KeEL: Knowledge enhanced entity linking in automatic biography construction

被引：2

作者：

Tianlei, Zhang ^{[1
]}

Xinyu, Zhang ^{[1
]}

Mu, Guo ^{[1
]}

机构：

[1] Computer Science Department, Tsinghua University, 100084, Beijing

来源：

Journal of China Universities of Posts and Telecommunications | 2014年 / 22卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Biography construction; Entity linking; Knowledge; Knowledge enhanced entity linking; Markov logic network;

D O I：

10.1016/S1005-8885(15)60625-2

中图分类号：

O211 [概率论（几率论、或然率论）];

学科分类号：

摘要：

Biography is a direct and extensive way to know the representation of well known peoples, however, for common people, there is poor knowledge for them to be recognized. In recent years, information extraction (IE) technologies have been used to automatically generate biography for any people with online information. One of the key challenges is the entity linking (EL) which can link biography sentence to corresponding entities. Currently the used general EL systems usually generate errors originated from entity name variation and ambiguity. Compared with general text, biography sentences possess unique yet rarely studied relational knowledge (RK) and temporal knowledge (TK), which could sufficiently distinguish entities. This article proposed a new statistical framework called the knowledge enhanced EL (KeEL) system for automated biography construction. It utilizes commonsense knowledge like PK and TK to enhance Entity Linking. The performance of KeEL on Wikipedia data was evaluated. It is shown that, compared with state-of-the-art method, KeEL significantly improves the precision and recall of Entity Linking.

引用

页码：57 / 64

页数：7

共 25 条

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