An Upper-Level Ontology for the Biomedical Domain

By Alexa T. McCray. 2003

229 citations recorded by the source corpus.

At the US National Library of Medicine we have developed the Unified Medical Language System (UMLS), whose goal it is to provide integrated access to a large number of biomedical resources by unifying the vocabularies that are used to access those resources. The UMLS currently interrelates some 60 controlled vocabularies in the biomedical domain. The UMLS coverage is quite extensive, including not only many concepts in clinical medicine, but also a large number of concepts applicable to the broad domain of the life sciences. In order to provide an overarching conceptual framework for all UMLS concepts, we developed an upper-level ontology, called the UMLS semantic network. The semantic network, through its 134 semantic types, provides a consistent categorization of all concepts represented in the UMLS. The 54 links between the semantic types provide the structure for the network and represent important relationships in the biomedical domain. Because of the growing number of information resources that contain genetic information, the UMLS coverage in this area is being expanded. We recently integrated the taxonomy of organisms developed by the NLM's National Center for Biotechnology Information, and we are currently working together with the developers of the Gene Ontology to integrate this resource, as well. As additional, standard, ontologies become publicly available, we expect to integrate these into the UMLS construct.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

A semantic matching energy function for learning with multi-relational data (2013, methodology): “UMLS This dataset contains data from the Unified Medical Language System semantic work gathered by McCray (2003). This consists in a graph with 135 entities and 49 relation types.”
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal (2022, unknown): “Facts # Test Facts ATOMIC [205] 304,388 9 610,536 87,700 87,701 Countries [206] 271 2 1,110 24 24 CoDEX-S [207] 2,034 42 32,888 3,654 3656 CoDEX-M [207] 17,050 51 185,584 20620 20622 CoDEX-L [207] 77,951 69 551,193 30,622 30622 ConceptNet [208] 28,370,083 50 27,259,933 3,407,492 3,407,492 ConceptNet”
Statistical predicate invention (2007, methodology): “UMLS contains data from the Unified Medical Language System, a biomedical ontology (McCray, 2003).”
Learning Systems of Concepts with an Infinite Relational Model (2006, methodology): “We demonstrate here that the IRM discovers a simple biomedical ontology given data from the Unified Medical Language System (UMLS, McCray 2003 ).”
Structured Embedding via Pairwise Relations and Long-Range Interactions in Knowledge Base (2015, methodology): “001]; (ii) for datasets Kinship (Denham 1973) and UMLS (McCray 2003), the dimensionality of the embedding space is set to d = 10; for datasets WordNet (Miller 1995) and FreeBase (Bollacker et al.”
Semi-Supervised Learning to Identify UMLS Semantic Relations (2014, methodology): “This hierarchy has been developed and populated by 1 Italic font in the main paper denotes the matching relations in the UMLS Semantic Network [1]. hand in a top-down manner and undergoes periodic manual revisions [2].”
A latent factor model for highly multi-relational data (2012, methodology): “This dataset contains data from the Unified Medical Language System semantic work gathered by [12].”