JP2021163316A - Search program, search method, and search device - Google Patents

Abstract

To output proper multiple facets from a set of facets associated with a category.SOLUTION: The search device receives a specification of a specific category and outputs multiple facets 210 while arranging in order according to the priority of the multiple calculated facets on the basis of at least either one of a first index based on the number of categories in which each of the multiple facets associated with the specific category in the knowledge graph is associated with the knowledge graph and a second index based on the distance between the specific category and each of the multiple facets in the knowledge graph.SELECTED DRAWING: Figure 3

Description

The present invention relates to a search program, a search method, and a search device.

The Knowledge Graph (KG) is known as an example of a knowledge base (KB) that collects information from various information sources.

The KG may be used, for example, for a faceted search for the entire data stored in the KG. The facet search is a search method that enables the user to narrow down the contents in the KG by selecting the search conditions prepared by the data search system.

Japanese Patent Publication No. 2011-513819 Special Table 2005-514673

Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa, "Automation of facet extraction for facet search for D-022 XML data", 13th Information Science and Technology Forum (FIT2014), Volume 2, pp. 133-134, 2014 Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa, "Proposal of FoX Framework for Faceted Navigation for XML Data", 1st Forum on Data Engineering and Information Management (DEIM), B7-6, 2009

In the facet search, when the KG data is narrowed down to a specific category and the search is performed, the narrowing down by an appropriate key (facet key) may not be performed.

For example, in the case of the category of professional baseball players, it is possible to search for facets according to the user's knowledge by narrowing down by appropriate facet keys such as at-bats, dominant arm, and experience of participating in Koshien, which are highly related to the category.

However, in the conventional facet search system, even when the search is narrowed down to a specific category, it is narrowed down by an unimportant or inappropriate facet key such as date of birth, place of origin, company type, etc. May be done.

In this way, if the narrowing down by an appropriate facet key is not performed, there is a possibility that the turn to reach the target data of the user in the facet search increases.

In one aspect, one of the objects of the present invention is to output a plurality of appropriate facets from a set of facets associated with a category.

In one aspect, the search program may cause the computer to perform the following operations: The process may accept the designation of a particular category. In addition, the processing includes a first index based on the number of categories in which each of the plurality of facets associated with the specific category in the knowledge graph is associated with the knowledge graph, and the specific category in the knowledge graph. The plurality of facets may be output in order according to the second index based on the distance to each of the plurality of facets and the priority of the plurality of facets calculated based on at least one of them.

In one aspect, the invention can output the appropriate set of facets from a set of facets associated with a category.

It is a figure which shows the expression example of the graph format which is an example of the description method of RDF (Resource Description Framework). It is a figure which exemplifies the narrowing-down target by a category and the facet for each category in KG data. It is a figure which shows the screen display example of the UI (User Interface) of a facet search system. It is a block diagram which shows the functional structure example of the facet search system which concerns on one Embodiment. It is a block diagram which shows the hardware configuration example of the computer which realizes a server function. It is a figure which shows an example of the frequency table creation process by a frequency table creation part. It is a figure which shows an example of a class set, a P frequency table, and a PO frequency table. It is a figure which shows an example of the calculation formula of each index. It is a figure which shows an example of an index value. It is a figure which shows an example of the category importance table which expressed the category importance in tabular form. It is a figure which shows an example of the facet score table which represented the facet score in a tabular form. It is a figure which shows an example of the graph which shows the class and facet key in RDF Schema. It is a figure which shows an example of the graph which shows the class and facet key in RDF Schema. It is a figure which illustrates the score (org_score), facet score (new_score), score (ont_score), and final score (final_score) in a table format. It is a figure which shows an example of the facet key displayed in the item list area. It is a figure which shows the comparative example of MRR (Mean Reciprocal Rank) when the facet key is sorted based on each of "org_score" and "new_score". It is a figure which shows the comparative example of MRR when the facet key is sorted based on each of "new_score" and "final_score". It is a flowchart explaining the operation example of the DB (Database) creation process which concerns on one Embodiment. It is a flowchart explaining the operation example of the frequency table creation process of step S1 of FIG. It is a flowchart explaining the operation example of the score DB creation process of step S2 of FIG. It is a flowchart explaining the operation example of the facet search process which concerns on one Embodiment.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. However, the embodiments described below are merely examples, and there is no intention of excluding the application of various modifications or techniques not specified below. For example, the present embodiment can be variously modified and implemented without departing from the spirit of the present embodiment. In the drawings used in the following description, the parts having the same reference numerals represent the same or similar parts unless otherwise specified.

[1] Embodiment [1-1] Description of facet search system First, a facet search system will be briefly described. The facet search system is, for example, a system for performing a facet search on a large-scale Knowledge Graph (KG).

The facet search system according to one embodiment is made more efficient by, for example, instead of performing a facet search on the entire data to be searched in the KG, narrowing down candidates by category and then performing a facet search within the category. To realize.

The data stored in the KG is expressed by a description method called RDF (Resource Description Framework) in which three elements of a subject (Subject), a predicate (Predicate), and an object (Object) are set as one set.

In facet search using KG, a category is a class of instances. The facet key is a "predicate" and the facet value is an "object". By facet search, the facet search system searches for an instance that matches the search condition, for example, a set of "subjects".

Here, the "instance" represents an event or thing in the world, and is also called an entity. For example, the baseball player "Taro Yamada" is an example of an instance, and may be represented by a character string such as "Taro Yamada" or by an ID in a DB (Database).

An example of a DB is various DBs that store data (open data) and publish it on the Internet, for example, a DB that uses LOD (Linked Open Data) technology.

The ID in the DB is the URI (Uniform Resource Indicator) of the place where the information of "Taro Yamada" can be referred to in the DB, for example, the URL (Uniform Resource Locator) of the web page containing the article of "Taro Yamada". Can be mentioned. As an example, when the domain of the DB is "aaa.org", the ID in the DB of "Taro Yamada" is "http://aaa.org/resource/Taro_Yamada".

In one embodiment, the KG may be capable of setting one or more IDs in the DB as an instance. In other words, KG is a knowledge base that collects information from one or more DBs as an information source.

The "class" represents the type of instance. For example, the class of "Taro Yamada" is a "baseball player". Instances may belong to multiple classes, where "Taro Yamada" is a "baseball player" "http://aaa.org/ontology/BaseballPlayer" and "athlete" "http://aaa.org/" It is "ontology / Athlete" and "person" "http://aaa.org/ontology/Person".

Classes can maintain superior and inferior relationships. For example, using RDF Schema, "BaseballPlayer" and "Athlete" have a "rdfs: subClassOf" relationship.

Hereinafter, the RDF schema (which may be referred to as RDFS schema) and the schema defined by using other standard vocabularies or independently defined vocabularies are referred to as “Ontology”.

FIG. 1 is a diagram showing an example of representation in a graph format, which is an example of an RDF description method. In the explanation of FIG. 1, an example is shown in which the URI in the DB “http://aaa.org/” is included in the instance of “Hanako Suzuki”, the governor of Tokyo.

