Abstract: Entity linking is the process of mapping entity mentions in a document to their entities in Knowledge Base(KB) and plays a key role in the expansion of knowledge base. Aiming at traditional entity linking methods, which mainly utilize surface features such as context similarity and ignore the semantic correlation between co-occur mentions in a text corpus, a collective entity linking method based on multiple features is proposed. Firstly, it combines synonym list and namesake list to produce a set of candidate entities. After that, it extracts varieties of the semantic features and builds a referent graph. At last, it ranks the candidate entities and choses the top1 entity as the linking target. The evaluation on data sets of NLP&CC2013 Chinese micro-blog entity linking track shows a average accuracy of 90.97%, which is better than the state-of-art result.

Key words: Chinese collective entity linking, knowledge graph, entity disambiguation