Skip to main content

User account menu

  • Log in
DBS-Logo

Database Group Leipzig

within the department of computer science

ScaDS-Logo Logo of the University of Leipzig

Main navigation

  • Home
  • Study
    • Exams
      • Hinweise zu Klausuren
    • Courses
      • Current
    • Modules
    • LOTS-Training
    • Abschlussarbeiten
    • Masterstudiengang Data Science
    • Oberseminare
    • Problemseminare
    • Top-Studierende
  • Research
    • Projects
      • Benchmark datasets for entity resolution
      • FAMER
      • HyGraph
      • Privacy-Preserving Record Linkage
      • GRADOOP
    • Publications
    • Prototypes
    • Annual reports
    • Cooperations
    • Graduations
    • Colloquia
    • Conferences
  • Team
    • Erhard Rahm
    • Member
    • Former employees
    • Associated members
    • Gallery

Instance-based matching of large life science ontologies

Breadcrumb

  • Home
  • Research
  • Publications
  • Instance-based matching of large life science ontologies

Kirsten, T. ; Thor, A. ; Rahm, E.

Instance-based matching of large life science ontologies

Proc. of 4th Intl. Workshop on Data Integration in the Life Sciences (DILS), 2007

2007 / 06

Paper

Abstract

Ontologies are heavily used in life sciences so that there is increasing value to match different ontologies in order to determine related conceptual categories. We propose a simple yet powerful methodology for instance-based ontology matching which utilizes the associations between molecular-biological objects and ontologies. The approach can build on many existing ontology as-sociations for instance objects like sequences and proteins and thus makes heavy use of available domain knowledge. Furthermore, the approach is flexi-ble and extensible since each instance source with associations to the ontologies of interest can contribute to the ontology mapping. We study several ap-proaches to determine the instance-based similarity of ontology categories. We perform an extensive experimental evaluation to use protein associations for different species to match between subontologies of the Gene Ontology and the OMIM ontology. We also provide a comparison with metadata-based ontology matching.

Recent publications

  • 2025 / 9: Generating Semantically Enriched Mobility Data from Travel Diaries
  • 2025 / 8: Slice it up: Unmasking User Identities in Smartwatch Health Data
  • 2025 / 6: SecUREmatch: Integrating Clerical Review in Privacy-Preserving Record Linkage
  • 2025 / 6: Leveraging foundation models and goal-dependent annotations for automated cell confluence assessment
  • 2025 / 5: Federated Learning With Individualized Privacy Through Client Sampling

Footer menu

  • Directions
  • Contact
  • Impressum