@conference {IT601,
	title = {IT601: Identifying Missing Hierarchical Relations in SNOMED CT from Logical Definitions Based on the Lexical Features of Concept Names},
	booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)},
	series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)},
	year = {2016},
	month = {11/30/16},
	publisher = {CEUR-ws.org Volume 1747},
	organization = {CEUR-ws.org Volume 1747},
	abstract = {<p>Objectives. To identify missing hierarchical relations in SNOMED CT from logical definitions based on the lexical features of concept names. Methods. We first create logical definitions from the lexical features of concept names, which we represent in OWL EL. We infer hierarchical (subClassOf) relations among these concepts using the ELK reasoner. Finally, we compare the hierarchy obtained from lexical features to the original SNOMED CT hierarchy. We review the differences manually for evaluation purposes. Results. Applied to 15,833 disorder and procedure concepts, our approach identified 559 potentially missing hierarchical relations, of which 78\% were deemed valid. Conclusions. This lexical approach to quality assurance is easy to implement, efficient and scalable.</p>
},
	url = {http://ceur-ws.org/Vol-1747/IT601_ICBO2016.pdf},
	author = {Olivier Bodenreider}
}
@conference {BT302,
	title = {BT302: Annotations for biomedical research and healthcare {\textendash} Bridging the gap},
	booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)},
	series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)},
	year = {2016},
	month = {11/30/16},
	publisher = {CEUR-ws.org Volume 1747},
	organization = {CEUR-ws.org Volume 1747},
	abstract = {<p>"Characterizing protein products from various model organisms with Gene Ontology terms, indexing the biomedical literature with MeSH descriptors, and coding clinical data with ICD10-CM all constitute examples of annotation tasks, i.e., the extraction ands summarization of knowledge related to a biological entity, article or patient, in reference to some controlled vocabulary or ontology. However, the annotations made in biomedical research and healthcare environments tend to rely on different terminologies and ontologies, making it difficult to reconcile these annotations for translational research purposes. We will discuss how terminology integration systems, such as the Unified Medical Language System (UMLS) and BioPortal, can help bridge the gap between annotations made by biomedical researchers and physicians, and argue that more efforts are needed to foster interoperability between the resources developed by these two communities."</p>
},
	url = {http://ceur-ws.org/Vol-1747/BT302_ICBO2016.pdf},
	author = {Olivier Bodenreider}
}
