Using Dempster-shafer’s Evidence Theory For Query Expansion Based On Freebase Knowledge

Query expansion is generally a useful technique in improving search performance.However, some expanded query terms obtained by traditional statistical methods (e.g.,pseudo-relevance feedback) may not be relevant to the user’s information need, whilesome relevant terms may not be contained in the feedback documents at all. Recentstudies utilize external resources to detect terms that are related to the query, and thenadopt these terms in query expansion. In this paper, we present a study in the use ofFreebase, which is an open source general-purpose ontology, as a source for derivingexpansion terms. Freebase provides a graph-based model of human knowledge, fromwhich a rich and multi-step structure of instances related to the query concept can beextracted, as a complement to the traditional statistical approaches to query expansion.We propose a novel method, based on the well-principled Dempster-Shafer’s (D-S)evidence theory, to measure the certainty of expansion terms from the Freebasestructure. The expanded query model is then combined with a state of the artstatistical query expansion model--the Relevance Model (RM3). Experiments showthat the proposed method achieves significant improvements over RM3.