Further, an example of the expression shown in FIG. 1 is shown below in the text (n3) format.

<http://ja.aaa.org/resource/Tokyo><http://aaaa.org/ontology/leader><http://ja.aaa.org/resource/ Hanako Suzuki>.
<http://ja.aaa.org/resource/ Hanako Suzuki>
rdf: type <http://aaa.org/ontology/Politician>;
<http://aaa.org/ontology/birthPlace><http://ja.aaa.org/resource/ Hyogo Prefecture>;
<http://aaa.org/ontology/birthDate> “1960-01-01”.

In this way, RDF expresses "things" and "things" as a triplet of "subject" (S; Subject), "predicate" (P; Predicate), and "object" (O; Object). .. According to FIG. 1 and the expression example shown in the text (n3) format, "things" and "things" are organized as follows. Note that "rdf: type" is a predicate that defines the relationship between an instance and a class.

"The governor (P) of Tokyo (S) is Hanako Suzuki (O)."
"Hanako Suzuki (S) is a (P) politician (O), her birthplace (P) is Hyogo prefecture (O), and her date of birth (P) is 1960-01-01 (O)."

FIG. 2 is a diagram illustrating a target for narrowing down by category and facets for each category in KG data. For example, KG data is a set of "subject", "predicate", and "object", such as "Taro Yamada", "rdf: type", "professional baseball player", "Taro Yamada", and "at bat". , "Left" set may be included.

The facet search system is, for example, "things" to "people" or "organization", "people" to "politicians", "professional baseball players" or "soccer players", "organizations" to "company" or "professional baseball". It may be possible to narrow down (search) categories hierarchically, such as "team".

For example, in the facet search system, the user can narrow down the search results by selecting facets in any category and selecting the presented candidate values. In the example of FIG. 2, focusing on the category of "politician", the facet is {name, political party, birthplace, date of birth}, and the character string and ID in the DB associated with each are The value for the facet.

FIG. 3 is a diagram showing a screen display example of the UI (User Interface) of the facet search system. As shown in FIG. 3, the UI of the facet search system may include a category search screen 100 and a facet search screen 200 transitioning from the category search screen 100 as screens to be displayed on the user's terminal.

The category search screen 100 is a screen for narrowing down categories from KG data, in other words, a screen that accepts designation of a specific category, and may include, for example, a category selection area 110 and a class selection area 120. The category search screen 100 may include a button 130 for displaying the search conditions saved on the facet search screen 200.

The selection area 110 is, for example, an area for accepting the selection of a data set, for example, a “person” or an “organization” from the “thing” shown in FIG. 2, and in the example of FIG. 3, the “data set A” and the “data set”. "B" is displayed. The selection area 120 is an area for displaying a list of classes associated with the "person" or "organization" selected in the selection area 110, and is, for example, from "person" to "politician" shown in FIG. This is an area for accepting selections such as "professional baseball player" or "soccer player".

In the example of FIG. 3, for convenience, the selection area 110 is referred to as a “data set” and the selection area 120 is referred to as a “class” selection area, but the “data set” selected in the selection areas 110 and 120 You can think of "class" as "category".

The UI displays the facet search screen 200 based on the information of the class (category) selected in the selection area 120.

The facet search screen 200 is a screen for searching facets from the class (category) selected on the category search screen 100. That is, the facet search screen 200 is a search screen that is a transition destination from the category search screen 100, and is a search screen for performing a search targeting a plurality of facets associated with a specific category.

The facet search screen 200 may include, for example, an item list area 210, a search condition setting area 230, an output item setting area 250, an output language setting area 260, and a list display area 280.

The item list area 210 is an area for displaying the facet key in the KG data based on the class (category) selected on the category search screen 100.

The search condition setting area 230 is an area for setting a search condition for the facet key for which the add button 220 is pressed while the item list area 210 is selected. The output item setting area 250 is an area for setting an item to output an entity and an output order for the facet key for which the add button 240 is pressed while being selected in the item list area 210. The output language setting area 250 is an area for setting the output language of the entity.

The list display area 280 is an area for displaying a list of entities based on the setting contents of the setting areas 230, 250, and 260 when the search button 270 is pressed.

The facet search screen 200 displays operation buttons 290 for any one or more of the display contents of the list display area 280, the setting contents of the setting areas 230, 250 and 260, and the display contents of the item list area 210. You may. The operation button 290 includes, for example, an output button in CSV (Comma Separated Value) format, a confirmation button for a SPARQL (SPARQL Protocol and RDF Query Language) statement, which is an example of an RDF query language, and a save button for search conditions. May include.

By the way, the facet key suitable for the category may not be displayed in the item list area 210 on the facet search screen 200. Hereinafter, such a case will be described as a comparative example.

(Comparison example)
For example, it is assumed that the screen display shown in FIG. 3 is executed by using the technique described in Non-Patent Document 1. In this technique, for example, the server uses XML (eXtensible Markup Language) data to extract facet values according to the following procedure.

(I) The server extracts a structural summary from the XML data.
The structure summary is information indicating the parent-child relationship of each element in the XML data, the appearance frequency of the child element with respect to the parent element, and the like.

(Ii) Extract class candidates and facet candidates from the structural summary.
In the above technique, a certain node in the structure summary corresponding to an object in XML data is called a class, and a selected node among the descendant nodes of the class node in the structure summary is called a facet.

(Iii) Extract appropriate ones from class candidates and facet candidates.
(Iv) Extract the XML subtree corresponding to the class extracted from the XML data as an object.
(V) The value of the element corresponding to the extracted facet is extracted as the facet value.

For example, in the above procedures (i) to (v), the server may target KG data in the RDF description format instead of XML data.

In the technique described in Non-Patent Document 1, in the procedure (iii) above, the server employs a method that combines a frequency-based approach and a meaning-based approach. In the frequency approach, the server can extract facet keys that can get more instances in the KG data. In a meaning-based approach, the server uses existing knowledge such as WordNet and Wikipedia® to determine if the facet key is human-interpretable.

In the above technique, the importance of the facet key is not considered. On the other hand, Non-Patent Document 2 describes an index for ranking facet keys.

Here, when the item list area 210 on the facet search screen 200 is displayed by using the techniques described in Non-Patent Documents 1 and 2, in other words, a case where facet search within a specific category is performed is considered. .. When performing a facet search within a specific category, there are cases where it is better to narrow down by the facet key specific to the category. For example, it is assumed that the category of professional baseball player is selected on the category search screen 100.

In this case, in the technique described in Non-Patent Document 2, due to the statistical tendency of the entire KG data, the place of origin and the date of birth are ranked higher as important facet keys than the experience of participating in Koshien.

Further, in the technique described in Non-Patent Document 1, only the presence or absence of facets in the knowledge base is used, and the ontology of knowledge is not used. For example, regarding the facet key of "date", the "date of birth" for a person and the "date of first appearance" for a professional baseball player are more important facets. This information is structured in the KG by an ontology or the like.

