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 hierarchical ontologies

Breadcrumb

  • Home
  • Research
  • Publications
  • Instance-based matching of hierarchical ontologies

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

Instance-based matching of hierarchical ontologies

Proc. of 12. GI-Fachtagung für Datenbanksysteme in Business, Technologie und Web (BTW), 2007

2007 / 03

Andere

Abstract

We study an instance-based approach for matching hierarchical ontologies such as product catalogs. The motivation for utilizing instances is that metadata-based match approaches often suffer from semantic heterogeneity, e.g. ambiguous concept names, different concept granularities or incomparable categorizations. Our instance-based match approach matches categories based on the instances (e.g. products) assigned to them. This way we partly translate the ontology match problem into an instance (object) match problem which is often easier to solve, especially when instances carry globally unique object ids. Since concepts of different ontologies rarely match 1:1 we propose to determine correspondences between sets of concepts. We experimentally demonstrate the usefulness of such correspondences for real product catalogs.

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 / 7: MPGT: Multimodal Physics-Constrained Graph Transformer Learning for Hybrid Digital Twins
  • 2025 / 6: Leveraging foundation models and goal-dependent annotations for automated cell confluence assessment
  • 2025 / 6: SecUREmatch: Integrating Clerical Review in Privacy-Preserving Record Linkage

Footer menu

  • Directions
  • Contact
  • Impressum