Exploring criteria for successful query expansion in the genomic domain

Abstract

Query Expansion is commonly used in Information Retrieval to overcome vocabulary mismatch issues, such as synonymy between the original query terms and a relevant document. In general, query expansion experiments exhibit mixed results. Overall TREC Genomics Track results are also mixed; however, results from the top performing systems provide strong evidence supporting the need for expansion. In this paper, we examine the conditions necessary for optimal query expansion performance with respect to two system design issues: IR framework and knowledge source used for expansion. We present a query expansion framework that improves Okapi baseline passage MAP performance by 185%. Using this framework, we compare and contrast the effectiveness of a variety of biomedical knowledge sources used by TREC 2006 Genomics Track participants for expansion. Based on the outcome of these experiments, we discuss the success factors required for effective query expansion with respect to various sources of term expansion, such as corpus-based cooccurrence statistics, pseudo-relevance feedback methods, and domain-specific and domain-independent ontologies and databases. Our results show that choice of document ranking algorithm is the most important factor affecting retrieval performance on this dataset. In addition, when an appropriate ranking algorithm is used, we find that query expansion with domain-specific knowledge sources provides an equally substantive gain in performance over a baseline system.Keywords Passage retrieval for genomic queries Á Knowledge based query expansion Á Corpus based query expansion Á Pseudo relevance feedback Á Concept-based normalisation passage ranking Á TREC

Managing misspelled queries in IR applications

“…Practical results suggest that while baseline IR can remain relatively unaffected by misspellings, relevance feedback via query expansion becomes highly unstable under these conditions (Lam-Adesina and Jones, 2006). This constitutes a major drawback in the design of IR systems, since query expansion is a major issue in the production of improved query formulations (Guo and Ramakrishnan, 2009;Lu et al, 2009a,b;Stokes et al, 2009). This fact in itself justifies efforts made in dealing with misspelled queries.…”

Answering PICO Clinical Questions: A Semantic Graph-Based Approach

“…Some statistical characteristics of the collection are depicted in Table 1.We used the MeSH terminology which has been widely accepted as the main controlled vocabulary used to index biomedical citations [10]. Each node of the terminology represents a concept node referred to using a preferred entry.…”

Mining document, concept, and term associations for effective biomedical retrieval: introducing MeSH-enhanced retrieval models

“…Lu et al (2009) used MeSH in their automatic query expansion process. Stokes et al (2009) examined genomic query expansion with different knowledge sources including MeSH. Their results suggest that query expansion with domain-specific knowledge sources is preferable.…”

“…However, the issue of how to effectively use MeSH terms in health information retrieval is still under discussion to date. MeSH has been used to improve retrieval performance in a number of ways, such as query expansion (Stokes et al 2009), or terminology assistance for users (Zeng et al 2006). In these methods, MeSH terms that are related to users' information needs are first identified either automatically or manually by users.…”