Human relationship extraction device and human relationship extraction method

Abstract

To provide a human relationship extraction apparatus for determining whether a target person is a high-risk person by considering not only basic information linked to the individual of the target person, but also the relationship between the individual and organizations and the relationship between the organizations.SOLUTION: A human relationship extraction apparatus 100 comprises a processor and a storage apparatus for storing programs and data. The storage apparatus stores known factual information input from the outside (a personnel relationship table 111, an organization-personnel relationship table 112, an organization relationship fact table 113, a BL applicable person table 115). The processor acquires, if a target person is specified, factual information linked to the individual of the target person and registers it in the storage apparatus (a target person relationship information table 116), performs first related party extraction that extracts related parties of the target person based on various factual information, performs first determination that determines whether or not the target person is a high-risk person based on whether or not a blacklisted person is included in the extracted related parties, and outputs a determination result of the first determination.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a human relationship extraction device and a human relationship extraction method, and is suitable for use in a human relationship extraction device and a human relationship extraction method for determining whether a subject is a high-risk person in various screening procedures. [Background technology]

[0002] Conventionally, in various situations such as opening an account or registering a residence certificate, a screening is conducted to determine whether or not a subject is a high-risk person. The above-mentioned "risk" may be, for example, the possibility of being involved in a crime in the near future. The screening organization that performs the above screening generally holds a blacklist that registers known high-risk persons (e.g., criminals and their associates), and it is known that high-risk persons are often connected in some way to those on the blacklist. Therefore, during screening, whether or not the subject is a high-risk person is determined by determining the relationship between the subject and those on the blacklist.

[0003] For example, Patent Document 1 discloses a technique for resolving entities based on their relationship to a common entity. Patent Document 1 discloses a method of "comparing two entities and resolving them to a single entity if the two entities satisfy an entity resolution threshold based on a set of one or more entities that are commonly related." When using Patent Document 1 to determine whether or not there is a relationship between a high-risk person and a blacklisted person, by treating the target and the blacklisted person as two entities, it is possible to determine that there is a relationship between the target and the blacklisted person if attributes such as a telephone number or address are common to both entities. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Special Publication No. 2014-529129 Summary of the Invention [Problem to be solved by the invention]

[0005] As described above, according to the technique of Patent Document 1, when the values ​​of basic information linked to an individual, such as a telephone number or address, match between a target person and a blacklisted person, it is possible to extract the relationship between the two. However, there are not many cases where the basic information matches perfectly, and it has to be said that the risk of overlooking high-risk individuals who have relationships with blacklisted people remains high.

[0006] In addition, the relationship between a high-risk person and a blacklisted person cannot be sufficiently determined based on only basic information linked to an individual, and it is assumed that the relationship may exist based on the individual's peripheral information (for example, the organization to which the person belongs, such as the place of employment), but the conventional technology was not able to extract the relationship between the subject and the blacklisted person by taking into consideration such peripheral information of the subject. Specifically, for example, if the subject's workplace also includes a blacklisted person, it was possible to extract the relationship using the conventional technology, but if the blacklisted person works at another workplace similar to the subject's workplace, the conventional technology was not able to extract the relationship.

[0007] The present invention has been made in consideration of the above points, and aims to propose a human relationship extraction device and method that are capable of determining whether a subject is a high-risk individual by taking into account not only basic information linked to an individual, but also the relationship between the individual and an organization, and even the relationship between organizations. [Means for solving the problem]

[0008] In order to solve such problems, the present invention provides a human relationship extraction device that uses a computer to determine whether a person being screened is a high-risk person having a high level of risk, the device comprising a processor that executes predetermined processing by executing a program, and a storage device that stores the program and data, the storage device stores at least information indicating factual relationships between people, information indicating factual relationships between organizations and people, information indicating factual relationships between organizations and organizations, and information indicating individuals on a blacklist, as known factual information input from outside, and the processor, when the subject is designated, obtains factual information linked to the subject and registers it in the storage device, further registering organizational relationship analogy information indicating an analogy relationship between organizations inferred from the factual information in the storage device; Based on the various factual information stored in the storage device, 1. performing a first participant extraction to extract participants; execute a second related person extraction to extract, based on the organizational relationship inference information, a person who has a history of affiliation to an organization that has the analogous relationship with an organization to which the target person has a history of affiliation, as a second related person of the target person, in addition to the first related person extracted by the first related person extraction; The above extracted in the first related party extraction 1. execute a first determination as to whether the subject is a high-risk person based on whether the relevant person includes a person on the blacklist; Calculating an association score indicating the degree to which the second related person extracted in the second related person extraction is included in the blacklist, and performing a second determination to determine whether the subject is a high-risk person based on the calculated association score, and if a determination result that the subject is a high-risk person is obtained in at least one of the first and second determinations, making a final determination that the subject is a high-risk person, and The first and the second judgment Judgment result and The human relationship extraction device outputs the following:

[0009] In addition, in order to solve such problems, the present invention provides a human relationship extraction method by a human relationship extraction device that determines whether or not a person being examined is a high-risk person having a high level of risk, the computer constituting the human relationship extraction device has a processor that executes a predetermined process by executing a program, and a storage device that stores the program and data, the storage device stores at least information indicating a factual relationship between people, information indicating a factual relationship between organizations and people, information indicating a factual relationship between organizations and organizations, and information indicating a person on a blacklist, as known factual information input from outside, and the processor, when the person being examined is designated, obtains factual information linked to the person being examined and registers it in the storage device. Facts The registration step, an inference information registration step in which the processor further registers, in the storage device, organizational relationship inference information indicating an inferred relationship between organizations inferred from the fact information; The processor ,before Based on the various factual information stored in the memory device, 1. The first stakeholder extraction Out Tep and a second related person extraction step in which the processor extracts, as a second related person of the subject, a person who has a history of affiliation to an organization that has the analogous relationship with an organization to which the subject has a history of affiliation, based on the organizational relationship inference information registered in the analogous information registration step, in addition to the first related person extracted in the first related person extraction step; The processor 1. The above extracted in the stakeholder extraction step 1. A first judgment is made to determine whether the subject is a high-risk person based on whether the relevant person includes a person on the blacklist. Fixed Tep and a second determination step in which the processor calculates an association score indicating the degree to which the second related person extracted in the second related person extraction step is included in the blacklist, and determines whether the subject is a high-risk person based on the calculated association score; and a final determination step in which the processor makes a final determination that the subject is a high-risk person when a determination result that the subject is a high-risk person is obtained in at least one of the first and second determination steps; The processor, A judgment result in the final judgment step; The first and in the second determination step Judgment result and and outputting the above-mentioned output step. Effect of the Invention

