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.
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Acknowledgement
This work was supported by The National Natural Science Foundation of China (NSFC) Grant Nos. 71402157 and 71672163, the Guangdong Provincial Natural Science Foundation No. 2014A030313753, and The Theme-Based Research Scheme of the Research Grants Council of Hong Kong Grant No. T32-102/14Â N.
The authors would like to thank Prof. Guanrong Chen for his constructive advice and guidance in this work.
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Wang, H., Zhang, Q. (2017). Semantic Expansion Network Based Relevance Analysis for Medical Information Retrieval. In: Chen, H., Zeng, D., Karahanna, E., Bardhan, I. (eds) Smart Health. ICSH 2017. Lecture Notes in Computer Science(), vol 10347. Springer, Cham. https://doi.org/10.1007/978-3-319-67964-8_27
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DOI: https://doi.org/10.1007/978-3-319-67964-8_27
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