WO2022107989A1 - Procédé et dispositif permettant de compléter des connaissances à l'aide d'un apprentissage de relation entre une interrogation et un graphe de connaissances - Google Patents

Procédé et dispositif permettant de compléter des connaissances à l'aide d'un apprentissage de relation entre une interrogation et un graphe de connaissances Download PDF

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WO2022107989A1
WO2022107989A1 PCT/KR2020/018966 KR2020018966W WO2022107989A1 WO 2022107989 A1 WO2022107989 A1 WO 2022107989A1 KR 2020018966 W KR2020018966 W KR 2020018966W WO 2022107989 A1 WO2022107989 A1 WO 2022107989A1
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query
embedding
knowledge
value
knowledge graph
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PCT/KR2020/018966
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English (en)
Korean (ko)
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박영택
이완곤
김민성
이민호
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숭실대학교산학협력단
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3329—Natural language query formulation or dialogue systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/3332—Query translation
    • G06F16/3334—Selection or weighting of terms from queries, including natural language queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G06F16/3344—Query execution using natural language analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/901—Indexing; Data structures therefor; Storage structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/901—Indexing; Data structures therefor; Storage structures
    • G06F16/9024—Graphs; Linked lists
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/284—Lexical analysis, e.g. tokenisation or collocates
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models

