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Mondo Ontology

Ontology Mapping Data Integration

Mondo Case Study Infobox

  • Author: Nicolas Matentzoglu (@matentzn)
  • Last updated: 2025-02-15
  • Mapping Type: Mapping Type
  • Status of this case study: Status

Mapping disease terminologies into a harmonized ontology of diseases.

Domain

Biomedical and clinical informatics, focusing on disease classification and standardization.

Purpose of the mapping

To create a unified disease ontology by integrating multiple disease vocabularies and classifications, ensuring semantic interoperability across biomedical databases.

Other purpose of the mapping

  • Facilitating disease data integration for translational research.
  • Enabling cross-resource disease annotations in knowledge graphs.
  • Supporting clinical decision support systems and patient stratification.

Type of mapped resources

  • Biomedical ontologies (e.g., DOID, NCIT).
  • Terminologies and classifications (e.g., ORDO, ICD10, ICD11).
  • Disease-related data from biomedical databases (eg NORD, GARD, OMIM).

Tools used for creating the mapping

  • Lexical matching tools (e.g., OAK lexmatch)
  • Custom scripts (e.g., Python), see Mondo Ingest
  • Manual curation (Domain expert review and ontology alignment)

Type of mapping relations

  • Exact match (skos:exactMatch): A term in an external source is conceptually identical to a term in Mondo.
  • Broad match (skos:broadMatch): A term in an external source is conceptually narrower to a term in Mondo.
  • Narrow match (skos:narrowMatch): A term in an external source is conceptually broader to a term in Mondo.
  • Related match (skos:relatedMatch): A term in an external source is conceptually related, but neither identical, nor broader, nor narrower to a term in Mondo.

Examples (samples) of different types of mapping implementations

See here.