[0010] According to the present invention, it is possible to determine whether a subject is a high-risk individual by taking into consideration not only basic information linked to the individual, but also the relationship between the individual and an organization, and the relationship between organizations. [Brief description of the drawings]

[0011] [Figure 1] 1 is a block diagram showing an example of the configuration of a human relationship extraction device 100 according to an embodiment of the present invention. [Diagram 2] 1 is a block diagram showing an example of a hardware configuration of a computer 10 that realizes a human relationship extraction device 100. FIG. [Diagram 3] FIG. 2 is a diagram showing an example of a human relationship table 111. [Figure 4] FIG. 2 is a diagram showing an example of an organization-person relationship table 112. [Diagram 5] FIG. 13 is a diagram showing an example of an organization relationship fact table 113. [Figure 6] FIG. 13 is a diagram showing an example of a person basic information table 114. [Figure 7] FIG. 13 is a diagram showing an example of a BL matched person table 115. [Figure 8] FIG. 13 is a diagram showing an example of a target relationship information table 116. [Figure 9] FIG. 2 is a diagram showing an example of an individual similarity table 117. [Figure 10] FIG. 13 is a diagram showing an example of an organizational relationship inference table 118. [Figure 11] FIG. 13 is a diagram showing an example of an extraction result table 119. [Figure 12] 13 is a flowchart showing an example of a processing procedure for human relationship extraction processing. [Figure 13] 1 is a schematic diagram showing an example of a connection relationship between subject A and the surrounding environment. [Figure 14] FIG. 2 is a schematic diagram showing an example of the connection relationship between subject A and the extended surrounding environment. [Figure 15] FIG. 13 is a diagram showing an example of a determination result screen. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] An embodiment of the present invention will be described below with reference to the drawings. Note that the embodiment described below does not limit the invention according to the claims, and not all of the combinations of features described in the embodiment are necessarily essential to the solution of the invention. In the following description, various information may be described using expressions such as "table", "list", and "queue", but the various information may be expressed using data structures other than these. In order to indicate that the information does not depend on the data structure, "XX table", "XX list", and the like may be referred to as "XX information". When describing the contents of each piece of information, expressions such as "identification information", "identifier", "name", "ID", and "number" are used, but these are mutually interchangeable.

[0013] (1) Equipment configuration Fig. 1 is a block diagram showing an example of the configuration of a human relationship extraction device 100 according to an embodiment of the present invention. As shown in Fig. 1, the human relationship extraction device 100 includes an input relationship registration unit 101, an organizational relationship analogy unit 102, a related person extraction unit 103, and a high-risk person determination unit 104 as processing units providing predetermined functions, and includes a human relationship table 111, an organization-human relationship table 112, an organizational relationship fact table 113, a human basic information table 114, a BL corresponding person table 115, a target person relationship information table 116, an individual similarity table 117, an organizational relationship analogy table 118, and an extraction result table 119 as various data for holding predetermined information.

[0014] The human relationship extraction device 100 is a device that determines whether or not a subject is a high-risk person who has a high level of risk by executing a human relationship extraction process that extracts relationships between a subject to be examined and blacklisted persons (BL subjects) registered in a blacklist (BL). Details will be described later with reference to Fig. 12 and the like, but in the human relationship extraction process, based on relationships between people, between organizations, and between organizations shown from factual information, and relationships between organizations inferred from factual information, it extracts related persons of the subject from the surrounding environment, and determines whether or not the subject is a high-risk person based on whether or not the related persons are BL subjects, and visualizes the determination result, thereby detecting high-risk persons with a high degree of accuracy.

[0015] The input relationship registration unit 101 has a function of accepting input of various factual information, registering the input factual information in various data (person relationship table 111, organization-person relationship table 112, organization relationship fact table 113, person basic information table 114, BL applicable person table 115), and storing it in a storage device (memory 12 or storage device 13 shown in FIG. 2). The organizational relationship analogy unit 102 has a function of analogizing the relationship between organizations and registering the analogy result in the organizational relationship analogy table 118. The related person extraction unit 103 has a function of registering basic information linked to the subject to be determined in the subject relationship information table 116, and extracting people (related persons) who have a relationship with the subject based on the various registered data. The extraction result of related persons by the related person extraction unit 103 is registered in the extraction result table 119. The high-risk person determination unit 104 has a function of determining whether the subject is a high-risk person or not based on the extraction result of related persons by the related person extraction unit 103, and outputting the determination result to the user.

[0016] Details of various data held by the human relationship extraction device 100 will be described later with reference to Figs. 3 to 11. The human relationship table 111, organization-human relationship table 112, organization relationship fact table 113, human basic information table 114, and BL applicable person table 115 are data about factual information already known about people and organizations, which are input and registered in the human relationship extraction device 100 before a determination is made as to whether or not the subject is a high-risk person. The subject relationship information table 116 is data about factual information about a subject of human relationship extraction, which is input and registered in the human relationship extraction device 100 when the subject is designated. The individual similarity table 117, organization relationship analogy table 118, and extraction result table 119 are data generated and registered during human relationship extraction processing.

[0017] FIG. 2 is a block diagram showing an example of the hardware configuration of the computer 10 that realizes the human relationship extraction device 100. As shown in FIG.

