S120). Next, the first probability calculation unit 113 calculates the anchor text probability and the entity text click probability (S130).

Next, the first probability calculation unit 113 calculates the first entity probability and the second entity probability (S140). Then, the first probability calculation unit 113 multiplies the anchor text probability based on the encyclopedia web page, the first entity probability, and the first content ratio, the entity text click probability based on the search result web page, and the second entity. The sum of the probability and the value obtained by multiplying the second content ratio is calculated as the first probability (S150), and the calculated first probability is stored in the storage unit 120 in association with the content information D2 (S160). This completes the processing of this flowchart.

<1-9. Second offline operation of Knowledge Data Server 100>
Hereinafter, the second offline operation of the knowledge data server 100 will be described. In the second offline operation, the second probability is calculated. FIG. 17 is a flow chart showing an example of an operation of calculating the second probability of the knowledge data server 100 of the present embodiment. The selection unit 111 selects the target text data to be calculated from the content information D2 (S210). The related page collection unit 112 collects related web pages based on the target text data. Next, the context collecting unit 114 collects the context based on the related web page collected by the related page collecting unit 112 (S220).

Next, the second probability calculation unit 115 calculates the frequency information of each context for each entity (S230), and generates a virtual document including the calculated frequency information. Next, the second probability calculation unit 115 calculates the second probability by applying an algorithm such as LDA to the generated virtual document (S240).

FIG. 18 is a flow chart showing an example of a detailed operation for calculating the second probability of the knowledge data server 100 of the present embodiment. In LDA, the characteristics of a sentence are analyzed by assuming that the document is composed of a plurality of topics (for example, 1000 topics) (groups). A large number of patterns of multiple topics (hereinafter referred to as "topic patterns") are defined. That is, one topic pattern includes a plurality of topics, and many topic patterns having different ways of dividing the plurality of topics are defined. In this embodiment, the entity corresponds to the document, and the frequency of the context corresponding to the entity corresponds to the frequency of the phrase in the document. First, the second probability calculation unit 115 selects one pattern from a large number of topic patterns (S241).

Next, the second probability calculation unit 115 selects a candidate for a set of the entity and the context to be calculated (S242). Next, the second probability calculation unit 115 calculates the second probability (P (context | entity)) (S243). For example, the second probability calculation unit 115 obtains the product of the probability that the context belongs to the topic and the probability that the topic belongs to the entity (by content) based on the following equation (5). The second probability calculation unit 115 learns the distribution so that the value of the product of the probabilities is maximized.

Next, the second probability calculation unit 115 determines whether or not the calculation of the second probability is completed for the candidates of the set of all entities and contexts (S244). When the second probability calculation unit 115 determines that the calculation of the second probability has not been completed for the candidates of the set of all the entities and the context, the set of the entity and the context that has not performed the calculation process of the second probability. Is selected, and the processing after S242 is repeated. On the other hand, when the second probability calculation unit 115 determines that the calculation of the second probability is completed for the candidates of the set of all entities and contexts, the second probability calculation unit 115 performs a process of adding the calculated second probabilities to the processing target. Calculate the score for the topic pattern.

Next, the second probability calculation unit 115 determines whether or not the processing for all the topic patterns is completed (S245). When the second probability calculation unit 115 determines that the processing for all the topic patterns is not completed, the second probability calculation unit 115 selects one topic pattern that has not been processed, and repeats the processing after S242.

On the other hand, when it is determined that the processing for all the topic patterns is completed, the second probability calculation unit 115 determines the topic pattern having the highest calculated score as the topic pattern to be adopted (S246).

Next, the second probability calculation unit 115 determines the second probability calculated for the topic pattern determined as described above as the second probability (S247). The second probability calculation unit 115 does not have to calculate the second probability for all the candidates of the set of the entity and the context. For example, if the candidate pair of entity and context contains an inappropriate expression, it may be excluded. In addition, the second probability calculation unit 115 may perform exclusion processing according to additional conditions. For example, the second probability calculation unit 115 may exclude a context that is not included in the summary text of the encyclopedia web page corresponding to the entity. The data may be excluded after the probability calculation is completed, instead of being excluded before the calculation of the second probability. This is because even an inappropriate expression is useful for calculating the probability in LDA. This completes the processing of this flowchart.

Returning to FIG. 17, the second probability calculation unit 115 stores the determined second probability in the storage unit 120 in association with the context information D3 (S250). The second probability calculation unit 115 may store the determined second probability in an inverted index such as Solr. By throwing an OR search query to this Solr, it is possible to acquire all the candidates with one inquiry without making inquiries many times. This completes the processing of this flowchart.

