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Relation-Based Document Retrieval for Biomedical IR
Book chapter   Peer reviewed

Relation-Based Document Retrieval for Biomedical IR

Xiaohua Zhou, Xiaohua Hu, Guangren Li, Xia Lin and Xiaodan Zhang
Transactions on Computational Systems Biology V, pp 112-128
2006
DOI: https://doi.org/10.1007/11790105_9
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Betweenness Centrality Binary Relation Information Retrieval System Query Expansion Semantic Type
In this paper, we explore the use of term relations in information retrieval for precision-focused biomedical literature search. A relation is defined as a pair of two terms which are semantically and syntactically related to each other. Unlike the traditional “bag-of-word” model for documents, our model represents a document by a set of sense-disambiguated terms and their binary relations. Since document level co-occurrence of two terms, in many cases, does not mean this document addresses their relationships, the direct use of relation may improve the precision of very specific search, e.g. searching documents that mention genes regulated by Smad4. For this purpose, we develop a generic ontology-based approach to extract terms and their relations, and present a betweenness centrality based approach to rank retrieved documents. A prototyped IR system supporting relation-based search is then built for Medline abstract search. We use this novel IR system to improve the retrieval result of all official runs in TREC-2004 Genomics Track. The experiment shows promising performance of relation-based IR. The average P@100 (the precision of top 100 documents) for 50 topics is significantly raised from 26.37 %( the P@100 of the best run is 42.10%) to 53.69% while the MAP (mean average precision) is kept at an above-average level of 26.59%. The experiment also shows the expressiveness of relations for the representation of information needs, especially in the area of biomedical literature full of various biological relations.

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3 citations in Web of Science
2 citations in Scopus

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Biochemical Research Methods
Computer Science, Interdisciplinary Applications
Computer Science, Theory & Methods
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