基于重要性筛选与语义聚类的本体摘要技术

Ontology Summarization Based on Importance Filtering and Semantic Clustering

本体是知识图谱的上层结构,为了帮助用户快速、灵活地理解和使用本体,本体摘要技术应运而生。针对现有的本体摘要技术中对语义信息利用不充分以及易向长文本倾斜的不足,提出基于重要性初筛和语义聚类的两级本体摘要方法。首先,基于预定义的重要性度量指标筛选候选本体概念集;其次,采用BERT将每个候选本体概念转化为对应的本体概念向量;最后,使用K-means++聚类对这些本体概念向量进行聚类,获得具有代表性的重要概念,完成本体摘要。实验结果表明,相对于已有方法,提出的方法可以挑选出更具代表性的本体概念。

As the superstructure of knowledge graph,ontology is the normalized and formalized description of abstract concepts and relations in a certain domain.In order to understand ontology quickly and flexibly,ontology summarization arises.To solve the problem of the insufficient utilization of semantic information and the tilt toward long text,a two-level ontology summarization based on importance filtering and semantic clustering is proposed.First,the candidate ontology concept sets are filtered by predefined importance measures.Then,BERT is used to transform each candidate ontology concept into its corresponding ontology concept vector.Finally,K-means++is used to cluster these ontology concept vectors to obtain representative concepts.The experimental results show that compared with the existing methods,the proposed ontology summarization method can pick out more representative ontology concepts.