<1-10. Third offline operation of Knowledge Data Server 100>
Hereinafter, the third offline operation of the knowledge data server 100 will be described. In the third offline operation, a white list is generated. FIG. 19 is a flow chart showing an example of an operation of generating a white list of the knowledge data server 100 of the present embodiment. First, the context collecting unit 114 collects, for example, an attribute character string indicating an attribute of the object displayed after the character string of the object in the encyclopedia web page (S310). For example, the context collecting unit 114 collects the attribute character string of "physics" described after the character string of the object "scholar A" in the example shown in FIG. The context collecting unit 114 performs the above-mentioned character string collection processing for a plurality of pages (for example, all pages) included in the encyclopedia web page, and obtains a set of a plurality of attribute character strings.

Next, the selection unit 111 selects an entity (hereinafter, “target entity”) for which the white list is generated from the content information D2 (S320). Next, the white list generation unit 118 extracts one attribute character string from the plurality of attribute character strings collected by the context collection unit 114, and the extracted attribute character string is an encyclopedia in which the target entity is described. It is determined whether or not it is included in the web page (for example, the outline description of the encyclopedia web page) (S330). When the white list generation unit 118 determines that the extracted attribute character string is included in the encyclopedia web page in which the target entity is described, the white list generation unit 118 uses this attribute character string as a context candidate of the target entity (hereinafter, "first". Context candidate ”) (S340). On the other hand, if the white list generation unit 118 determines that the extracted attribute character string is not included in the encyclopedia web page in which the target entity is described, the white list generation unit 118 does not use this attribute character string as the first context candidate of the target entity. (S350). The white list generation unit 118 performs the above-mentioned processes S330 to S350 for all of the plurality of attribute character strings collected by the context collection unit 114.

Next, the context collecting unit 114 collects a character string indicating the property of the target entity in the Knowledge Graph, for example, a character string given to an edge included within one step of the target entity in the Knowledge Graph. The context collecting unit 114 may further collect the character string of the name of the adjacent entity of the target entity and the character string given to the edge included in two or more steps. The white list generation unit 118 sets the collected character string as a context candidate (hereinafter, “second context candidate”) of the target entity (S360). In the example shown in FIG. 12, the white list generation unit 118 sets character strings such as “place of birth” and “height” associated with the entity “scholar A” as the second context candidate.

Next, the white list generation unit 118 generates a white list based on the first context candidate and the second context candidate, and stores it in the storage unit 120 as the white list information D4 (S370). For example, the white list generation unit 118 sets the union of the first context candidate and the second context candidate as a white list. FIG. 20 is a diagram showing an example of the white list information D4 of the present embodiment. In the example shown in FIG. 20, "place of birth", "height", "physics", "Germany" and the like are associated with the entity "scholar A" as context candidates. The above processing is repeated for all the entities included in the content information D2. This completes the processing of this flowchart.

<1-11. Fourth offline operation of Knowledge Data Server 100>
Hereinafter, the fourth offline operation of the knowledge data server 100 will be described. In the fourth offline operation, the context grant rate for each entity is calculated. FIG. 21 is a flow chart showing an example of an operation of calculating the context granting rate of the knowledge data server 100 of the present embodiment. First, the context grant rate calculation unit 119 extracts, for example, one search log data to be processed from a plurality of search log data included in the search log of the search engine (S410). This one search log data corresponds to one search process by the user. The search log data includes information such as at least one query used for the search, the URL of the link clicked by the user on the search result web page (hereinafter, "transition destination URL"), and the search date and time.

Next, the context grant rate calculation unit 119 extracts at least one query used for the search and the transition destination URL from the extracted search log data (S420). Next, the context grant rate calculation unit 119 determines which of at least one query used for the search corresponds to the entity based on the extracted transition destination URL (S430). For example, the context grant rate calculation unit 119 determines which query corresponds to the entity by comparing the transition destination URL with the location information of the entity included in the knowledge graph D1. For example, if the context grant rate calculation unit 119 has an entity having location information matching the transition destination URL in the Knowledge Graph D1 and the name of this entity is included in the query, the context grant rate calculation unit 119 determines this query as an entity. do.

Next, the context grant rate calculation unit 119 determines whether or not a context, which is a complementary expression, is assigned to the query determined to be an entity (S440). That is, the context grant rate calculation unit 119 determines whether or not the query used for the search includes a query (second word) other than the query determined to be an entity. When it is determined that the context is given, the context grant rate calculation unit 119 increases the first counter for counting the number of search processes to which the context is given for each entity by one (S450). On the other hand, when the context grant rate calculation unit 119 determines that the context is not assigned (that is, when the number of queries used for the search is one), the search in which the context is not assigned for each entity. The second counter for counting the number of processes is incremented by one (S460). The first and second counters are provided for each entity.

