Abstract
Diseases and their symptoms are a frequent information need for Web users. Diseases often are categorized into sub-types, manifested through different symptoms. Extracting such information from textual corpora is inherently difficult. Yet, this can be easily extracted from semi-structured resources like tables. We propose an approach for identifying tables that contain information about sub-type classifications and their attributes. Often tables have diverse and redundant schemas, hence, we align equivalent columns in disparate schemas s.t. information about diseases are accessible through a unified and a common schema. Experimental evaluation shows that we can accurately identify tables containing disease sub-type classifications and additionally align equivalent columns.
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Koutraki, M., Fetahu, B. (2020). MedTable: Extracting Disease Types from Web Tables. In: Harth, A., et al. The Semantic Web: ESWC 2020 Satellite Events. ESWC 2020. Lecture Notes in Computer Science(), vol 12124. Springer, Cham. https://doi.org/10.1007/978-3-030-62327-2_26
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DOI: https://doi.org/10.1007/978-3-030-62327-2_26
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