[0018] The computer 10 is configured by a CPU 11, a memory 12, a storage device 13, an input device 14, an output device 15, and a network device 16, all connected to one another via a bus 17.

[0019] The CPU 11 is a processor that executes a program, and is, for example, a CPU (Central Processing Unit). The memory 12 is a main storage device, such as a RAM (Random Access Memory), that stores programs and data referenced when the programs are executed. The storage device 13 is an auxiliary storage device that stores programs and data referenced when the programs are executed, and is, for example, a HDD (Hard Disk Drive) or an SSD (Solid State Drive). The input device 14 is a device that accepts input from an operator, and is, for example, a keyboard, a mouse, or a touch panel. The output device 15 is a device that outputs the results of processing executed by the CPU 11, or data held in the memory 12 or the storage device 13, and is, for example, a liquid crystal display device or an organic EL (Electro Luminescence) display. The network device 16 is various interfaces, such as a communication interface and an input / output interface. The bus 17 is an internal communication line that connects each device in the computer 10.

[0020] Each processing unit of the human relationship extraction device 100 shown in Fig. 1 is realized by executing a processing program corresponding to the function of each processing unit by the hardware resources of the computer 10. Specifically, the input relationship registration unit 101, the organizational relationship analogy unit 102, the related person extraction unit 103, and the high-risk individual determination unit 104 shown in Fig. 1 are realized by the CPU 11 executing a predetermined program stored in the memory 12 or the storage device 13 while referring to related data. The high-risk individual determination unit 104 is realized by the CPU 11 executing a predetermined program and outputting the execution result (determination result screen 120 in Fig. 15 described later) to the output device 15 (or a user-side terminal).

[0021] In addition, the various data of the human relationship extraction device 100 shown in Figure 1 (human relationship table 111, organization-human relationship table 112, organizational relationship fact table 113, human basic information table 114, BL matched person table 115, target person relationship information table 116, personal similarity table 117, organizational relationship inference table 118, and extraction result table 119) are realized by the memory 12 or the storage device 13.

[0022] The above-mentioned programs executed by the CPU 11 in the computer 10 and some or all of the data referenced by the programs may be stored in advance in the memory 12 or the storage device 13, or may be configured to be stored in the storage device 13 of the computer 10 from a non-transitory storage device or a non-transitory storage medium of another device connected via a network via the network device 16, as necessary. In addition, in the following description, for simplicity, the human relationship extraction device 100 is configured to be realized by one computer 10, but the configuration of the human relationship extraction device 100 according to this embodiment is not limited to this, and may be realized by multiple computers 10, or may be realized by including a database device, cloud storage, etc.

[0023] (2) Data Structure 3 is a diagram showing an example of the person relationship table 111. The person relationship table 111 is a table that holds information showing the factual relationship between people, and has columns of a person 1111, a related person 1112, and a relationship 1113. In the person relationship table 111, a relationship 1113 between the person 1111 and the related person 1112 is recorded for each person 1111. In this embodiment, regarding the factual relationship between people, for example, when person A and person B are in a family relationship or when person A and person B have the same address, a relationship is established, and in the above cases, "family" or "living together" is written in the relationship 1113, respectively.

[0024] FIG. 4 is a diagram showing an example of the organization-person relationship table 112. The organization-person relationship table 112 is a table that holds information indicating a factual relationship between an organization and a person, and has columns of an organization 1121, a person 1122, a relationship 1123, a relationship start 1124, and a relationship end 1125. In the organization-person relationship table 112, for each organization 1121, a relationship 1123 for a person 1122 that is related to (or was related to) the organization, and further, a start time (relationship start 1124) and an end time (relationship end 1125) of the relationship are recorded. In this embodiment, for example, when a person A belongs to an organization P for a certain period of time, the relationship is considered to exist, and in this case, "affiliation" is written in the relationship 1123. Specifically, for example, the record in the first row of FIG. 4 indicates that a person C belonged to the organization P from April 2017 to March 2019. The column value of the relationship 1123 can be set appropriately to something other than "affiliation". For example, if the person was directly employed, "affiliation" may be written, and if the person worked under a dispatch contract or contracted work, "dispatch" or "contracted work" may be written. Furthermore, the relationship 1123 may be written by distinguishing the position (job title, etc.) in the organization 1121.

[0025] 5 is a diagram showing an example of the organizational relationship fact table 113. The organizational relationship fact table 113 is a table that holds information indicating a factual relationship between organizations, and has columns of a corresponding organization 1131, a related organization 1132, and a relationship 1133. In the organizational relationship fact table 113, a related organization 1132 and a relationship 1133 with the related organization are recorded for each corresponding organization 1131. In this embodiment, regarding the factual relationship between organizations, for example, when organizations P and Q are group companies, or when organizations P and Q have the same representative, it is considered that there is a relationship, and in the above cases, "group company" or "same representative" or the like is written in the relationship 1133, respectively.

[0026] 6 is a diagram showing an example of the person basic information table 114. The person basic information table 114 is a table that holds basic information linked to an individual, and has columns of name 1141, nationality 1142, occupation 1143, address 1144, age 1145, and place of employment 1146.

[0027] 7 is a diagram showing an example of the BL hit person table 115. The BL hit person table 115 is a table that holds registration information of the blacklist, and lists the names of BL hit people.

[0028] FIG. 8 is a diagram showing an example of the target relationship information table 116. The target relationship information table 116 is a table that holds basic information linked to an individual target as factual information related to the target, and has columns of related party 1161, category 1162, relationship 1163, relationship start 1164, and relationship end 1165. The registered contents of the target relationship information table 116 are input together with the designation of the target in the human relationship extraction process. The related party 1161 describes a target that is directly related to the target, and includes not only "person" but also "organization" (the organization to which the target belongs). The category 1162 describes the type of the related party 1161, which is "person" or "organization". The relationship 1163 describes the relationship between the target and the related party 1161. Then, the relationship start 1164 and the relationship end 1165 describe the period during which the target belonged to the organization when an organization is described in the related party 1161 (in other words, the category 1162 is "organization").