However, when the category of professional baseball players is selected, it is more in line with the user's knowledge to narrow down the facet keys by turn at bat, dominant arm, Koshien participation experience, etc., rather than by date of birth, place of birth, company type, etc. Facet search can be realized.

Therefore, the facet search system according to the embodiment described below mainly adopts the methods (a) and (b) below and displays the facet key ranking suitable for the category in the item list area 210. To do.

(A) Introduction of indicators that emphasize category-specific facet keys.
(B) Implementation of semantic interpretation of facet keys in consideration of the knowledge structure of KG.

As a result, it is possible to output a plurality of appropriate facets from the set of facets associated with the category, and it is possible to reduce the turn to reach the target data of the user.

[1-2] Configuration Example of Facet Search System FIG. 4 is a block diagram showing a functional configuration example of the facet search system 1 according to the embodiment. The facet search system 1 is an example of a search system that performs facet search, and as shown in FIG. 4, an example, a server 2, a knowledge graph (KG) 3, and one or more (one in the example of FIG. 4). ) Terminal 4 may be provided.

KG3 is an example of a knowledge base, and may store data described in RDF description format, for example.

The terminal 4 is an example of an information processing terminal used by a user of the facet search system 1, and is a computer such as a PC (Personal Computer) or a server that accesses the server 2 for facet search.

The KG 3 and the server 2 and the terminal 4 and the server 2 may be connected to each other so as to be able to communicate with each other via a network (not shown). The network may include a WAN (Wide Area Network), a LAN (Local Area Network), or a combination thereof. The WAN may include the Internet and the LAN may include a VPN (Virtual Private Network).

The server 2 is an example of a search device, an information processing device, or a computer. For example, in the facet search system 1, the server 2 performs various processes such as referencing the KG3, responding to the terminal 4, and notifying information in response to various accesses related to the facet search from the terminal 4.

The server 2 may provide, for example, a function for enabling access to the terminal 4. Examples of the function include generation and display control of a screen such as a web page used for access by the terminal 4. For example, the terminal 4 transmits an access request to the server 2 using an application such as a browser, and accesses the server 2 via a web page displayed on the application based on the screen information received from the server 2. good.

The server 2 may be a virtual server (VM) or a physical server. Further, the function of the server 2 may be realized by one computer or may be realized by two or more computers. Further, at least a part of the functions of the server 2 may be realized by using the HW (Hardware) resource and the NW (Network) resource provided by the cloud environment.

(Hardware configuration example)
FIG. 5 is a block diagram showing a hardware (HW) configuration example of the computer 10 that realizes the function of the server 2. When a plurality of computers are used as the HW resource that realizes the function of the server 2, each computer may have the HW configuration illustrated in FIG.

As shown in FIG. 5, the computer 10 has an HW configuration, for example, a processor 10a, a memory 10b, a storage unit 10c, an IF (Interface) unit 10d, an I / O (Input / Output) unit 10e, and a reading unit. It may be provided with 10f.

The processor 10a is an example of an arithmetic processing unit that performs various controls and operations. The processor 10a may be connected to each block in the computer 10 so as to be able to communicate with each other by the bus 10i. The processor 10a may be a multiprocessor including a plurality of processors, a multicore processor having a plurality of processor cores, or a configuration having a plurality of multicore processors.

Examples of the processor 10a include integrated circuits (ICs) such as CPUs, MPUs, GPUs, APUs, DSPs, ASICs, and FPGAs. A combination of two or more of these integrated circuits may be used as the processor 10a. CPU is an abbreviation for Central Processing Unit, and MPU is an abbreviation for Micro Processing Unit. GPU is an abbreviation for Graphics Processing Unit, and APU is an abbreviation for Accelerated Processing Unit. DSP is an abbreviation for Digital Signal Processor, ASIC is an abbreviation for Application Specific IC, and FPGA is an abbreviation for Field-Programmable Gate Array.

The memory 10b is an example of HW that stores information such as various data and programs. Examples of the memory 10b include one or both of a volatile memory such as DRAM (Dynamic Random Access Memory) and a non-volatile memory such as PM (Persistent Memory).

The storage unit 10c is an example of HW that stores information such as various data and programs. Examples of the storage unit 10c include a magnetic disk device such as an HDD (Hard Disk Drive), a semiconductor drive device such as an SSD (Solid State Drive), and various storage devices such as a non-volatile memory. Examples of the non-volatile memory include a flash memory, an SCM (Storage Class Memory), a ROM (Read Only Memory), and the like.

Further, the storage unit 10c may store a program 10g (search program) that realizes all or a part of various functions of the computer 10. For example, the processor 10a of the server 2 can realize the function as the server 2 illustrated in FIG. 4 by expanding and executing the program 10g stored in the storage unit 10c in the memory 10b.

The IF unit 10d is an example of a communication IF that controls connection with a network and communication. For example, the IF unit 10d may include an adapter compliant with LAN (Local Area Network) such as Ethernet (registered trademark) or optical communication such as FC (Fibre Channel). The adapter may support one or both wireless and wired communication methods. For example, the server 2 may be connected to each of the KG3 and the terminal 4 so as to be able to communicate with each other via the IF unit 10d. Further, for example, the program 10g may be downloaded from the network to the computer 10 via the communication IF and stored in the storage unit 10c.

The I / O unit 10e may include one or both of an input device and an output device. Examples of the input device include a keyboard, a mouse, a touch panel, and the like. Examples of the output device include a monitor, a projector, a printer and the like.

The reading unit 10f is an example of a reader that reads data and program information recorded on the recording medium 10h. The reading unit 10f may include a connection terminal or device to which the recording medium 10h can be connected or inserted. Examples of the reading unit 10f include an adapter compliant with USB (Universal Serial Bus) and the like, a drive device for accessing a recording disk, a card reader for accessing a flash memory such as an SD card, and the like. The program 10g may be stored in the recording medium 10h, or the reading unit 10f may read the program 10g from the recording medium 10h and store it in the storage unit 10c.

Examples of the recording medium 10h include a non-temporary computer-readable recording medium such as a magnetic / optical disk or a flash memory. Examples of magnetic / optical disks include flexible discs, CDs (Compact Discs), DVDs (Digital Versatile Discs), Blu-ray discs, HVDs (Holographic Versatile Discs), and the like. Examples of the flash memory include semiconductor memories such as USB memory and SD card.

The HW configuration of the computer 10 described above is an example. Therefore, the increase / decrease of HW (for example, addition or deletion of arbitrary blocks), division, integration in any combination, addition or deletion of buses, etc. in the computer 10 may be performed as appropriate. For example, in the server 2, at least one of the I / O unit 10e and the reading unit 10f may be omitted.

The terminal 4, which is an example of the information processing terminal, may be realized by the same HW configuration as the computer 10 described above.

For example, the processor 10a of the terminal 4 can realize the function as the terminal 4 shown in FIG. 4 by expanding and executing the program 10g stored in the storage unit 10c in the memory 10b.

