Semantic expansion network based relevance analysis for medical information retrieval

Abstract

Complex networks provide quantitative measures for complex systems, thus enabling effective semantic network analysis. This research aims to develop semantic relevance analysis methods for medical information retrieval to answer questions for clinical decision support system. We proposed a query based semantic expansion network for semantic relevance analysis in medical information retrieval tasks. Empirical studies of the network structure and attributes for discriminant relevance analysis revealed that expansion networks for relevant documents have a compact structure, which provides new features to identify relevant documents. We also found the existence of densely connected nodes as hubs in the associative networks for queries. Then, we proposed a novel rescaled centrality measure to evaluate the importance of query concepts in the semantic expansion network. Experiments with real-world data demonstrated that the proposed measure is able to improve the performance for relevance analysis.