Semantic Relation Discovery by Using Co-occurrence Information Background: MEDLINE contains high quality semantic metadata covering more than 22 million.

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Semantic Relation Discovery by Using Co-occurrence Information Background: MEDLINE contains high quality semantic metadata covering more than 22 million bibliographic records, by manually assigned MeSH descriptors. Can this resource be used as a “non-ontological knowledge” layer on top of the clinical ontology SNOMED CT? BioTxtM2014 – Fourth Workshop on Building and Evaluating Resources for Health and Biomedical Text Processing Stefan Schulz, Catalina Martínez Costa, Markus Kreuzthaler, Jose A. Miñarro-Giménez, Ulrich Andersen, Anders B. Jensen, Bente Maegaard qualify the source concept, e.g. DT = drug therapy, PC = prescription and control, CO = complication

Semantic Relation Discovery by Using Co-occurrence Information Hypothesis: the following combination of information permits the generation of factoid Subject – Predicate – Object statements: Example: A high score of the “TU” qualifier on Substance allows to induce the predicate “treats” with Disorder as object; a high score of the “PC” qualifier suggests “prevents”, accordingly BioTxtM2014 – Fourth Workshop on Building and Evaluating Resources for Health and Biomedical Text Processing Stefan Schulz, Catalina Martínez Costa, Markus Kreuzthaler, Jose A. Miñarro-Giménez, Ulrich Andersen, Anders B. Jensen, Bente Maegaard MeSH co-occurrence in MEDLINE (source UMLS) MeSH subheading profiles (source UMLS) MeSH – UMLS – SNOMED CT mappings SNOMED CT semantic types Subject Object

Semantic Relation Discovery by Using Co-occurrence Information Results: Preliminary testing for “treats” and “prevents”. Results are promising, however requiring further refinement. Outlook: Publication as linked data. Possible use cases: question answering, query expansion, decision support, knowledge discovery, background knowledge for different NLP applications BioTxtM2014 – Fourth Workshop on Building and Evaluating Resources for Health and Biomedical Text Processing Stefan Schulz, Catalina Martínez Costa, Markus Kreuzthaler, Jose A. Miñarro-Giménez, Ulrich Andersen, Anders B. Jensen, Bente Maegaard