The terminal 4 shown in FIG. 4 may include an input device and a display device, which are examples of the I / O unit 10e. For example, the processor 10a of the terminal 4 may display each screen on the display device based on the information received from the server 2 via the IF unit 10d. Further, the processor 10a of the terminal 4 may transmit the input information to the server 2 via the IF unit 10d.

(Functional configuration example)
Returning to the description of FIG. 4, the server 2 may optionally include a memory unit 21, a search control unit 22, a statistical processing unit 23, a meaning interpretation processing unit 24, and a ranking adjusting unit 25.

The memory unit 21 is an example of a storage area, and stores various information related to facet search. As shown in FIG. 4, the memory unit 21 may optionally store the frequency table 21a and the score DB 21b. In the following description, for convenience, the data formats of the frequency table 21a and the score DB 21b will be described as a table format, but the present invention is not limited to this, and various DB data formats may be used.

The frequency table 21a and the score DB 21b may be stored in, for example, a storage area of at least one of the memory 10b and the storage unit 10c shown in FIG. In other words, the memory unit 21 may be realized by a storage area included in at least one of the memory 10b and the storage unit 10c.

The search control unit 22 provides the terminal 4 with a UI including the category search screen 100 and the facet search screen 200 illustrated in FIG. For example, the search control unit 22 generates screen information for each of the category search screen 100 and the facet search screen 200, transmits the screen information to the terminal 4, and indicates a character string and selection items input via the UI on the terminal 4. The control information may be received from the terminal 4.

As an example, when the search control unit 22 receives the control information including the class (category) selected in the selection areas 110 and 120 of the category search screen 100 from the terminal 4, the search control unit 22 receives the control information from the statistical processing unit 23 and the semantic interpretation processing. Each of the parts 24 may be notified. In other words, the search control unit 22 is an example of a reception unit that accepts the designation of a specific category.

Further, when the search control unit 22 is notified by the ranking adjustment unit 25 of information indicating a plurality of facet keys, the search control unit 22 displays the facet keys included in the information, including the display order (arrangement order), in the item list area 210. To display. In other words, the search control unit 22 is an example of an output unit that outputs a plurality of facets in order (for example, displayed in the item list area 210) according to the priority calculated by the ranking adjustment unit 25.

The statistical processing unit 23 creates or updates the score DB 21b based on the KG3 and the control information as a preliminary phase (preparation phase) before the facet search is performed. For example, the statistical processing unit 23 may include a frequency table creation unit 23a and a score calculation unit 23b.

The frequency table creation unit 23a creates a frequency table 21a. For example, as shown in FIG. 6, the frequency table creation unit 23a acquires all the class sets 21a1 to be faceted search from KG3. For example, the frequency table creation unit 23a may execute the class acquisition query Q1 shown in FIG. 6 to acquire the class set 21a1 from the KG3.

Then, the frequency table creation unit 23a calculates the facet key frequency table 21a2 and the facet key facet value frequency table 21a3 (see FIG. 7) based on the class and KG3 included in the class set 21a1. For example, the frequency table creation unit 23a executes the facet key frequency table query Q2 and the facet key facet value frequency table query Q3 shown in FIG. 6, respectively, and from KG3 to the facet key frequency table 21a2 and the facet key facet value frequency. Table 21a3 may be obtained.

In KG3, the class corresponds to the subject (S), the facet key corresponds to the predicate (P), and the facet value corresponds to the object (O). Therefore, the facet key frequency table 21a2 may be referred to as the P frequency table 21a2, and the facet key facet value frequency table 21a3 may be referred to as the PO frequency table 21a3.

Each query Q1 to Q3 shown in FIG. 6 is an example of a query using SPARQL, which is an example of an RDF query language. In queries Q1 to Q3, "? S" corresponds to the subject (S), "? P" corresponds to the predicate (P), and "? O" corresponds to the object (O), and "% CLASS%" in queries Q2 and Q3. Is replaced by each class obtained in query Q1.

FIG. 7 is a diagram showing an example of the class set 21a1, the P frequency table 21a2, and the PO frequency table 21a3.

As illustrated in FIG. 7, the class set 21a1 is information obtained by extracting facet keys (P) such as "name" and affiliated political party from KG3 for each category such as "politician".

The P frequency table 21a2 is information obtained by extracting the "frequency" in the KG3 (for example, the "number" of the records obtained in the query Q2) for each facet key (P) included in the class set 21a1 from the KG3.

The PO frequency table 21a3 shows the "frequency" in KG3 (for example, the "number" of the records obtained by the query Q3) for each facet key (P) and each facet value (O) included in the class set 21a1 from KG3. ") Is the extracted information.

The frequency table creation unit 23a may store at least the information of the PO frequency table 21a3 among the class set 21a1, the P frequency table 21a2, and the PO frequency table 21a3 in the memory unit 21 as the frequency table 21a. The reason why at least the information of the PO frequency table 21a3 is stored in the frequency table 21a is that the P frequency table 21a2 can be derived by summing the facet values of the PO frequency table 21a3 in facet key units.

The score calculation unit 23b calculates the score DB 21b based on the frequency table 21a created by the frequency table creation unit 23a.

FIG. 8 is a diagram showing an example of a calculation formula for each index, and FIG. 9 is a diagram showing an example of an index value 21b1. For example, the score calculation unit 23b may calculate the index value 21b1 illustrated in FIG. 9 by using the calculation formulas (see FIG. 8) of each index exemplified in the following formulas (1) to (4).

The facet frequency (freq (f)) shown in the above formula (1) is an index showing the appearance frequency of facets in the entire search target data of KG3. A facet with a higher facet frequency means that it appears in more search target data, and a facet with a lower facet frequency means that it appears more locally in the search target data. In the above equation (1), "N" is the total number of data to be searched, and "n (facet)" is the number of data to be searched per facet.

The facet equilibrium degree (bala (f)) shown in the above equation (2) is a distribution of the number of data that can be searched for each facet. A facet with a larger facet balance means that the range of data that can be searched is wider and a well-balanced facet, and a facet with a smaller facet balance means that the appearance of the facet key in the search target data is more biased. In the above equation (2), "n (key _i )" is the number of search target data that can be searched by the facet key "key _{i ", "n key} " is the number of keys in the facet "f", and "μ". Is the average of “n (key _i)”.

The key density (card (f)) shown in the above equation (3) is the distribution of the number of facet keys in each facet. In the above equation (3), "n _key " is the number of keys in the facet "f", "μ" is _{the average of "n key} ", and "σ ² " is the variance.

The key monotonicity (mono (f)) shown in the above equation (4) is an index of the monotonicity of the facet key, and is an index for evaluating the average number of data that can be searched for each facet key in a certain facet. In the above equation (4), “avg” is _{the average of “n (key i} )”, and “μ” and “σ ² ” are the average and variance.

The above formulas (1) to (4) are the same as the calculation formulas for each index described in, for example, Non-Patent Document 2, and detailed description thereof will be omitted.

