Data quality measurement is a prerequisite to assure and improve the quality of data, thus enabling reliable decision-making. In alignment with the “fitness for use” definition, most existing solutions for measuring data quality are either (1) tailored to...
The purpose of these datasets is to support equivalence and subsumption ontology matching. There are five ontology pairs extracted from MONDO and UMLS: Source Ontology Pair Category MONDO OMIM-ORDO Disease MONDO NCIT-DOID Disease UMLS SNOMED-FMA Body UMLS SNOMED-NCIT Pharm UMLS SNOMED-NCIT Neoplas Each pair is associated with three folders: "raw_data", "equiv_match", and "subs_match", corresponding to the downloaded source ontologies, the package for equivalence matching, and the package for subsumption matching. See detailed documentation at: https://krr-oxford.github.io/DeepOnto/#/om_resources. See the incoming OAEI Bio-ML track at: https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/. See our resource paper at: https://arxiv.org/abs/2205.03447.
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D. Cantone, V. Di Caro, C. Longo, S. Menza, M. Asmundo, and D. Santamaria. Proceedings of the fourth edition of the International Workshop on Semantic Web and Ontology Design for Cultural Heritage, Tours, France, October 30-31, 2024, volume 3809 of CEUR Workshop Proceedings, CEUR-WS.org, (2024)