AUTOMATIC UNSUPERVISED KEYPHRASE-BASED QUERY EXPANSION FOR BIOMEDICAL DOMAIN

Min Song, Il-Yeol Song and Ki Jung Lee
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Min Song: College of Information Science and Technology, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104-2875, USA
Il-Yeol Song: College of Information Science and Technology, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104-2875, USA
Ki Jung Lee: College of Information Science and Technology, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104-2875, USA

Chapter 18 in Knowledge Management:Nurturing Culture, Innovation, and Technology, 2005, pp 209-220 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractThis paper introduces an automatic querying technique, DocSpotter, and presents that it is an efficient tool to identify the set of documents for the extraction of a pre-defined relation from text. DocSpotter is designed to retrieve documents with more precise match to the initial query by expanding queries as it repeats the keyphrase extraction process in a given database. It utilizes keyphrase extraction in conjunction with referencing ontologies for the query expansion. We report two sets of experimental results demonstrating the performance of DocSpotter. The experiments were designed to evaluate the performance of DocSpotter on the task of protein-protein interaction extraction. The results identified that DocSpotter was able to retrieve more and more documents that contain protein-protein pairs from MEDLINE as it repeated the keyphrase extraction process. In the other set of experiments, performance of DocSpotter was compared with that of SLIPPER, a supervised rule-based query expansion technique. The results showed that DocSpotter outperformed SLIPPER from 17.90% to 29.98% in terms of accuracy in all iterations.

Keywords: Knowledge Management; Knowledge Sharing; Knowledge Discovery; KM Tools and Technologies; Communication and Organization Culture (search for similar items in EconPapers)
Date: 2005
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