The score calculation unit 23b may calculate the index value 21b1 for each facet key as illustrated in FIG. 9 based on the frequency table 21a and the above equations (1) to (4).

Further, the score calculation unit 23b calculates the category importance (signif (f, C)) for each category and for each facet key based on the frequency table 21a and the index value 21b1. The category importance (signif (f, C)) is an index that considers the frequency of appearance of facets in the class, and is an index that emphasizes the facet key unique to the category.

FIG. 10 is a diagram showing an example of the category importance table 21b2 in which the category importance (signif (f, C)) is represented in a tabular format.

For example, the score calculation unit 23b may calculate the category importance (signif (f, C)) based on the following formula (5).
signif (f, C) = weight (f, C) * uniq (f) (5)

In the above equation (5), "weight (f, C)" is the weight of the appearance frequency of facets in the class, and may be expressed by the following equation (6), for example.
weight (f, C) = n (f, C) / N _d (6)

Here, in the above equation (6), “N _d ” is the total number of “f” in the class “C”, and “n (f, C)” is the number of facets in the class “C”.

Further, in the above equation (5), "uniq (f)" is an index indicating the uniqueness of each facet. The uniqueness is an index indicating whether or not a facet appears in a specific class among a plurality of classes (for example, only in a specific class). For example, "uniq (f)" may be expressed by the following equation (7).
uniq (f) = NC / uniq_count (f) (7)

Here, in the above equation (7), "NC" is the total number of classes in KG3, and "uniq_count (f)" is the number of classes including "f" in KG3. In this way, the uniqueness may be obtained by dividing the total number of classes in KG3 by the number of classes in which facets appear.

Thus, the category importance (signif (f, C)) is an example of a first index based on the number of categories in which each of the plurality of facets associated with a particular category in KG3 is associated in KG3. ..

The score calculation unit 23b may calculate the category importance (signif (f, C)) for each facet key by using the frequency table 21a and the above formula (5). As shown in FIG. 10, the score calculation unit 23b normalizes the category importance (signif (f, C)) to the range of “0” to “1”.

Then, the score calculation unit 23b calculates the facet score (new_score) by using the score (org_score) based on the index value 21b1 described above and the score (signif) based on the above formula (5).

For example, the score calculation unit 23b may calculate a weighted linear sum of the score (org_score) and the score (signif) and obtain the facet score (new_score) as illustrated in the following equation (8).
new_score (f) = ω * org_score (f) + (1-ω) * signif (f) (8)

In the above equation (8), "ω" is a weight. In one embodiment, as a non-limiting example, “ω = 0.5”. When "ω = 0.5", the above equation (8) is expressed as the following equation (8').
new_score (f) = 0.5 * org_score (f) + 0.5 * signif (f) (8')

Here, in the above formula (8) or (8'), "org_score (f)" may be expressed by the following formula (9). In the following equation (9), it is assumed that α + β + γ + θ = 1.

FIG. 11 is a diagram showing an example of a facet score table 21b3 in which the facet score (new_score) is represented in a tabular format. For example, the score calculation unit 23b may store the information of the index value 21b1, the category importance table 21b2, and at least the facet score table 21b3 of the facet score table 21b3 in the memory unit 21 as the score DB 21b.

As illustrated in FIG. 11, according to the score (new_score) that takes into account the importance of facets within the class (category), for example, for "politician", "affiliation" rather than "name" or "date of birth" "Political party" has a higher score, and for "professional baseball player", "defensive position" has a higher score than "name" and "date of birth".

That is, in the item list area 210 shown in FIG. 3, facet keys such as "affiliated party" for "politician" and "defensive position" for "professional baseball player" are displayed as high priority items. become. Therefore, the server 2 can output a plurality of appropriate facets narrowed down by the facet key having a higher score from the set of facets associated with the category.

In this way, the score calculation unit 23b calculates the category importance including uniq (f) for each of the plurality of facets, and stores the information including the calculated category importance (for example, the score DB 21b) in the memory unit 21. This is an example of the calculation unit.

Returning to the explanation of FIG. 4, the meaning interpretation processing unit 24 extends from the class (category) selected on the category search screen 100 (see FIG. 3) to the class to which a certain facet belongs when the facet search is performed. The pass on KG3 is reflected in the score. In this way, the semantic interpretation processing unit 24 changes the score so that the facets having a high priority in consideration of the ontology are extracted according to the selected class when the facet search is performed.

For example, the semantic interpretation processing unit 24 may calculate a score (ont_score) in consideration of the distance on the graph in the ontology, as shown in the following equation (10). The score (ont_score) is an example of a second index based on the distance between the selected class and each of the plurality of facets in KG3.
ont_score (f, C) = 1 / (distance (C, C _f ) + 1) (10)

Here, in the above equation (10), “f” is the facet for which the score is calculated, “C” is the class (category) selected in the item list area 210, and “C _f ” is the item. This is the class to which the candidate facets displayed in the list area 210 belong.

Further, in the above equation (10), "distance (C, C _f )" is the distance on the schema (ontology), that is, on the graph, between the selected class and the class to which the selected facet belongs. .. The distance on the graph may mean the distance between the layers of the class (category). For example, the semantic interpretation processing unit 24 may regard each hierarchical class as each node in the tree structure, and calculate the distance between the nodes as the distance on the graph by a known method. If “C = C _f ”, then “distance (C, C _f ) = 0”.

FIG. 12 is a diagram showing an example of a graph representing a class and a facet key in RDF Schema. Hereinafter, in the example of FIG. 12, an example of calculating the score (ont_score) when the selected category is “BaseballPlayer” (see the broken line) will be described.

As an example, when the facet for which the score is calculated is a "name (person)", the score (ont_score) is calculated as shown in the following formula (11).
ont_score (name (person), Baseball Player)
= 1 / (distance (BaseballPlayer, Person) + 1)
= 1 / (1 + 1) = 0.5 (11)

As another example, when the facet for which the score is calculated is the "head office location", the score (ont_score) is calculated as shown in the following formula (12).
ont_score (Headquarters location, Baseball Player)
= 1 / (distance (BaseballPlayer, Company) + 1)
= 1 / (3 + 1) = 0.25 (12)

As another example, when the facet for which the score is calculated is the "affiliated political party", the score (ont_score) is calculated as shown in the following formula (13).
ont_score (Political party, Baseball Player)
= 1 / (distance (BaseballPlayer, Politician) + 1)
= 1 / (2 + 1) = 0.33 (13)

In this way, the semantic interpretation processing unit 24 has a higher priority as the facet key having a higher degree of relevance in the ontology, for example, the facet key having a shorter distance on the graph (the distance (C, C _{f) is smaller).} Calculate the score (ont_score). Further, the semantic interpretation processing unit 24 has a score such that the facet key having a smaller degree of relevance in the ontology, for example, the facet key having a longer distance on the graph (the distance (C, C _f ) is larger), the lower the priority. Calculate (ont_score). In other words, the score (ont_score) can be said to be a score that reflects the semantic interpretation of the facet key in consideration of the knowledge structure of KG3.

