基于多知识库的短文本实体链接方法研究——以Wikipedia和Freebase为例  

Entity Linking Method for Short Texts with Multi-Knowledge Bases:Case Study of Wikipedia and Freebase

作　　者：周鹏程[1] 武川[1] 陆伟[1,2] 

机构地区：[1]武汉大学信息管理学院,武汉430072 [2]武汉大学信息资源研究中心,武汉430072

出　　处：《现代图书情报技术》2016年第6期1-11,共11页New Technology of Library and Information Service

基　　金：国家自然科学基金面上项目"基于语言模型的通用实体检索建模及框架实现研究"(项目编号:71173164);武汉大学与中国科技信息研究所合作项目"科学文献的语义功能识别与深度利用"的研究成果之一

摘　　要：【目的】基于多知识库进行实体链接,解决基于单一知识库的实体链接覆盖度低的问题。【方法】首先生成文本的n-gram并利用词性和多个指称–实体字典获取候选指称,然后生成指称组合并保留覆盖度最大且不被其他组合包含的指称组合,接着生成候选实体序列并利用多知识库信息计算实体序列的相关度,最后选择相关度最大的实体序列为最终结果。【结果】以Wikipedia和Freebase为例的实验结果表明,基于Wikipedia+Freebase的实体链接准确率、召回率、F值分别达到71.81%、76.86%、74.25%。【局限】基于词性过滤n-gram缺乏理论依据,数据集FACC1具有高准确率和低召回率的特点。【结论】利用多个知识库的实体信息,能够提升实体链接效果。[Objective] This paper proposes an entity linking method using multi-knowledge bases, aiming at solving the problem of low coverage caused by entity linking with single knowledge base. [Methods] First, we generated n-gram of input text and obtained candidate mentions using part of speech and multi-mention-entity dictionary. Second, we generated and retained mention combinations of highest coverage which are not contained by other mention combinations. Third, we generated entity sequences and calculated their relevence degree using information from multi-knowledge bases. We listed entity sequence with the highest relevence degree as the final result. [Results] This case study showed that the Precision, Recall, and F-value of the entity linking based on Wikipedia＋Freebase reaches 71.81%, 76.86%, and 74.25% respectively. [Limitations] Filtering n-gram based on part of speech lacked theoretical foundation, and the FACC1 dataset featured high precision but low recall. [Conclusions] Utilizing entity information from multi-knowledge bases can improve the performance of entity linking.

关 键 词：实体链接 知识库 WIKIPEDIA Freebase 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]