An entity disambiguation method based on LeaderRank

Entity Disambiguation is commonly faced in semantic search and knowledge base population. However, it is a challenging task because of the diversity of mentions. Previous methods can be classified into two main groups. One focuses on disambiguating mentions in a document independently and mainly relies on the local context similarity. The other collectively disambiguates mentions only taking into account link information. These are not appropriate when the context and the link information are poor or misleading. In this paper, we propose a new method to collectively disambiguate mentions in documents. Our proposed framework considers three features, including text similarity, entity popularity, and entity relationship. First we adopt LeaderRank algorithm on the graph model to rank entities according to the link information among entities. Then we combine with global text similarity between entity and document to disambiguate mentions. Our detailed experimental evaluation on two benchmark datasets demonstrates our methods is effective.