Next, the context grant rate calculation unit 119 determines whether or not the processing for all the search log data included in the search log is completed (S470). When the context grant rate calculation unit 119 determines that the processing for all the search log data included in the search log has not been completed, the context grant rate calculation unit 119 extracts one other search log data to be processed and thereafter (S410). Repeat the process.

On the other hand, when the context grant rate calculation unit 119 determines that the processing for all the search log data included in the search log is completed, the context grant rate for each entity is calculated, and the context grant rate information D5 is stored in the storage unit 120. Remember (S480). For example, the context grant rate calculation unit 119 calculates the context grant rate for each entity based on the following equation (6).

In the above equation (6), the switch variable switches whether to calculate the probability that the context is given or the probability that the context is not given. The switch variable = 1 indicates that the probability that the context is given is calculated, and the switch variable = 0 indicates that the probability that the context is not given is calculated. FIG. 22 is a diagram showing an example of the context grant rate information D5 of the present embodiment. In the example shown in FIG. 22, the context grant rate “0.40” is associated with the entity “scholar A”. This completes the processing of this flowchart.

<1-12. Online operation of Knowledge Data Server 100>
FIG. 23 is a flow chart showing an example of online operation of the knowledge data server 100 of the present embodiment. First, the location information acquisition unit 116 receives a query from the web server 300 (S510). FIG. 24 is a diagram illustrating the online operation of the knowledge data server 100 of the present embodiment. FIG. 24 shows an example of receiving a query containing two words, "Scholar A" and "Physics".

Next, the location information acquisition unit 116 reads out the entity associated with the text data matching each of the words included in the received query in the content information D2 (S520). 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. FIG. 24 shows an example in which, in addition to the human entity EN1, the two-person entertainer entity EN2 and the racehorse entity EN3 are read out as the entity named “Scholar A”.

Next, the location information acquisition unit 116 uses the received query and the read entity as a candidate for a combination of the "entity name", the "entity", and the "context" (entity name, entity, Context) is generated in its entirety (S530). In the example shown in FIG. 24, the following six combinations of candidates are generated.
・ (Scholar A, Entity EN1, Physics)
・ (Physics, Entity EN1, Scholar A)
・ (Scholar A, Entity EN2, Physics)
・ (Physics, Entity EN2, Scholar A)
・ (Scholar A, Entity EN3, Physics)
・ (Physics, Entity EN3, Scholar A)

Next, the location information acquisition unit 116 calculates the value of the following evaluation formula (7) for each of the combination candidates (S540).

The above equation (7) specifies the first probability, the second probability, the context grant rate, and whether or not the context belongs to the white list for each combination (entity name, entity, context). The value multiplied by the value of the indicator function (I (white list (entity) inclusions (context))) defined by is obtained. I (white list (entity) inclusions (context)) is 1 when the context is included in the white list and 0 when the context is not included in the white list.

For example, in the case of a combination of (Scholar A, Entity EN1, Physics), the location information acquisition unit 116 obtains "Scholar A" as the name of the entity (text data) and the entity "Entity EN1" from the content information D2. Read the first probability associated with the set of. Further, the location information acquisition unit 116 reads out the second probability associated with the pair of the entity "entity EN1" and the "physics" as the context from the context information D3. Further, the location information acquisition unit 116 reads out the white list of the entity "entity EN1" from the white list information D4. Further, the location information acquisition unit 116 reads out the content text grant rate of the entity "entity EN1" from the context grant rate information D5. Then, the location information acquisition unit 116 obtains a multiplication value of the read first probability, the second probability, the context addition rate, and the value of the indicator function (I (white list (entity) inclusions (context))). ..

Next, the location information acquisition unit 116 determines the set of the entity name, the entity, and the context having the largest value among the calculated multiplication values as an appropriate set as the search intention of the query input by the user (S550). ). For example, in the example shown in FIG. 24, the location information acquisition unit 116 has a set in which the entity name is "scholar A", the entity is "person (entity EN1)", and the context is "physics". Determine as an appropriate set as the search intent of the query. The location information acquisition unit 116 acquires the location information of the entity included in the determined set from the knowledge graph D1 and transmits it to the web server 300. This completes the processing of this flowchart.