In addition, the semantic interpretation processing unit 24 adds or substitutes the semantic distance between the facet key on KG3 and the selected class on the graph (distance (C, C _f)). The score (ont_score) may be calculated based on the considered index.

Examples of the index considering the semantic distance (semantic distance index) include an index determined according to whether or not the vocabulary of the predicate (P; Predicate) serving as the facet key is a standard vocabulary. The determination of whether or not the vocabulary is the standard vocabulary may be made, for example, by determining whether or not the vocabulary serving as the facet key is registered in the DB for accumulating the standard vocabulary. Examples of the DB for accumulating the standard vocabulary include DBs such as "prefix.cc" and "Linked Open Vocabularies".

For example, the semantic interpretation processing unit 24 may calculate a score (ont_score) in consideration of the semantic distance in the ontology, as shown in the following equation (14).
ont_score (f, C) = (1 / (distance (C, C _f ) + 1)) * std_vocab (f) (14)

Here, in the above equation (14), the term of (1 / (distance (C, C _f ) + 1)) is the same as that of the above equation (10), and “std_vocab (f)” is the following equation ( As shown in 15), the function may be "1.0" if it is a standard vocabulary, "0.5" if it is not a standard vocabulary, and so on.

For example, as shown in the above equation (14), the semantic interpretation processing unit 24 has a semantic distance index shown in the above equation (15) with respect to the score considering the distance on the graph shown in the above equation (10). By multiplying, a score that considers both the graph and the semantic distance may be calculated.

Alternatively, the semantic interpretation processing unit 24 may adopt "std_vocab (f)" shown in the above equation (15) as a score (ont_score) considering only the semantic distance instead of the above equation (14). good.

FIG. 13 is a diagram showing an example of a graph representing a class and a facet key in RDF Schema. Hereinafter, in the example of FIG. 13, when the selected category is “BaseballPlayer” (see the broken line), a calculation example of the score (ont_score) in consideration of the semantic distance index will be described.

In FIG. 13, for example, "name (person)" is a standard vocabulary represented by "foat: name", and "name (company)" is a standard vocabulary represented by "skos: prefLabel". Yes, "Headquarters location" shall be the original vocabulary (non-standard vocabulary) represented by "14a-ont: Headquarters location".

As an example, when the facet for which the score is calculated is "name (company)", the score (ont_score) is calculated as shown in the following formula (16).
ont_score (name (company), Baseball Player)
= (1 / (distance (BaseballPlayer, Company) + 1)) * std_vocab (name (company))
= (1 / (3 + 1)) * 0.5 = 0.125 (16)

As another example, when the facet for which the score is calculated is the "head office location", the score (ont_score) is calculated as shown in the following formula (17).
ont_score (Headquarters location, Baseball Player)
= (1 / (distance (BaseballPlayer, Company) + 1)) * std_vocab (Headquarters location)
= (1 / (3 + 1)) * 1.0 = 0.25 (17)

In this way, the semantic interpretation processing unit 24 has a score (ont_score) such that the facet key having a higher degree of relevance in the ontology, for example, the facet key having a closer (larger) semantic distance (std_vocab), the higher the priority. Is calculated. Further, the semantic interpretation processing unit 24 calculates a score (ont_score) that has a lower priority as the facet key having a smaller degree of relevance in the ontology, for example, a facet key having a longer (smaller) semantic distance (std_vocab). do.

As a result, in the item list area 210 shown in FIG. 3, facet keys in which one or both of the distance on the graph and the semantic distance are close to the selected category are displayed as high-priority items. It will be. Therefore, the server 2 can output a plurality of appropriate facets narrowed down by the facet key having a higher score from the set of facets associated with the category.

Returning to the description of FIG. 4, the ranking adjustment unit 25 calculates the final facet score (final_score) in the facet search.

For example, in the facet search, the search control unit 22 described above selects a target category on the category search screen 100 while tracing the hierarchy of the classes assigned to the entities, and the facet search screen 200 is displayed.

At this time, based on the selected category, the semantic interpretation processing unit 24 calculates the score (ont_score) as the facet importance using the knowledge structure.

The ranking adjustment unit 25 calculates the final score (final_score) based on the facet score (new_score) calculated by the score calculation unit 23b in the preliminary phase and the score (out_score) calculated by the meaning interpretation processing unit 24. You can do it. For example, the ranking adjusting unit 25 may calculate the score (final_score) by multiplying the facet score (new_score) and the score (out_score) based on the following equation (18).
final_score (f, C) = new_score (f, C) * ont_score (f, C) (18)

FIG. 14 shows the score (org_score) shown in the above formula (9), the facet score (new_score) shown in the above formula (8), the score (ont_score) shown in the above formula (10) or the above formula (14), and the above formula. It is a figure which illustrates the final score (final_score) shown in (18) in a tabular form.

In the example of FIG. 14, a case where “professional baseball player” is selected as a category on the category search screen 100 is shown. In this case, the final score (final_score) is "0.562", which is a higher value for both the facet score (new_score) and the score (ont_score), and the "defensive position" is higher. This "defensive position" is a score lower than the "name" and "date of birth" in the score (org_score) shown in the above formula (9).

In this way, by the index that emphasizes the facet key peculiar to the category and the meaning interpretation of the facet key considering the knowledge structure of KG3, the appropriate facet like the "defensive position" for the category of "professional baseball player". It is calculated so that the key score is high.

For example, the ranking adjustment unit 25 sorts the facet keys to be displayed in the item list area 210 in descending order of the score based on the calculated final score (final_score), and the sorted facet key information is searched and controlled by the search control unit 22. You may output to.

The final score (final_score) calculated by the ranking adjustment unit 25 is an example of the priority of a plurality of facets.

As a result, as illustrated in FIG. 15, the search control unit 22 replaces the score (org_score) -based list 211 shown in the above equation (9) with the item list area 210 based on the final score (final_score). List 212 of facet keys can be displayed. The list 212 is an example of a list in which a plurality of facets are arranged in order according to the priority calculated by the ranking adjustment unit 25.

16 and 17 are diagrams showing a comparative example of MRR (Mean Reciprocal Rank) when facet keys are sorted based on each of “org_score”, “new_score” and “final_score”. MRR is an evaluation index of the quality of the search result, and is a value in the range of "0" to "1". The closer the MRR is to "1", the higher the facet key displayed in the item list area 210 is searched (selected), which means that the facet search according to the user's knowledge is realized. ..

FIG. 16 shows a comparative example of the facet ranking and MRR based on the score (org_score) shown in the above formula (9) and the facet ranking and MRR based on the facet score (new_score) shown in the above formula (8). In the example of FIG. 16, from the data set having the number of data to be faceted “11401”, the category of “baseball player” is selected, and each facet key of “at bat”, “draft ranking”, and “first appearance” is selected. Imagine a case.