Definitions

  • the present invention relates to a knowledge completion method and apparatus using a relationship learning between a query and a knowledge graph.
  • a knowledge graph refers to a network composed of relationships between entities.
  • there is a problem of an incomplete knowledge graph due to problems such as omission of relationships for specific entities or incorrect connection of relationships.
  • the present invention intends to propose a knowledge completion method and apparatus using a relationship learning between a query sentence and a knowledge graph capable of inferring missing knowledge by using a specific query sentence and a knowledge graph.
  • a query embedding module that outputs a query embedding value corresponding to an input query ; a topic extraction module for extracting topics from the input query; a knowledge graph embedding module for outputting embedding values for a plurality of predicates, subjects, and objects included in the knowledge graph; a similarity calculation module for determining a predicate most similar to a query sentence by calculating a similarity between the query embedding value and each of the embedding values of the plurality of predicates; an embedding connection module that connects the embedding value of the query with the embedding value of the most similar predicate; and a scoring module for inferring a new triple using the extracted topic, the embedding value connecting the embedding value of the query and the embedding value of the most similar predicate, and the subject and object of the knowledge graph.
  • a knowledge completion apparatus that outputs a query embedding value corresponding to an input query ; a topic extraction module for extracting topics from the input query; a knowledge
  • the query embedding module may determine an embedding value corresponding to the input query using a BERT-based RoBERT model.
  • the query embedding module may perform tokenization to separate the input query into words, and may extract a topic by excluding words having a preset number of appearances or more in the knowledge graph.
  • the similarity calculation module may perform a dot product operation on the embedding values of all predicates obtained through the graph embedding module, take a sigmoid, find the highest value, and search for a predicate most similar to the query statement.
  • the scoring module places the extracted topic in the subject, places the embedding value connecting the query embedding value and the embedding value of the most similar predicate in the predicate, and places the subject and objects of the knowledge graph in the candidate object to deduce a new triple.
  • the scoring module sequentially places a plurality of candidate objects in the score calculation function, so that the entity with the highest score is an object related to an embedding value that connects the extracted topic and the query embedding value with the embedding value of the most similar predicate. can decide
  • a method of completing knowledge by using a query and knowledge graph relationship learning in an apparatus including a processor and a memory, the method comprising: outputting a query embedding value corresponding to an input query; extracting a topic from the input query; outputting embedding values for a plurality of predicates, subjects, and objects included in the knowledge graph; determining a predicate most similar to a query statement by calculating a similarity between the query embedding value and each of the embedding values of the plurality of predicates; concatenating the embedding value of the query and the embedding value of the most similar predicate; and inferring a new triple using the extracted topic, the embedding value connecting the embedding value of the query and the embedding value of the most similar predicate, and the subject and object of the knowledge graph.
  • FIG. 1 is a diagram showing the configuration of a knowledge completion device according to an embodiment of the present invention.
  • FIG. 2 is a diagram illustrating a detailed configuration of a query embedding module according to the present embodiment.
  • FIG. 3 is a diagram illustrating a case in which the number of appearances is used for topic extraction according to the present embodiment.
  • FIG. 4 is a diagram for explaining a process of searching for a predicate similar to a query according to the present embodiment.
  • FIG. 5 is a diagram for explaining a process of inferring a new triple through the score calculation function according to the present embodiment.
  • the present invention provides a method for inferring missing knowledge by using a specific query and a knowledge graph.
  • a topic is automatically extracted from a question-type query to obtain the corresponding topic embedding value, and a new triple is created by learning the relationship between the topic and the query from the knowledge graph by using the query embedding and the knowledge graph embedding.
  • predicate embedding of a knowledge graph related to a specific query is used together.
  • FIG. 1 is a diagram showing the configuration of a knowledge completion device according to an embodiment of the present invention.
  • the knowledge completion device includes a query embedding module 100, a topic extraction module 102, a knowledge graph embedding module 104, a similarity calculation module 106, and an embedding connection module. 108 and a scoring module 110 .
  • the query embedding module 100 outputs an embedding value corresponding to the query input by the user.
  • query embedding means embedding a query inputted through various algorithms in a vector form in a multidimensional space.
  • FIG. 2 is a diagram illustrating a detailed configuration of a query embedding module according to the present embodiment.
  • a BERT-based RoBERT model is used for query embedding.
  • the BERT model on which RoBERT is based is a context-dependent model.
  • the word 'bank' can have two meanings, such as 'bank deposit' or 'river bank', so the advantage of obtaining an embedding value that expresses the characteristics of the word well considering the context, that is, the front and back sentences. have it
  • each word constituting the query is divided into tokens and input, and the first value of the result is embedded in the input query. used as a value.
  • the topic extraction module 102 extracts a topic in consideration of the number of appearances in the knowledge graph of each word included in the input query.
  • the input query is “What does Christian Bale star in?”
  • the topic is separately marked as [Christian Bale] in the query, such as “What does [Christian Bale] star in?”. Therefore, when extracting a topic, work for marking is required.
  • FIG. 3 is a diagram illustrating a case in which the number of appearances is used for topic extraction according to the present embodiment.
  • a topic is extracted by extracting a small number of words by calculating the number of appearances of each word included in a query in the knowledge graph. For example, “What does Christian Bale star in?” 'Waht', 'does', 'star', 'in', and '?' appear in the query because the probability of appearing in other queries is much higher than that of “Christian Bale”. It is desirable to extract the few words as the topic.
  • a topic may be extracted by performing tokenization of dividing a query into words and excluding words having a preset number of appearances or more (eg, 2,000 or more).
  • the knowledge graph embedding module 104 outputs an embedding matrix that well represents a knowledge graph (KB).
  • a triple is composed of a predicate corresponding to a relation and an entity (subject, object) corresponding to a subject and purpose, and the knowledge graph embedding module 104 includes embedding values for all predicates and entities included in the knowledge graph.
  • the ComplEx model that can express real and imaginary numbers as well as symmetric and asymmetric relationships can be used, and all triple learning of KG is possible through the Score Function.
  • an embedding value of a query sentence and a knowledge graph is used to find a predicate similar to the query sentence.
  • the similarity calculation module 106 calculates the similarity between the embedding values output through the query embedding module 100 and the embedding values of all predicates obtained through the knowledge graph embedding module 104 to determine the predicate most similar to the query.
  • the similarity calculation module 106 performs a dot product operation on the embedding values output through the query embedding module 100 and the embedding values of all predicates obtained through the knowledge graph embedding module 104, and takes a sigmoid to simulate Look for high values to find similar predicate embeddings.
  • FIG. 4 is a diagram for explaining a process of searching for a predicate similar to a query according to the present embodiment.
  • an embedding value is obtained through the query embedding module 100 for “What does Christian Bale star in?”. And if you take all the predicate embedding values of the knowledge graph in matrix form and take the sigmoid, you can see the similarity value between the query and each predicate as shown in the box on the right. Among them, the highest value “starred_actors” becomes the predicate embedding value most similar to the corresponding query.
  • the embedding concatenation module 108 concatenates the query embedding value output from the query embedding module 100 and the predicate embedding value most similar to the query sentence determined by the similarity calculation module 106 .
  • the scoring module 110 infers the missing triple by using the score calculation function (Equation 1) of the knowledge graph embedding module.
  • the scoring module 100 assigns the topic extracted from the query to the subject of the score calculation function ( ) and concatenating the query and similar predicate embeddings in that query to place the predicate in the score calculation function ( ) is placed in In place of the last object, entities such as the subject and object of the knowledge graph ( ) are candidates, and after calculation, the target with the highest value is found and a corresponding triple is newly inferred.
  • FIG. 5 is a diagram for explaining a process of inferring a new triple through the score calculation function according to the present embodiment.
  • the knowledge completion process using the query and knowledge graph relationship learning may be performed in an apparatus including a processor and a memory.
  • the processor may include a central processing unit (CPU) or other virtual machine capable of executing a computer program.
  • CPU central processing unit
  • the memory may include a non-volatile storage device such as a fixed hard drive or a removable storage device.
  • the removable storage device may include a compact flash unit, a USB memory stick, and the like.
  • the memory may also include volatile memory, such as various random access memories.

