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Identification of Circular RNAs using RNA Sequencing
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UMLS to DBPedia link discovery through circular resolution.

John Cuzzola1, Ebrahim Bagheri1, Jelena Jovanovic2

  • 1Laboratory for Systems, Software and Semantics (LS3), Ryerson University, Ontario, Canada.

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Summary

This study introduces circular resolution to map Unified Medical Language System (UMLS) concepts to DBpedia resources, creating a comprehensive, publicly available dataset. The method achieved 83% recall and 77% precision-at-one.

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Area of Science:

  • Medical Informatics
  • Knowledge Representation
  • Linked Data

Background:

  • The Unified Medical Language System (UMLS) is a crucial resource for medical information.
  • DBpedia provides structured information extracted from Wikipedia.
  • Linking these two knowledge bases enhances data interoperability.

Purpose of the Study:

  • To develop and evaluate a method for mapping UMLS concepts to DBpedia resources.
  • To create a comprehensive and publicly available mapping between UMLS and DBpedia.
  • To leverage ontology relations for robust knowledge base integration.

Main Methods:

  • A novel 'circular resolution' method using semantic annotators.
  • Annotation of UMLS concept definitions and DBpedia resource abstracts.
  • Alignment of annotations to establish UMLS-to-DBpedia mappings using skos:exactMatch, skos:closeMatch, and rdfs:seeAlso relations.

Main Results:

  • Generated a complete mapping of UMLS (2016AA) to DBpedia (2015-10).
  • The mapping includes over 221,000 skos:exactMatch, 26,000 skos:closeMatch, and 6.7 million rdfs:seeAlso links.
  • Achieved 83% recall and 77% precision-at-one (P@1) on a Wikidata-based test set.

Conclusions:

  • The circular resolution method is effective for linking UMLS to DBpedia.
  • The study provides a valuable, high-accuracy mapping resource.
  • Accessible mapping data via download, online browser, and RESTful service.