A collective entity linking algorithm with parallel computing on large-scale knowledge base

摘要

Entity linking is a central concern of automatic knowledge question answering and knowledge base population. Traditional collective entity linking approaches only consider one of the entity contexts or semantic relations between entities. Thus, these approaches always have poor performance on Web documents. The efficiency of collective entity linking needs to be improved as well. This paper proposes a collective entity linking algorithm based on topic model and graph. Constructing the topic model can represent mentions and candidate entities by using topic distributions. It makes full use of context in documents. Entity semantic relations are represented by document similarities which are computed through the topic model. Parallel computing is used to reduce long running time which is caused by topic model construction. Entity graph is constructed according to the relations between entities in the knowledge graph. Hypertext-Induced Topic Search exploits the entity graph to compute hub value and authority value of candidate entities. And the authority value is the basis for entity linking. Experimental results on open-domain corpus (NLPCC2014) demonstrate the validity of the proposed method. Experimental results show that the proposed approach has 5.2% improvement in F-1-measure than AGDISTIS on corp NLPCC2014.