Efficient Semantic Network Construction with Application to PubMed Search

Abstract

Exploring PubMed to find relevant information is challenging and time-consuming because PubMed typically returns a long list of articles as a result of query. Semantic network helps users to explore a large document collection and to capture key concepts and relationships among the concepts. The semantic network also serves to broaden the user's knowledge and extend query keyword by detecting and visualizing new related concepts or relations hidden in the retrieved documents. The problem of existing semantic network techniques is that they typically produce many redundant relationships, which prevents users from quickly capturing the underlying relationships among concepts. This paper develops an online PubMed search system, which displays semantic networks having no redundant relationships in real-time as a result of query. To do so, we propose an efficient semantic network construction algorithm, which prevents producing redundant relationships during the network construction. Our extensive experiments on actual PubMed data show that the proposed method (COMPACT) is significantly faster than the method removing redundant relationships afterward. Our method is implemented and integrated into a relevance-feedback PubMed search engine, called RefMed, "http://dm.postech.ac.kr/refmed". (c) 2012 Elsevier B.V. All rights reserved.