[0029] 9 is a diagram showing an example of the individual similarity table 117. The individual similarity table 117 is a table that holds individual similarities that indicate inferred relationships between people. Although details will be described later, the individual similarity is calculated by the organizational relationship inference unit 102 based on information belonging to each individual registered in the person basic information table 114. In this example, the individual similarity is expressed as a numerical value in the range of "0.0" to "1.0", and the closer the value is to 1.0, the higher the similarity (there is inferred to be a relationship).

[0030] FIG. 10 is a diagram showing an example of the organizational relationship analogy table 118. The organizational relationship analogy table 118 is a table that holds information showing an analogy relationship between organizations, and has columns of a corresponding organization 1181, a related organization 1182, and an organizational similarity 1183. The corresponding organization 1181 and the related organization 1182 are organizations that have an analogy relationship, and the organizational similarity 1183 is a similarity (organizational similarity) of the corresponding organization 1181 to the related organization 1182. Although details will be described later, the organizational similarity is calculated by the organizational relationship analogy unit 102 based on the personal similarity of people belonging to each organization, and a combination of organizations having a predetermined organizational similarity or higher is recorded in the organizational relationship analogy table 118. In this example, the organizational similarity is expressed as a numerical value in the range of "0.0" to "1.0", and the closer the value is to 1.0, the higher the similarity (it is inferred that there is a relationship). In this embodiment, an analogy relationship between organizations is inferred when, for example, the attribute information of person A belonging to organization P and person B belonging to organization Q is similar, or when organizations P and Q have similar financial information, corporate size, or products handled.

[0031] FIG. 11 is a diagram showing an example of the extraction result table 119. The extraction result table 119 is a table that holds information on the extraction result of related persons of the target person, and is registered by the related person extraction unit 103. The extraction result table 119 has columns of related person 1191, BL applicability 1192, factual relationship 1193, analogical relationship 1194, and related organization 1195. The related person 1191 records a person extracted as a related person of the target person. The BL applicability 1192 records whether the related person is a BL related person, the factual relationship 1193 records whether the related person has a direct relationship with the target person based on factual information, and the analogical relationship 1194 records whether the related person has an analogical relationship with the target person based on analogical information. In addition, the related organization 1195 records an organization to which the target person or related person belongs (or belonged).

[0032] (3) Human Relationship Extraction Processing The human relationship extraction process executed by the human relationship extraction device 100 will be described in detail below with appropriate reference to specific data examples in Fig. 3 to Fig. 11. The human relationship extraction process is a process in which, when a target person is specified, the human relationship extraction device 100 extracts related persons who have a strong relationship with the target person and compares them with the blacklisted persons to extract the relationship between the target person and the blacklisted persons, and determines whether the target person is a high-risk person or not.

[0033] Fig. 12 is a flowchart showing an example of the processing procedure of the human relationship extraction processing. In Fig. 12, the processing of steps S1 and S2 is executed at any timing before a target person is specified. On the other hand, the processing of steps S3 to S5 is executed after a target person is specified and relationship information of the target person (information to be registered in target person relationship information table 116) is input. In this explanation, it is assumed that "target person A" is specified.

[0034] (3-1) Input-related registration First, in step S1, the input relationship registration unit 101 registers information indicating various pre-prepared factual relationships in the person relationship table 111, the organization-person relationship table 112, the organization relationship fact table 113, the person basic information table 114, and the BL matched person table 115.

[0035] (3-2) Analogy of organizational relationships Next, in step S2, the organizational relationship inference unit 102 infers relationships between organizations. In step S2, the organizational relationship inference unit 102 calculates the similarity between organizations (organizational similarity) based on the similarity of people belonging to each organization (individual similarity), infers that there is an organizational relationship when the organizational similarity exceeds a predetermined threshold, and registers the inference result in the organizational relationship inference table 118.

[0036] Here, the process of step S2 will be described in detail.

[0037] First, the organizational relationship inference unit 102 extracts attribute information (for example, nationality 1142, occupation 1143, address 1144, and age 1145) belonging to an individual from the person basic information table 114, and calculates the similarity between individuals (individual similarity) using a predetermined calculation method based on the extracted attribute information. The calculation method of the individual similarity is not limited to a specific method, but for example, the ratio (degree) of matching of the extracted attribute information between the target individuals is set as the individual similarity. Specifically, in the case of the combination of X1 and X3 shown in the person basic information table 114 of FIG. 6, two items of nationality "Country A" and address "Tokyo" match among the four items of attribute information of nationality, occupation, address, and age, so the individual similarity is 0.5 (2 / 4). The organizational relationship inference unit 102 executes such calculation of the individual similarity for all combinations of people registered in the person basic information table 114. The individual similarity table 117 shown in FIG. 9 is a data table in which the calculation results of these individual similarities are recorded together.

[0038] Next, for each organization listed in the workplace 1146 of the person basic information table 114, the organizational relationship inference unit 102 extracts pairs having a personal similarity equal to or greater than a predetermined threshold (e.g., 0.8) from among combinations of people belonging to different organizations. Specifically, in the case of the person basic information table 114 of FIG. 6 and the personal similarity table 117 of FIG. 9, one pair of X1 belonging to company P and X4 belonging to company R is extracted. The personal similarity of the pair is 1.0. Next, the organizational relationship inference unit 102 calculates the ratio (degree) of people extracted as a pair to the total number of people in each organization as the organizational similarity of the organization. In the above specific example, according to the person basic information table 114, there are two people, X1 and X2, who belong to company P, and the only person extracted as a pair from company P is X1, so the organizational similarity of company P to company R is 0.5 (1 / 2). The organizational similarity based on Company R (i.e., the organizational similarity of Company R to Company P) is 0.33 (1 / 3) because there are three people (X3, X4, X5) who belong to Company R. In this way, by calculating the organizational similarity of each organization with the other organization, it is possible to calculate similarity that takes the size of the organizations into account.

