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Knowledge Completion System through Learning the Relationship between Query and Knowledge Graph
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질의문과 지식 그래프 관계 학습을 통한 지식 완성 시스템

논문 기본 정보

Type
Academic journal
Author
Min-Sung Kim (숭실대학교) Min-Ho Lee (숭실대학교) Wan-Gon Lee (숭실대학교) Young-Tack Park (숭실대학교)
Journal
Korean Institute of Information Scientists and Engineers Journal of KIISE Vol.48 No.6 KCI Excellent Accredited Journal
Published
2021.6
Pages
649 - 656 (8page)
DOI
10.5626/JOK.2021.48.6.649

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Knowledge Completion System through Learning the Relationship between Query and Knowledge Graph
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The knowledge graph is a network comprising of relationships between the entities. In a knowledge graph, there exists a problem of missing or incorrect relationship connection with the specific entities. Numerous studies have proposed learning methods using artificial neural networks based on natural language embedding to solve the problems of the incomplete knowledge graph. Various knowledge graph completion systems are being studied using these methods. In this paper, a system that infers missing knowledge using specific queries and knowledge graphs is proposed. First, a topic is automatically extracted from a query, and topic embedding is obtained from the knowledge graph embedding module. Next, a new triple is inferred by learning the relationship between the topic from the knowledge graph and the query by using Query embedding and knowledge graph embedding. Through this method, the missing knowledge was inferred and the predicate embedding of the knowledge graph related to a specific query was used for good performance. Also, an experiment was conducted using the MetaQA dataset to prove the better performance of the proposed method compared with the existing methods. For the experiment, we used a knowledge graph having movies as a domain. Based on the assumption of the entire knowledge graph and the missing knowledge graph, we experimented on the knowledge graph in which 50% of the triples were randomly omitted. Apparently, better performance than the existing method was obtained.
#지식 완성 #지식 그래프 #딥러닝 #인공지능 #임베딩 #쿼리 #트리플 #knowledge completion #knowledge graph #deep learning #artificial intelligence #embedding #query #triple

Contents

요약
Abstract
1. 서론
2. 배경 지식 및 관련 연구
3. 연구 내용
4. 실험
5. 결론 및 향후 연구
References

References (16)

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Bollacker, Kurt, et al., "Freebase: a collaboratively created graph database for structuring human knowledge," Proc. of the 2008 ACM SIGMOD international conference on Management of data, pp. 1247-1250, Jun. 2008. google schola Auer, Sören, et al., "Dbpedia: A nucleus for a web of open data," Springer Berlin Heidelberg, Lecture Notes in Computer Science, Vol. 4825, pp. 722-735, 2007. google schola Suchanek, Fabian M., Gjergji Kasneci, and Gerhard Weikum, "Yago: a core of semantic knowledge," Proc. of the 16th international conference on WorldWide Web. ACM, pp. 697-706, May, 2007. google schola Min-Sung Kim, Min-Ho Lee, Wan-Gon Lee, Young-Tack Park, "OWL-Horst Ontology Inference Engine Using Distributed Table Structure in Cloud Computing Environment," KIISE, Vol. 47, No. 7, pp. 674-681, Jul. 2020. dbpia Min-Ho Lee, Min-Sung Kim, Wan-Gon Lee, Young-Tack Park, "An Integrated System for Large-Scale Knowledge Graph Inference Using the Spark DataFrame," KIISE, Vol. 47, No. 12, Dec. 2020. dbpia

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