If there is only one query entered by the user, the location information acquisition unit 116 determines the associated entity based on the first probability when this one query is used as the entity name. You can do it. For example, the location information acquisition unit 116 reads the first probability associated with the text data matching this one query from the content information D2, and determines that the entity having the largest first probability is the associated entity. .. The value of the evaluation formula calculated as described above may be stored in the database and used for the display processing of the related keyword on the search web page. For example, the location information acquisition unit 116 was used to calculate the highest value among the values of the evaluation formula calculated using the entity name which is one query and the entity determined as described above. The "context" may be determined as a related keyword and output to the web server 300.

As described above, the knowledge data server 100 of the present embodiment is a first probability calculation unit that calculates a first index value indicating the degree of relevance between each of two or more words input as a query and an entity. A second probability calculation unit (second index value calculation unit) that calculates a second index value indicating the degree of association between 113 (first index value calculation unit), an entity that includes the word as a context, and each of the words. A list generation unit that generates a list that defines context candidates for each entity, the first index value calculated by the first index value calculation unit, and the second index value calculation unit. Based on the second index value and the list generated by the list generator, the word assumed as the name of the entity in the word, the entity, and the context of the word. An information processing device including a location information acquisition unit 116 (occurrence probability calculation unit) for calculating the occurrence probability of each combination with a assumed word. Thereby, the knowledge data server 100 of the present embodiment can appropriately evaluate the search intention of the query input by the user.

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 ... Related page collection unit 113 ... First probability calculation unit 114 ... Context collection unit 115 ... Second probability calculation unit 116 ... Location information acquisition unit 117 ... Communication I / F
118 ... White list generation unit 119 ... Context grant rate calculation 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 D3 ... Context information D4 ... White list information D5 ... Context grant rate information

Claims (9)

Each of the two or more words entered as a query, the first index value calculation unit that calculates the first index value indicating the degree of association with the entity, and
An entity that includes the word as a context, a second index value calculation unit that calculates a second index value indicating the degree of association with each of the words, and a second index value calculation unit.
A list generator that generates a list that defines context candidates for each entity,
Based on the first index value calculated by the first index value calculation unit, the second index value calculated by the second index value calculation unit, and the list generated by the list generation unit. , A occurrence probability calculation unit for calculating the occurrence probability of each combination of the word assumed as the name of the entity in the word and the combination of the entity and the word assumed as the context in the word. Information processing device to be equipped. The first index value is the probability that each of the words assumed as the name of the entity is associated with each of the plurality of the entities.
The second index value is the probability that each of the plurality of the entities is associated with each of the words assumed as the context.
The information processing apparatus according to claim 1. Further, a context grant rate calculation unit for calculating a context grant rate indicating the ease of attaching a context to the entity is provided.
The occurrence probability calculation unit further calculates the occurrence probability based on the context grant rate calculated by the context grant rate calculation unit.
The information processing apparatus according to claim 1 or 2. The occurrence probability calculation unit is defined by designating the first index value, the second index value, and whether or not the list includes the word assumed as the context for each combination. The multiplication value with the value of the function is calculated, and the combination having the largest calculated multiplication value is determined to be the combination of the word corresponding to the name of the entity, the entity, and the word corresponding to the context.
The information processing apparatus according to any one of claims 1 to 3. The occurrence probability calculation unit is defined by designating the first index value, the second index value, and whether or not the list includes the word assumed as the context for each combination. The multiplication value of the value of the function and the context grant rate is calculated, and the combination in which the calculated multiplication value is the largest is the word corresponding to the name of the entity, the entity, and the word corresponding to the context. Judged as a combination of
The information processing apparatus according to claim 3. The second index value calculation unit is based on group information generated by a method of optimizing features when entities and words that can be contexts are divided into virtual groups, and given frequency information. , Calculate the second index value,
The information processing apparatus according to any one of claims 1 to 5. Further provided with a frequency information calculation unit that calculates the frequency information based on the click log of the page associated with the entity.
The information processing apparatus according to claim 6. The computer
Calculate the first index value indicating the degree of association between each of the two or more words entered as a query and the entity.
A second index value indicating the degree of relevance between the entity including the word as a context and each of the words is calculated.
Generate a list that defines context candidates for each entity,
Based on the calculated first index value, the calculated second index value, and the generated list, the word assumed as the name of the entity among the words, the entity, and the word. Calculate the probability of occurrence of each of the words in combination with the word assumed as the context.
Information processing method. On the computer
Have each of the two or more words entered as a query calculate the first index value that indicates the degree of relevance to the entity.
A second index value indicating the degree of relevance between the entity including the word as a context and each of the words is calculated.
Generate a list that defines context candidates for each entity.
Based on the calculated first index value, the calculated second index value, and the generated list, the word assumed as the name of the entity among the words, the entity, and the word. To calculate the probability of occurrence of each of the above words in combination with the word assumed as the context.
program.

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