[0039] Then, the organizational relationship inference unit 102 regards those organizational similarities calculated for each organization that are equal to or greater than a predetermined threshold (e.g., 0.3) as inferring an inter-organizational relationship, and registers them in the organizational relationship inference table 118. In the above specific example, the organizational similarity of company P to company R, "0.5," and the organizational similarity of company R to company P, "0.33," are both equal to or greater than the predetermined threshold "0.3," and are therefore registered in the organizational relationship inference table 118 as shown in FIG.

[0040] As described above, in step S2, the organizational relationship inference unit 102 infers the relationships between organizations, and the inference results are registered in the organizational relationship inference table 118.

[0041] (3-3) Extraction of related parties based on factual information Next, in step S3, the related person extraction unit 103 extracts people who have a relationship (direct relationship) based on factual information with the target person A specified by the user, as related persons of the target person A (first related person extraction).

[0042] Here, the process of step S3 will be described in detail. In step S3, the related person extraction unit 103 executes the following first to fourth stages of processing in order.

[0043] FIG. 13 is a schematic diagram showing an example of a connection relationship between subject A and the surrounding environment. FIG. 13 shows an example of a direct relationship that is clarified from factual information about the surrounding environment of subject A, and the notation "family" or "affiliation" indicates the relationship between the subjects. In addition, the number written below the notation "affiliation" indicates the period of the affiliation. Below, the first related person extraction will be explained using the relationship shown in FIG. 13 as a specific example.

[0044] In the first stage, the related person extraction section 103 registers the factual information about the subject A, which was input when the subject A was designated, in the subject relationship information table 116.

[0045] In the second stage, the related party extraction unit 103 extracts "people" related to the subject A based on the subject relationship information table 116 and the various relationship tables registered in step S1 (person relationship table 111, organization-person relationship table 112, organization relationship fact table 113). All information in each table referenced in this second stage is fact information that does not include analogies.

[0046] The second stage processing will be specifically described with reference to the tables in FIG. 3, FIG. 4, FIG. 5 and FIG.

[0047] First, the related person extraction unit 103 refers to the target person relationship information table 116 (Fig. 8) and confirms that the related person 1161 in the first record is "B" and the category 1162 is "person", then it searches the person relationship table 111 (Fig. 3) and the organization-person relationship table 112 (Fig. 4) using "B" as a key, and attempts to extract a target (which may be a person or an organization) related to "B". However, since there is no information about "B" in the person relationship table 111 in Fig. 3 and the organization-person relationship table 112 in Fig. 4, a target related to "B" is not extracted.

[0048] Next, the related party extraction unit 103 refers to the second record in the target person relationship information table 116, and when it confirms that the related party 1161 is "P" and the category 1162 is "organization", it searches the organization-person relationship table 112 (FIG. 4) and the organization relationship fact table 113 (FIG. 5) using "P" as a key, and attempts to extract targets (which may be people or organizations) related to "P". In this case, Q, C, and D are extracted as targets related to "P". Furthermore, if any targets are extracted, the related party extraction unit 103 repeats the same search using each of the extracted targets as a key.

[0049] Then, the related person extraction unit 103 selects "people" from the multiple targets extracted by repeatedly executing the above search, and sets these (in this example, B, C, D, and E) as candidates for related persons of target A.

[0050] In the third stage, the related party extraction unit 103 checks the period of affiliation to the organization of each of the candidates of related parties of the subject A extracted in the second stage, and excludes candidates whose period of affiliation to the organization of the subject A does not overlap with the period of affiliation to the organization of the subject A from the related parties of the subject A. Specifically, according to the subject relationship information table 116 of FIG. 8, the period during which the subject A belonged to the organization P was from April 2018 to March 2019. On the other hand, according to the organization-person relationship table 112 of FIG. 4, the period during which D belonged to the organization P was from April 2013 to March 2016, and the affiliation periods do not overlap. Note that C and E are not excluded because there is no factual information regarding their affiliation to the organization. Therefore, by the processing of the third stage, D is excluded, and B, C, and E are regarded as related parties of the subject A. Note that the candidates to be excluded in the third stage may include not only candidates whose period of affiliation to the organization does not overlap with the subject, but also candidates subordinate to the candidate (for example, family members).

[0051] In the fourth stage, the related person extraction section 103 registers in the extraction result table 119 the related persons of the subject A extracted at the end of the third stage.

[0052] In detail, the related party extraction unit 103 creates a new record for each related party in the extraction result table 119, registers the related party in related party 1191, and registers "○" in factual relationship 1193. Furthermore, in the fourth stage, the related party extraction unit 103 collates the related parties of the target person A with the BL pertinent person table 115 (FIG. 7) to confirm whether or not there is a BL pertinent person, and registers the confirmation result in BL pertinent 1192 of the corresponding record in the extraction result table 119. Specifically, in the fourth stage of step S3, four people, B, C, E, and F, are registered in the extraction result table 119 as related parties of the target person A. Of these, in the record of C who is a BL pertinent person, "○" is registered in the column value of BL pertinent 1192, and in the records of B, D, and E who are not BL pertinent people, "-" is registered in the column value of BL pertinent 1192. Further, in the associated organization 1195 of each record, the organization to which the subject or related person belongs (or belonged) is registered.

[0053] As described above, in step S3, the first related person extraction can extract related people who have a direct relationship with the target person A based on factual information, and the extraction result can be registered in the extraction result table 119.

[0054] (3-4) Extraction of related parties based on analogical information Next, in step S4, the related party extraction unit 103 extracts people who have relationships based on analogical information (analogous relationships) as related parties of the subject A, in addition to people who have relationships based on the factual information extracted in step S3 (direct relationships) (second related party extraction).

[0055] Here, the process of step S4 will be described in detail. In step S4, the related person extraction section 103 executes the following first to fourth stages of processing in order.