As illustrated in FIG. 16, on an "org_score" basis, the facet rank is around 100 for each facet key, and the MRR is "0.0094". On the other hand, on the "new_score" base, the facet ranking is 3rd to 6th, and the MRR is "0.2333".

Thus, in the "new_score" base, the facets unique to "baseball players" such as "at bat", "draft ranking", "first appearance", and MRR are higher than those in the "org_score" base.

FIG. 17 shows a comparative example of the facet ranking and MRR based on the facet score (new_score) shown in the above formula (8) and the facet ranking and MRR based on the final score (final_score) shown in the above formula (18). .. In the example of FIG. 17, from the data set having the number of data to be faceted "11401", the category of "baseball player" is selected, and each facet key of "name", "place of birth", and "date of birth" is selected. Imagine a case where you want to.

As illustrated in FIG. 17, on a “new_score” basis, the facet ranks are 31st to 66th and the MRR is “0.2333”. On the other hand, on the "final_score" basis, the facet ranking is 141st to 211th, and the MRR is "0.0057".

In this way, "name", "place of birth", and "date of birth" are not facets specific to "BaseballPlayer" but facets related to "Person". Therefore, the facet ranking and MRR are lower in the "final_score" base, which takes into account the degree of relevance (ont_score) in the ontology, than in the "new_score" base.

As described above, the server 2 according to the embodiment efficiently searches for facets by first narrowing down by the category (class) given to the facets in KG3 and then narrowing down by the facets in the category. To realize.

At this time, the server 2 belongs to a specific class by introducing an index that emphasizes class-specific facets, extracting the relationship between facets from the facet search target KG3, and using the relationship. Efficiently retrieve a set of entities.

As a result, it is possible to output a plurality of appropriate facets from the set of facets associated with the category, and it is possible to reduce the turn to reach the target data of the user.

[1-3] Operation Example Hereinafter, an operation example of the facet search system 1 described above will be described with reference to a flowchart.

[1-3-1] DB Creation Process FIG. 18 is a flowchart illustrating an operation example of the DB creation process according to the embodiment.

As illustrated in FIG. 18, in the server 2, the frequency table creation unit 23a of the statistical processing unit 23 creates the frequency table 21a (step S1). Further, the score calculation unit 23b of the statistical processing unit 23 creates the score DB 21b based on the frequency table 21a (step S2), and the processing ends.

FIG. 19 is a flowchart illustrating an operation example of the frequency table creation process in step S1 of FIG. As illustrated in FIG. 19, the frequency table creation unit 23a, as the frequency table creation process acquires all classes _{C all} in KG3 (step S11).

The frequency table creation unit 23a determines whether or not all the classes C, which are the elements of _{all the classes Call, have been processed (step S12).} When all processing is performed (YES in step S12), the processing ends.

When not all are processed (NO in step S12), the frequency table creation unit 23a calculates the number of instances for each predicate (Predicate) of the instance whose class is C, in other words, the facet key frequency (step S13). ), P It is stored in the frequency table 21a2.

Then, the frequency table creation unit 23a calculates the number of objects (Object) for each predicate of the instance whose class is C, in other words, the facet key / facet value frequency (step S14), and the PO frequency table 21a3. The process proceeds to step S12.

FIG. 20 is a flowchart illustrating an operation example of the score DB creation process in step S2 of FIG. As illustrated in FIG. 20, the score calculation unit 23b calculates the facet frequency, facet balance, key density, and key monotonicity, respectively, based on the frequency tables 21a and KG3 as the score DB creation process (step S21). ~ S24).

The score calculation unit 23b calculates the category importance based on the class and facet information in KG3 (step S25).

Then, the score calculation unit 23b calculates the score org_score shown in the above equation (9) by using the facet frequency, the facet equilibrium degree, the key concentration, and the key monotonicity (step S26).

Further, the score calculation unit 23b calculates the facet score new_score by using the score org_score calculated in step S26 and the category importance calculated in step S25, stores it in the score DB 21b (step S27), and processes it. finish.

[1-3-2] Facet Search Process FIG. 21 is a flowchart illustrating an operation example of the facet search process according to the embodiment.

As illustrated in FIG. 21, in the server 2, the search control unit 22 accepts the selection of the class C by the terminal 4 on the category search screen 100 presented to the terminal 4 (step S31).

The semantic interpretation processing unit 24 acquires the predicate (Predicate) possessed by the instance belonging to the class C (step S32).

The semantic interpretation processing unit 24 determines whether or not all the facet keys f, which are elements of the predicate (Predicate), have been processed (step S33). When all the processing is not performed (NO in step S33), the meaning interpretation processing unit 24 acquires the score new_score of the facet key f in the class C from the score DB 21b (step S34).

The semantic interpretation processing unit 24 uses the class C and the facet key f to calculate the distance score ont_score based on the ontology in KG3 (step S35), and the processing shifts to step S33.

When all the processing is performed in step S33 (YES in step S33), the meaning interpretation processing unit 24 calculates the score final_score of the facet key f using new_score and ont_score (step S36).

The ranking adjustment unit 25 notifies the search control unit 22 of the facet keys sorted based on final_score. The search control unit 22 displays the sorted facet keys in the item list area 210 of the facet search screen 200, which is the transition destination from the category search screen 100 (step S37), and the facet search related to the display of the item list area 210. The process ends.

[2] Others The technology according to the above-described embodiment can be modified or modified as follows.

For example, the search control unit 22, the statistical processing unit 23 (frequency table creation unit 23a and score calculation unit 23b), the semantic interpretation processing unit 24, and the ranking adjustment unit 25 included in the server 2 shown in FIG. 4 are merged in any combination. It may be divided into each.

Further, the server 2 shown in FIG. 1 may have a configuration in which a plurality of devices cooperate with each other via a network to realize each processing function. As an example, the search control unit 22 may be a Web server, the statistical processing unit 23, the meaning interpretation processing unit 24 and the ranking adjustment unit 25 may be an application server, the memory unit 21 may be a DB server, and the like. In this case, the Web server, the application server, and the DB server may realize each processing function as the server 2 by coordinating with each other via the network.

Further, in one embodiment, the final score (final_score) input to the ranking adjustment unit 25 is a new_score in which signif is taken into consideration and an ont_score as shown in the above equations (8) and (18). It was explained as being a multiplied score. The final score (final_score) is not limited to this, and at least one of signif and ont_score may be considered.

For example, the final score (final_score) may match new_score without considering ont_score, as shown in the following equation (19).

final_score (f, C) = new_score (f, C) (19)

Alternatively, the final score (final_score) may be a score that does not consider signif, as shown in the following equation (20). In one embodiment, the weight ω is exemplifiedly “0.5” in the following equation (20).

final_score (f, C) = ω * org_score (f, C) + (1-ω) * ont_score (f, C) (20)

The final score (final_score) represented by the above formula (19) or (20) can also have the same effect as that of one embodiment as illustrated in FIG. 16 or FIG.

