Joan Guisado-Gámez

Joan Guisado-Gámez, Arnau Prat-Pérez, Josep Lluís Larriba-Pey

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%

Joan Guisado-Gámez, David Dominguez-Sal, Josep-LLuis Larriba-Pey

Keyword based search engines have problems with term ambiguity and vocabulary mismatch. In this paper, we propose a query expansion technique that enriches queries expressed as keywords and short natural language descriptions. We present a new massive query expansion strategy that enriches queries using a knowledge base by identifying the query concepts, and adding relevant synonyms and semantically related terms. We propose two approaches: (i) lexical expansion that locates the relevant concepts in the knowledge base; and, (ii) topological expansion that analyzes the network of relations among the concepts, and suggests semantically related terms by path and community analysis of the knowledge graph. We perform our expansions by using two versions of the Wikipedia as knowledge base, concluding that the combination of both lexical and topological expansion provides improvements of the system's precision up to more than 27%.