[0056] FIG. 14 is a schematic diagram showing an example of a connection relationship between subject A and the expanded surrounding environment. In addition to the direct relationship shown in FIG. 13, FIG. 14 shows an example of an analogical relationship that becomes clear from analogical information regarding the surrounding environment of subject A. As shown in the legend, in FIG. 14, a direct relationship based on factual information is represented by a solid line, and an analogical relationship based on analogical information is represented by a dashed line. The other notation methods in FIG. 14 are the same as those in FIG. 13. Below, the second related person extraction will be explained using the relationship shown in FIG. 14 as a specific example.

[0057] In the first stage, the related party extraction unit 103 focuses on the organization extracted in the second stage of step S3 (first related party extraction) and extracts organizations that have an analogical relationship with the organization from the organizational relationship inference table 118. Specifically, since organization P was extracted in the second stage of the first related party extraction, the related party extraction unit 103 extracts organizations R and S as organizations that have an analogical relationship with organization P by referring to related organization 1182 of a record in which "P" is entered in corresponding organization 1181 in the organizational relationship inference table 118 of FIG. 10. Note that in the first stage, the related party extraction unit 103 may acquire related parties based on direct relationships extracted in step S3 (first related party extraction) as related parties of the extracted target person A.

[0058] In the second stage, based on the organization-person relationship table 112 and the person relationship table 111, the related person extraction unit 103 extracts people who have a direct relationship with the organizations extracted in the first stage as candidates for related persons who have an analogical relationship with the target person A. Specifically, from the organization-person relationship table 112 in Fig. 4, R1 and R2 are extracted as people who have a history of belonging to organization R, and S1 and S2 are extracted as people who have a history of belonging to organization S. Note that since people related to R1, R2, S1, and S2 are not registered in the person relationship table 111 in Fig. 3, other people are not extracted.

[0059] In the third stage, the related person extraction unit 103 checks the period of affiliation to the organization of each of the candidates for related persons based on the analogical relationship of the target person A extracted in the second stage, and excludes candidates whose period of affiliation to the organization of the target person A does not overlap with the period of affiliation to the organization of the target person A from the related persons based on the analogical relationship of the target person A. Specifically, the affiliation periods of R1, R2, S1, and S2 to the organization R or organization S can be read from the organization-person relationship table 112 in FIG. 4, and the related person extraction unit 103 compares these with the period of affiliation of the target person A to the organization P, which is "April 2018 to March 2019." As a result, since the period during which R1 and S1 belonged to the organization does not overlap with the period during which the target person A belonged to the organization P, R1 and S1 are excluded from the related persons based on the analogical relationship of the target person A. That is, by the processing of the third stage, R2 and S2 are regarded as related persons based on the analogical relationship of the target person A. The targets for exclusion in the third stage may include not only candidates whose period of affiliation to the organization does not overlap with that of the target candidate, but also candidates subordinate to the target candidate (for example, family members).

[0060] In the fourth stage, the related person extraction section 103 registers in the extraction result table 119 the related persons of the subject A extracted at the end of the third stage.

[0061] In detail, the related party extraction unit 103 creates a new record for each related party in the extraction result table 119, registers the related party in related party 1191, registers "-" in factual relationship 1193, and registers "○" in analogical relationship 1194. Furthermore, in the fourth stage, the related party extraction unit 103 collates the related parties of the target A newly registered in the extraction result table 119 with the BL corresponding party table 115 (FIG. 7), confirms whether or not there is a BL corresponding party, and registers the confirmation result in BL corresponding party 1192 of the corresponding record in the extraction result table 119. Specifically, in the fourth stage of step S4, R2 and S2 are registered in the extraction result table 119 as new related parties, and according to the BL corresponding party table 115 in FIG. 7, both R2 and S2 are BL corresponding parties. Therefore, in the records of R2 and S2 in the extraction result table 119, "○" is registered in the column value of BL corresponding party 1192. In addition, the organization to which the subject and related persons belong (or belonged) is registered in the related organizations 1195 of each record. Note that in the extraction result table 119, four persons B, C, E, and F are registered as related persons through direct relationships with the subject A in the fourth stage of step S3, and "○" is also registered in the analogical relationship presence / absence 1194 of the records of these related persons.

[0062] As described above, in step S4, related persons having an analogical relationship with the target person A can be extracted based on the analogy information by the second related person extraction, and the extraction result can be additionally registered in the extraction result table 119.

[0063] (3-4) Identification of high-risk individuals Next, in step S5, the high-risk individual determination unit 104 refers to the extraction result table 119 registered in steps S3 and S4 to determine whether or not subject A is a high-risk individual, and then outputs the final determination result to the user, and terminates the human relationship extraction process.

[0064] Here, the high-risk individual determination in this embodiment includes the first and second determinations described below.

[0065] First, in the first judgment, the high-risk person judgment unit 104 uses the extraction result of the first related person extraction in step S3 to judge whether or not subject A is a high-risk person based on whether or not there is a BL person among the related persons who have a strong relationship (direct relationship) with subject A based on factual information.

[0066] Specifically, in the first determination, the high-risk individual determination unit 104 refers to the extraction result table 119 and checks whether there is a record in which both the BL applicability 1192 and the factual relationship 1193 are marked with "○," thereby determining whether related parties having a direct relationship with the subject A are BL related parties. In the case of the extraction result table 119 in FIG. 11, referring to the factual relationship 1193, it is found that B, C, E, and F are related parties having a direct relationship, and referring to the BL applicability 1192, it is found that C is a BL related party among these related parties. In other words, since the subject A has a direct relationship with C who is a BL related party, the high-risk individual determination unit 104 determines the subject A to be a high-risk person in the first determination.

[0067] According to this type of first determination, when subject A has a direct relationship with a BL relevant person, subject A can be determined to be a high-risk individual from the viewpoint that subject A has a strong relationship with the BL relevant person.

[0068] Next, in the second judgment, the high-risk individual judgment unit 104 uses the extraction results of the first related person extraction in step S3 or the second related person extraction in step S4 to judge whether or not subject A is a high-risk individual based on the extent to which BL relevant individuals are present in subject A's surrounding environment.

