A Query Expansion Approach Using Entity Distribution Based on Markov Random Fields

The development of knowledge graph construction has prompted more and more commercial engines to improve the retrieval performance by using knowledge graphs as the basic semantic web. Knowledge graph is often used for knowledge inference and entity search, however, the potential ability of its entities and properties for better improving search performance in query expansion remains to be further excavated. In this paper, we propose a novel query expansion technique with knowledge graph (KG) based on the Markov random fields (MRF) model to enhance retrieval performance. This technique, called MRF-KG, models the joint distribution of original query terms, documents and two expanded variants, i.e. entities and properties. We conduct experiments on two TREC collections, WT10G and ClueWeb12B, annotated with Freebase entities. Experiment results demonstrate that MRF-KG outperforms traditional graph-based models.