知识图谱中实体关系抽取方法研究

doi:10.3778/j.issn.1673-9418.2305019

摘要/Abstract

摘要： 实体关系抽取作为知识图谱构建的基础得到了越来越多研究人员的关注。实体关系抽取能够自动、准确地从大量数据中获取知识，并以结构化形式表示和存储。因此，实体关系抽取的正确性直接影响到知识图谱构建的准确性和后续知识图谱应用效果。然而，针对复杂结构、开放领域、多语言、多模态、小样本数据和实体关系联合抽取等不同研究热点，现存的实体关系抽取方法仍存在一些局限性。基于当前实体关系抽取研究热点领域将实体关系抽取分为复杂结构研究领域、开放领域、多语言研究领域、多模态研究领域、小样本数据研究领域和实体关系联合抽取六个方面，将每个方面按照具体问题进行分类并列举出一些解决方法。不仅系统梳理了每一个类别当前存在的问题和解决方法，还归纳了每个类别的研究成果，并从定量分析和定性分析两个维度，详细地分析了每个方法的优点和缺点。最后，总结了当前热点领域中待解决的问题，同时展望了知识图谱中实体关系抽取方法未来的发展趋势。

关键词: 知识图谱构建, 实体抽取, 关系抽取

Abstract: Entity-relationship extraction has gained more and more attention from researchers as a basis for knowledge graph construction. Entity-relationship extraction can automatically and accurately obtain knowledge from a large amount of data, and represent and store it in a structured form. Therefore, the correctness of entity-relationship extraction directly affects the accuracy of knowledge graph construction and the effect of subsequent knowledge graph application. However, for different research hotspots such as complex structure, open domain, multi-language, multi-modal, small sample data, and joint extraction of entity-relationships, the existing entity-relationship extraction methods still have some limitations. Based on the current research hotspots of entity-relationship extraction, this paper tries to categorize entity-relationship extraction into six aspects: complex structure, open domain, multilingual, multimodal, small-sample data, and joint entity-relationship extraction, and categorizes each aspect according to the specific problems and lists out some solutions. Not only the current problems and solutions of each category are systematically sorted out, but the research results of each category are summarized, and the advantages and disadvantages of each method are analyzed in detail from the dimensions of quantitative analysis and qualitative analysis. Finally, the problems to be solved in the current hot areas are summarized, and the future development trend of entity-relationship extraction methods in the knowledge graph is also prospected.

Key words: knowledge graph construction, entity extraction, relationship extraction

张西硕, 柳林, 王海龙, 苏贵斌, 刘静. 知识图谱中实体关系抽取方法研究[J]. 计算机科学与探索, 2024, 18(3): 574-596.

ZHANG Xishuo, LIU Lin, WANG Hailong, SU Guibin, LIU Jing. Survey of Entity Relationship Extraction Methods in Knowledge Graphs[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(3): 574-596.

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