Abstract
The query used to retrieve information related to the medical domain may not contain the technical terms which are used in the medical industry. The user query should include more relevant terms and therefore, query expansion technique is required in the medical domain for their Information Retrieval Systems. In this paper, a metadata driven semantically aware medical query expansion methodology is proposed. The proposed approach takes a query as an input which is preprocessed and then Latent Semantic Indexing is used to generate new topics for each query word. A set of ontologies of PubMed keywords are semantically aligned using Lesk similarity and Normalized Pointwise Mutual Information. A Knowledge Tree is formed which is used to classify the metadata generated from Google Books using Recurrent Neural Networks. Finally, the terms from the Knowledge Tree are enriched using Wikidata, CASNET, and Hepatitis Knowledge Base, and are semantically integrated with 25% of the classified metadata using Normalized Pointwise Mutual Information under the Social Spider algorithm. The proposed MDSA-MQE methodology achieves the Precision of 90.12%, Recall of 93.87%, Accuracy of 92.08%, F-Measure of 91.95%, and Normalized Discounted Cumulative Gain value of 0.94 making it a better approach than the baseline approaches.
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Ojha, R., Deepak, G. (2021). Metadata Driven Semantically Aware Medical Query Expansion. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M. (eds) Knowledge Graphs and Semantic Web. KGSWC 2021. Communications in Computer and Information Science, vol 1459. Springer, Cham. https://doi.org/10.1007/978-3-030-91305-2_17
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