Entity centric query expansion for enterprise search

Abstract

Enterprise search is important, and the search quality has a direct impact on the productivity of an enterprise. Many information needs of enterprise search center around entities. Intuitively, information related to the entities mentioned in the query, such as related entities, would be useful to reformulate the query and improve the retrieval performance. However, most existing studies on query expansion are termcentric. In this paper, we propose a novel entity-centric query expansion framework for enterprise search. Specifically, given a query containing entities, we first utilize both unstructured and structured information to find entities that are related to the ones in the query. We then discuss how to adapt existing feedback methods to use the related entities to improve search quality. Experiment results show that the proposed entity-centric query expansion strategy is more effective to improve the search performance than the state-of-the-art pseudo feedback methods on longer, natural language-like queries with entities.

Fuzzy rule based profiling approach for enterprise information seeking and retrieval

“…A number of approaches enhance the retrieval performance of enterprise IS&R systems based on relevance feedback, [31] [56]. The user annotation based approach was suggested to enhance the retrieval performance using the PageRank algorithm which was commonly used in the web search [31].…”

“…In another research study, an entity-centric query expansion approach was proposed to address the information overloading problem in the enterprise [56]. This approach expanded the user query based on the relevant entities.…”