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Abstract

Sont divulgués, un procédé et un dispositif permettant de compléter des connaissances à l'aide d'un apprentissage de relation entre une interrogation et un graphe de connaissances. Selon la présente invention, un dispositif permettant de compléter des connaissances à l'aide d'un apprentissage de relation entre une interrogation et un graphe de connaissances est décrit, comprenant : un module d'incorporation d'interrogation qui délivre une valeur d'incorporation d'interrogation correspondant à une interrogation d'entrée ; un module d'extraction de sujet qui extrait des sujets de l'interrogation d'entrée ; un module d'incorporation de graphe de connaissances qui délivre des valeurs d'incorporation pour une pluralité de prédicats, de sujets et d'objets compris dans le graphe de connaissances ; un module de calcul de similarité qui détermine le prédicat le plus similaire à l'interrogation en calculant la similarité entre la valeur d'incorporation de l'interrogation et les valeurs d'incorporation de chaque prédicat de la pluralité de prédicats ; un module de liaison d'incorporation qui relie la valeur d'incorporation de l'interrogation à la valeur d'incorporation du prédicat le plus similaire ; et un module de notation qui infère un nouveau triple à l'aide du sujet extrait, la valeur d'incorporation reliant la valeur d'incorporation d'interrogation et la valeur d'incorporation du prédicat le plus similaire, et les sujets et les objets du graphe de connaissances.
PCT/KR2020/018966 2020-11-23 2020-12-23 Procédé et dispositif permettant de compléter des connaissances à l'aide d'un apprentissage de relation entre une interrogation et un graphe de connaissances WO2022107989A1 (fr)

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KR102583818B1 (ko) * 2022-09-14 2023-10-04 주식회사 글로랑 Bert를 기반으로한 응답자 집단을 대표하는 질의 응답 네트워크를 활용한 인적성 검사의 표집 과정 방법

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KR101267038B1 (ko) * 2011-02-25 2013-05-24 주식회사 솔트룩스 벡터 공간 모델을 이용한 ｒｄｆ 트리플 선택 방법, 장치, 및 그 방법을 실행하기 위한 프로그램 기록매체
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CN111639171A (zh) * 2020-06-08 2020-09-08 吉林大学 一种知识图谱问答方法及装置

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