一种基于图模型的维基概念相似度计算方法及其在实体链接系统中的应用

A Graph-based Similarity Measure between Wikipedia Concepts and Its Application in Entity Linking System

实体链接是指将文本中具有歧义的实体指称项链接到知识库中相应实体的过程。该文首先对实体链接系统进行了分析,指出实体链接系统中的核心问题—实体指称项文本与候选实体之间的语义相似度计算。接着提出了一种基于图模型的维基概念相似度计算方法,并将该相似度计算方法应用在实体指称项文本与候选实体语义相似度的计算中。在此基础上,设计了一个基于排序学习算法框架的实体链接系统。实验结果表明,相比于传统的计算方法,新的相似度计算方法可以更加有效地捕捉实体指称项文本与候选实体间的语义相似度。同时,融入了多种特征的实体链接系统在性能上获得了达到state-of-art的水平。

Entity linking is the task of map entity mentions in a document to their entities in a knowledge base （KB）. In this paper, we briefly introduce the traditional entity linking system and point out the key problem of entity linking system-the semantic similarity measure between the content of entity mention and the document of the candidate entity. And then, we propose a novel semantic relatedness measure between Wikipedia concepts based on the graph structure of Wikipedia. With this similarity measure, we present a novel learning to rank framework which leverage the rich semantic information derived from Wikipedia to deal with the entity lining task. Experiment results show that the performance of the system is comparable to the state-of-art result.