[0069] In the following, the second determination will be described in detail, with related parties of the subject A who are not only directly related but also extended to analogical relationships (i.e., related parties extracted in the first and second related party extractions) as the "surrounding environment of the subject A". As another example of the second determination, the human relationship extraction device 100 according to the present embodiment may perform the second determination with only related parties who have a direct relationship with the subject A (i.e., related parties extracted in the first related party extraction) as the "surrounding environment of the subject A", in which case it is not necessary to execute the process of step S4 (second related party extraction) in the human relationship extraction process.

[0070] In the second determination, the high-risk person determination unit 104 calculates a BL relevance score based on the proportion of BL relevant persons among the total number of related persons extracted as related persons of the target person A, and determines the target person A to be a high-risk person when the calculated BL relevance score is equal to or greater than a predetermined threshold. Specifically, the high-risk person determination unit 104 can obtain the total number of related persons from the total number of records by referring to the extraction result table 119, and can obtain the number of BL relevant persons from the number of records in which "○" is registered in the BL relevantness 1192. In the case of the extraction result table 119 in FIG. 11, the total number of related persons is six (B, C, E, F, R2, S2), of which three are BL relevant persons (C, R2, S2), so the high-risk person determination unit 104 calculates the BL relevance score of the target person A to be 0.5 (3 / 6). Here, if a predetermined threshold value set in advance for the BL relevance score is, for example, 0.4, since the BL relevance score of subject A is above the threshold value, the high-risk individual determination unit 104 determines that subject A is a high-risk individual in the second determination.

[0071] According to this second judgment, even if subject A does not have a direct relationship with a BL person, if there are a large number of BL people in subject A's surrounding environment (including, for example, by analogy), subject A can be judged to be a high-risk person from the perspective that there is a high possibility that subject A is related to a BL person.

[0072] In addition, when determining whether or not a person is high-risk, before making the first and second determinations described above, the high-risk person determination unit 104 may refer to the BL relevant person table 115 to confirm whether or not subject A is a BL relevant person, and if subject A is a BL relevant person, may determine that he or she is a high-risk person.

[0073] Then, the high-risk individual determination unit 104 displays a determination result screen based on the results of the above-mentioned first and second determinations, thereby notifying the user, who is the examiner, not only of the final determination result as to whether or not subject A is a high-risk individual, but also of the detailed determination results of each determination and any necessary countermeasures.

[0074] Fig. 15 is a diagram showing an example of a determination result screen. The determination result screen 120 shown in Fig. 15 is a display example of the determination result screen output in step S5, and is displayed on the display of the human relationship extraction device 100 or the user terminal.

[0075] 15, the final determination result of the high-risk individual determination is displayed in area 121 of determination result screen 120. When the high-risk individual determination unit 104 determines that the subject is a high-risk individual in at least either the first determination or the second determination, it displays "Corresponding to high-risk individual" in area 121.

[0076] In the area 122 of the judgment result screen 120, a detailed judgment result based on the first judgment and a recommended measure for dealing with the result are displayed. Specifically, when the high-risk individual judgment unit 104 judges the subject A as a high-risk individual in the first judgment, it checks "BL applicable" in the area 122 and displays that the subject A is related to the BL applicable individual and confirmation is required. Furthermore, the high-risk individual judgment unit 104 displays in the area 122 a related person of the BL applicable individual who has a direct relationship with the subject A (for example, C in this example), and the relationship with the subject A. These display contents can be generated, for example, from information of a record in which "○" is registered in both the BL applicable or not 1192 and the factual relationship or not 1193 in the extraction result table 119.

[0077] In the area 123 of the judgment result screen 120, a detailed judgment result based on the second judgment and a recommended measure for dealing with the result are displayed. Specifically, when the high-risk person judgment unit 104 judges the subject A as a high-risk person in the second judgment, it checks the "BL-related person relevance" in the area 122 and displays that the subject A has a high relationship with the BL-related person and therefore confirmation is required. In addition, the high-risk person judgment unit 104 displays in the area 123 the BL-relatedness score of the subject A calculated in the second judgment, the total number of related persons (number of subjects) and the number of BL-related persons in the calculation. In this example, as described above, the BL-relatedness score is 0.5, the total number of related persons is 6, and the number of BL-related persons is 3. In addition, in the area 123, the high-risk person judgment unit 104 displays, as information on organizations that require confirmation, the organization to which the subject A belongs, the organization to which the BL-related person who has an analogical relationship with the subject A belongs, the total number of related persons (number of subjects) and the number of BL-related persons in the organization to which the BL-related person belongs. In this example, the organization to which subject A belongs is organization P, and the BL relevant persons (R2, S2) who have an analogical relationship with subject A are organizations R and S. The total number of related persons in organization R is only R2, who is a BL relevant person, and the total number of related persons in organization S is only S2, who is a BL relevant person. These display contents can be generated, for example, from the information of a record in extraction result table 119 in which "X" is registered in factual relationship presence / absence 1193 and "O" is registered in analogical relationship presence / absence 1194.

[0078] In addition, on the determination result screen 120, specific information may be highlighted, for example, by displaying the organization to which the BL matched person belongs in the area 122 or area 123 in a different color or font from the others.

[0079] By outputting (displaying) the determination result screen 120 as described above, the user can know whether or not the subject is a high-risk person as a result of analyzing the related people extracted while taking into account the surrounding environment of the subject. Furthermore, such a determination result screen 120 not only presents detailed information on the first and second determinations that determined whether or not the subject is a high-risk person, but also items that require confirmation. Therefore, the user, who is the examiner, can reliably understand points that should be stopped in the examination, and can prevent high-risk people from being overlooked.

[0080] As described above, the human relationship extraction device 100 according to the present embodiment can extract related parties who have a direct relationship with the subject based on factual information, and if the related parties include a BL person, determine the subject as a high-risk supporter in the first determination. At this time, the factual information includes not only basic information linked to the individual (person basic information table 114, subject relationship information table 116), but also the relationship between the individual and the organization (organization-person relationship table 112) and the relationship between organizations (organization relationship fact table 113), so that related parties can be extracted taking into account the surrounding environment of the subject. Therefore, the human relationship extraction device 100 according to the present embodiment can accurately identify high-risk individuals who are suspected of having a direct connection with a BL person.