[3] Additional Notes The following additional notes will be further disclosed with respect to the above embodiments.

(Appendix 1)
Accepts the specification of a specific category,
A first index based on the number of categories associated with each of the specific categories in the Knowledge Graph, and each of the specific category and the plurality of facets in the Knowledge Graph. The plurality of facets are sequentially output in order according to the priority of the plurality of facets calculated based on at least one of the second index based on the distance between the two.
A search program that lets a computer perform processing.

(Appendix 2)
The first index includes the result of dividing the number of all categories in the Knowledge Graph by the number of categories each of the plurality of facets associated with in the Knowledge Graph.
The search program described in Appendix 1.

(Appendix 3)
The first index, which includes the results for each of the plurality of facets, is calculated.
The calculated information including the first index is stored in the storage area.
The search program according to Appendix 2, which causes the computer to execute the process.

(Appendix 4)
The second indicator is based on the distance between the hierarchy of the particular category in the Knowledge Graph and each of the hierarchy of categories with which each of the plurality of facets is associated in the Knowledge Graph.
The search program according to any one of Supplementary notes 1 to 3.

(Appendix 5)
The second index is based on whether or not each of the plurality of facets is a standard vocabulary.
The search program according to any one of Supplementary notes 1 to 4.

(Appendix 6)
The output process is a search screen that is a transition destination from the screen that accepts the designation of the specific category, and is for performing a search targeting the plurality of facets associated with the specific category. The search screen includes a process of displaying the plurality of facets arranged in order according to the priority of the plurality of facets.
The search program according to any one of Supplementary notes 1 to 5.

(Appendix 7)
Accepts the specification of a specific category,
Each of the plurality of facets associated with the specific category in the Knowledge Graph is a first index based on the number of categories associated with the Knowledge Graph, and each of the specific category and the plurality of facets in the Knowledge Graph. The plurality of facets are sequentially output in order according to the priority of the plurality of facets calculated based on at least one of the second index based on the distance between the two.
A search method in which a computer performs processing.

(Appendix 8)
The first index includes the result of dividing the number of all categories in the Knowledge Graph by the number of categories each of the plurality of facets associated with in the Knowledge Graph.
The search method described in Appendix 7.

(Appendix 9)
The first index, which includes the results for each of the plurality of facets, is calculated.
The calculated information including the first index is stored in the storage area.
The search method according to Appendix 8, wherein the computer executes the process.

(Appendix 10)
The second indicator is based on the distance between the hierarchy of the particular category in the Knowledge Graph and each of the hierarchy of categories with which each of the plurality of facets is associated in the Knowledge Graph.
The search method according to any one of Supplementary note 7 to Supplementary note 9.

(Appendix 11)
The second index is based on whether or not each of the plurality of facets is a standard vocabulary.
The search method according to any one of Supplementary note 7 to Supplementary note 10.

(Appendix 12)
The output process is a search screen that is a transition destination from the screen that accepts the designation of the specific category, and is for performing a search targeting the plurality of facets associated with the specific category. The search screen includes a process of displaying the plurality of facets arranged in order according to the priority of the plurality of facets.
The search method according to any one of Supplementary note 7 to Supplementary note 11.

(Appendix 13)
A reception desk that accepts the designation of a specific category,
A first index based on the number of categories associated with each of the specific categories in the Knowledge Graph, and each of the specific category and the plurality of facets in the Knowledge Graph. A second index based on the distance between the two, and an output unit that outputs the plurality of facets in order according to the priority of the plurality of facets calculated based on at least one of them.
A search device.

(Appendix 14)
The first index includes the result of dividing the number of all categories in the Knowledge Graph by the number of categories each of the plurality of facets associated with in the Knowledge Graph.
The search device according to Appendix 13.

(Appendix 15)
The first index, which includes the results for each of the plurality of facets, is calculated.
The calculated information including the first index is stored in the storage area.
The search device according to Appendix 14, further comprising a calculation unit.

(Appendix 16)
The second indicator is based on the distance between the hierarchy of the particular category in the Knowledge Graph and each of the hierarchy of categories with which each of the plurality of facets is associated in the Knowledge Graph.
The search device according to any one of Supplementary note 13 to Supplementary note 15.

(Appendix 17)
The second index is based on whether or not each of the plurality of facets is a standard vocabulary.
The search device according to any one of Supplementary note 13 to Supplementary note 16.

(Appendix 18)
The output unit is a search screen that is a transition destination from the screen that accepts the designation of the specific category, and is the search for performing a search targeting the plurality of facets associated with the specific category. The plurality of facets arranged in order according to the priority of the plurality of facets are displayed on the screen.
The search device according to any one of Supplementary note 13 to Supplementary note 17.

1 Facet search system 10 Computer 2 Server 21 Memory section 21a Frequency table 21b Score DB
22 Search control unit 23 Statistical processing unit 23a Frequency table creation unit 23b Score calculation unit 24 Semantic interpretation processing unit 25 Ranking adjustment unit 3 KG (Knowledge graph)
4 terminals

Claims (8)

Accepts the specification of a specific category,
A first index based on the number of categories associated with each of the specific categories in the Knowledge Graph, and each of the specific category and the plurality of facets in the Knowledge Graph. The plurality of facets are sequentially output in order according to the priority of the plurality of facets calculated based on at least one of the second index based on the distance between the two.
A search program that lets a computer perform processing. The first index includes the result of dividing the number of all categories in the Knowledge Graph by the number of categories each of the plurality of facets associated with in the Knowledge Graph.
The search program according to claim 1. The first index, which includes the results for each of the plurality of facets, is calculated.
The calculated information including the first index is stored in the storage area.
The search program according to claim 2, wherein the computer executes the process. The second indicator is based on the distance between the hierarchy of the particular category in the Knowledge Graph and each of the hierarchy of categories with which each of the plurality of facets is associated in the Knowledge Graph.
The search program according to any one of claims 1 to 3. The second index is based on whether or not each of the plurality of facets is a standard vocabulary.
The search program according to any one of claims 1 to 4. The output process is a search screen that is a transition destination from the screen that accepts the designation of the specific category, and is for performing a search targeting the plurality of facets associated with the specific category. The search screen includes a process of displaying the plurality of facets arranged in order according to the priority of the plurality of facets.
The search program according to any one of claims 1 to 5. Accepts the specification of a specific category,
A first index based on the number of categories associated with each of the specific categories in the Knowledge Graph, and each of the specific category and the plurality of facets in the Knowledge Graph. The plurality of facets are sequentially output in order according to the priority of the plurality of facets calculated based on at least one of the second index based on the distance between the two.
A search method in which a computer performs processing. A reception desk that accepts the designation of a specific category,
A first index based on the number of categories associated with each of the specific categories in the Knowledge Graph, and each of the specific category and the plurality of facets in the Knowledge Graph. A second index based on the distance between the two, and an output unit that outputs the plurality of facets in order according to the priority of the plurality of facets calculated based on at least one of them.
A search device.

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