Overview on Knowledge Graph Embedding Technology Research

被引：0
|
作者：
Zhang T.-C. [1 ]
Tian X. [1 ]
Sun X.-H. [1 ]
Yu M.-H. [2 ]
Sun Y.-H. [1 ]
Yu G. [1 ]
机构：
[1] School of Computer Science and Engineering, Northeastern University, Shenyang
[2] Software College, Northeastern University, Shenyang
来源：
Ruan Jian Xue Bao/Journal of Software | 2023年 / 34卷 / 01期
关键词：
complex relationship modeling; dynamic knowledge graph; knowledge graph embedding (KGE); relationship extraction; translation model;
D O I：
10.13328/j.cnki.jos.006429
中图分类号：
学科分类号：
摘要：
Knowledge graph (KG) is a kind of technology that uses graph model to describe the relationship between knowledge and modeling things. Knowledge graph embedding (KGE), as a widely adopted knowledge representation method, its main idea is to embed entities and relationships in a knowledge graph into a continuous vector space, which is used to simplify operations while preserving the intrinsic structure of the KG. It can benefit a variety of downstream tasks, such as KG completion, relation extraction, etc. Firstly, the existing knowledge graph embedding technologies are comprehensively reviewed, including not only techniques using the facts observed in KG for embedding, but also dynamic KG embedding methods that add time dimensions, as well as KG embedding technologies that integrate multi-source information. The relevant models are analyzed, compared and summarized from the perspectives of entity embedding, relation embedding and scoring functions. Then, typical applications of KG embedding technologies in downstream tasks are briefly introduced, including question answering systems, recommendation systems and relationship extraction. Finally, the challenges of knowledge graph embedding are expounded, and the future research directions are prospected. © 2023 Chinese Academy of Sciences. All rights reserved.
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页码：277 / 311
页数：34
相关论文
共 163 条
  • [1] Bollacker KD, Evans C, Paritosh P, Sturge T, Taylor J., Freebase: A collaboratively created graph database for structuring human knowledge, Proc. of the 2008 ACM SIGMOD Int’l Conf. on Management of Data, pp. 1247-1250, (2008)
  • [2] Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, Ives Z., DBpedia: A nucleus for a Web of open data, Proc. of the 6th Int’l Semantic Web Conf. and the 2nd Asian Semantic Web Conf, pp. 722-735, (2007)
  • [3] Suchanek FM, Kasneci G, Weikum G., Yago: A core of semantic knowledge, Proc. of the 16th Int’l Conf. on World Wide Web, pp. 697-706, (2007)
  • [4] Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka ER, Mitchell TM., Toward an architecture for never-ending language learning, Proc. of the 24th AAAI Conf. on Artificial Intelligence, pp. 1306-1313, (2010)
  • [5] Vrandecic D, Krotzsch M., Wikidata: A free collaborative knowledgebase, Communications of the ACM, 57, 10, pp. 78-85, (2014)
  • [6] Berant J, Chou A, Frostig R, Liang P., Semantic parsing on freebase from question-answer pairs, Proc. of the 2013 Conf. on Empirical Methods in Natural Language Processing (EMNLP), pp. 1533-1544, (2013)
  • [7] Heck LP, Hakkani-Tur D, Tur G., Leveraging knowledge graphs for web-scale unsupervised semantic parsing, Proc. of the 14th Annual Conf. of the Int’l Speech Communication Association (ISCA), pp. 1594-1598, (2013)
  • [8] Damljanovic D, Bontcheva K., Named entity disambiguation using linked data, Proc. of the 9th Extended Semantic Web Conf, pp. 231-240, (2012)
  • [9] Zheng ZC, Si XC, Li FT, Chang EY, Zhu XY., Entity disambiguation with freebase, Proc. of the 2012 IEEE/WIC/ACM Int’l Conf. on Web Intelligence and Intelligent Agent Technology, pp. 82-89, (2012)
  • [10] Hoffmann R, Zhang CL, Ling X, Zettlemoyer L, Weld DS., Knowledge-based weak supervision for information extraction of overlapping relations, Proc. of the 49th Annual Meeting of the Association for Computational Linguistics: Human, pp. 541-550, (2011)
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