[0081] Furthermore, according to the human relationship extraction device 100 of this embodiment, related parties having a direct or analogical relationship (indirect relationship) with the target can be extracted based on factual information or analogical information, and based on the ratio of BL corresponding persons among these related parties, it can be determined in the second determination whether the target is a high-risk person. Since the analogical information includes information indicating an analogical relationship between organizations (organizational relationship analogy table 118), related parties can be extracted by using the analogical information. This method of determining high-risk persons by the second determination takes into consideration the fact that when the ratio of BL corresponding persons is high in the surrounding environment of the target, the presence of brokers or the like who support crimes is assumed. Therefore, according to the human relationship extraction device 100 of this embodiment, even if a direct relationship between the target and the BL corresponding person is not found, it is possible to accurately identify high-risk persons who are suspected of having an indirect (environmental) connection with the BL corresponding person.

[0082] In addition, in the human relationship extraction device 100 of this embodiment, in the first and second judgments to determine high-risk individuals, a process is performed to exclude from related parties those whose period of affiliation to the organization does not overlap with that of the target person, thereby improving the accuracy of extraction of related parties. [Explanation of symbols]

[0083] 10. Computers 11 CPU 12. Memory 13 Storage Devices 14 Input Devices 15 Output Devices 16 Network Devices 17 Bus 100 Human Relationship Extraction Device 101 Input related registration section 102 Organizational Relationship Analogy Department 103 Related Party Extraction Unit 104 High-Risk Person Judgment Department 111 Person Relationship Table 112 Organization-Person Relationship Table 113 Organizational Relationship Fact Table 114 Person Basic Information Table 115 BL Applicant Table 116 Subject Relationship Information Table 117 Personal Similarity Table 118 Organizational Relationship Analogy Table 119 Extraction Results Table 120 Judgment result screen

Claims

1. A human relationship extraction device that uses a computer to determine whether a person to be screened is a high-risk person having a high level of risk, A processor that executes a program to perform a predetermined process; A storage device for storing the program and data; Equipped with the storage device stores at least information indicating a relationship between people, information indicating a relationship between organizations and people, information indicating a relationship between organizations and organizations, and information indicating a person on a blacklist, as known factual information input from outside; The processor, When the subject is designated, factual information associated with the subject is acquired and registered in the storage device; further registering organizational relationship analogy information indicating an analogy relationship between organizations inferred from the factual information in the storage device; Executing a first related person extraction to extract a first related person of the subject based on the various factual information stored in the storage device; execute a second related person extraction to extract, based on the organizational relationship inference information, a person who has a history of affiliation to an organization that has the analogous relationship with an organization to which the target person has a history of affiliation, as a second related person of the target person, in addition to the first related person extracted by the first related person extraction; execute a first determination as to whether the target person is a high-risk person or not based on whether the first related person extracted in the first related person extraction includes a person on the blacklist; calculating a relevance score indicating the degree to which the second related person extracted in the second related person extraction corresponds to a person on the blacklist, and performing a second determination as to whether the target person is a high-risk person based on the calculated relevance score; When at least one of the first and second determinations indicates that the subject is a high-risk individual, the subject is finally determined to be a high-risk individual; Outputting the determination result of the final determination and the determination results of the first and second determinations. A human relationship extraction device characterized by:

2. The factual information stored in the storage device includes basic information about a plurality of individuals that indicates basic information associated with each of the individuals, The processor, Calculating an inter-personal similarity indicating a similarity between individuals based on the person basic information; Inferring relationships between organizations based on the inter-personal similarity between people belonging to each organization; The analogical relationship between the organizations is registered in the organizational relationship analogy information.

2. The human relationship extraction device according to claim 1 .

3. The processor, Based on information indicating a factual relationship between the organization and the person, a person whose period of affiliation to the organization does not overlap with a period of affiliation to the organization of the subject is excluded from the first and second related persons of the subject.

2. The human relationship extraction device according to claim 1 .

4. The processor, When the first or second determination results in a determination that the subject is a high-risk person, an output is provided recommending confirmation of people or organizations that are thought to have a relationship with the subject on the blacklist.

2. The human relationship extraction device according to claim 1 .

5. A human relationship extraction method using a human relationship extraction device that determines whether a person to be examined is a high-risk person having a high level of risk, The computer constituting the human relationship extraction device has a processor that executes a predetermined process by executing a program, and a storage device that stores the program and data, the storage device stores at least information indicating a relationship between people, information indicating a relationship between organizations and people, information indicating a relationship between organizations and organizations, and information indicating a person on a blacklist, as known factual information input from outside; a fact information registration step in which, when the subject is designated, the processor acquires fact information associated with the subject and registers the fact information in the storage device; an inference information registration step in which the processor further registers, in the storage device, organizational relationship inference information indicating an inferred relationship between organizations inferred from the fact information; A first related person extraction step in which the processor extracts a first related person of the subject based on the various factual information stored in the storage device; a second related person extraction step in which the processor extracts, as a second related person of the subject, a person who has a history of affiliation to an organization that has the analogous relationship with an organization to which the subject has a history of affiliation, based on the organizational relationship inference information registered in the analogy information registration step, in addition to the first related person extracted in the first related person extraction step; a first determination step in which the processor determines whether the subject is a high-risk person based on whether the first related person extracted in the first related person extraction step includes a person on the blacklist; a second determination step in which the processor calculates an association score indicating the degree to which the second related person extracted in the second related person extraction step is included in the blacklist, and determines whether the target person is a high-risk person based on the calculated association score; a final determination step in which the processor finally determines that the subject is a high-risk individual when a determination result that the subject is a high-risk individual is obtained in at least one of the first and second determination steps; an output step in which the processor outputs a determination result in the final determination step and the determination results in the first and second determination steps; A human relationship extraction method comprising:

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