Relation-Based Document Retrieval for Biomedical Literature Databases [chapter]

Xiaohua Zhou, Xiaohua Hu, Xia Lin, Hyoil Han, Xiaodan Zhang
2006 Lecture Notes in Computer Science  
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
more » ... r relationship s, 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; a prototyped IR system supporting relation-based search is then built for Medline abstract search. We then 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 mean of P@100 (the precision of top 100 documents) for all 50 topics is raised from 26.37 %( the P@100 of the best run is 42.10%) to 5 3.69% while the recall is kept at an acceptable level of 44.31%. 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. Rule for entity-attribute relation: term1 preposition term2 Example: Obesity is an independent risk factor (term1) for periodontal disease (term2).
doi:10.1007/11733836_48 fatcat:lnj42gn3ajdbhgpbkpbcwr7ema