Abstract
This article addresses the task of mining named relationships between concepts from biomedical literature for indexing purposes or for scientific discovery from medical literature. This research builds on previous work on concept mining from medical literature for indexing purposes and proposes to learn semantic relationships names between concepts learnt. Previous ConceptMiner system did learn pairs of concepts, expressing a relationship between two concepts, but did not learn relationships semantic names. Building on ConceptMiner, RelationshipMiner is interested in learning as well the relationships with their name identified from the Unified Medical Language System (UMLS) knowledge-base as a basis for creating higher-level knowledge structures, such as rules, cases, and models, in future work. Current system is focused on learning semantically typed relationships as predefined in the UMLS, for which a dictionary of synonyms and variations has been created. An evaluation is presented showing that actually this relationship mining task improves the concept mining task results by enabling a better screening of the relationships between concepts for relevant ones.
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Bichindaritz, I. (2006). Named Relationship Mining from Medical Literature. In: Perner, P. (eds) Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining. ICDM 2006. Lecture Notes in Computer Science(), vol 4065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11790853_6
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DOI: https://doi.org/10.1007/11790853_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36036-0
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