Query Expansion via structural motifs in Wikipedia Graph

Abstract

The search for relevant information can be very frustrating for users who, unintentionally, use too general or inappropriate keywords to express their requests. To overcome this situation, query expansion techniques aim at transforming the user request by adding new terms, referred as expansion features, that better describe the real intent of the users. We propose a method that relies exclusively on relevant structures (as opposed to the use of semantics) found in knowledge bases (KBs) to extract the expansion features. We call our method Structural Query Expansion (SQE). The structural analysis of KBs takes us to propose a set of structural motifs that connect their strongly related entries, which can be used to extract expansion features. In this paper we use Wikipedia as our KB, which is probably one of the largest sources of information. SQE is capable of achieving more than 150% improvement over non expanded queries and is able to identify the expansion features in less than 0.2 seconds in the worst case scenario. Most significantly, we believe that we are contributing to open new research directions in query expansion, proposing a method that is orthogonal to many current systems. For example, SQE improves pseudo-relevance feedback techniques up to 13%.

Query expansion techniques for information retrieval: A survey

“…Then, the top N expansion terms having the highest MMRE scores are selected. Recently, Wikipedia and DBpedia are being used widely as data sources for QE (e.g., [167,14,286,3,9,13,108]). Li et al [167] performed an investigation using Wikipedia and retrieved all articles corresponding to the original query as a source of expansion terms for pseudo-relevance feedback.…”