一种融合实体语义知识的实体集合扩展方法

实体集合扩展是开放式信息抽取的一个重要问题,该问题研究如何从一个语义类的若干实体(称为种子)出发,得到该类别的更多实体。现有实体集合扩展方法主要使用上下文模板或种子在语料中的分布信息进行抽取,其缺点是无法解决种子的歧义问题,而该问题会影响方法的有效性。在该文中,作者提出了一种融合实体语义知识的实体集合扩展方法,通过引入语义知识来解决种子歧义性问题。新方法通过使用Wikipedia实现了语义知识的引入,并把基于语义知识的扩展方法和基于模板的扩展方法相融合。实验表明,与单纯基于上下文方法相比,该文方法在准确率上提升了18.5%,召回率上提升了6.8%,MAP值上提升了22.8%。

A Novel Entity Set Expansion Method Leveraging Entity Semantic Knowledge

Entity Set Expansion is one of the important problems in Open Information Extraction. Entity Set Expansion refers to expanding several given seeds of one concept into a more complete set. Most approaches solve the problem by using context or distributional information, suffering from the limitation of seed ambiguity problem which results in poor results. In this paper we present a novel method which introduces the semantic knowledge by leveraging Wikipedia knowledge base. We combine this method with traditional template based method. Experiment results show that the proposed method improves 18.5% in precision, 6.8% in recall and 22.8% in MAP.
Key wordsEntity Set Expansion; knowledge base; semantic knowledge