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  4. Too big to match: a strategy around matching tasks for large taxonomies
 
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2022
Conference Paper
Title

Too big to match: a strategy around matching tasks for large taxonomies

Abstract
Following the introduction of a new matching task at the biodiversity and ecology track of the Ontology Alignment Evaluation Initiative (OAEI), to align between two large taxonomies, we acknowledged the fact that large ontologies or taxonomies still cannot be efficiently tackled by state-of-the-art ontology matching systems. In this paper, we take advantage of structural specificities of taxonomies to devise a strategy for deviding large scale taxonomy matching tasks into smaller, more manageable subtasks. Our modularization approach is based on a locality-based module extraction technique. We conducted a first assessment of the coverage of the obtained modules as well as a preliminary evaluation using a set of tools from OAEI.
Author(s)
Algergawy, Alsayed
Friedrich-Schiller-Universität Jena  
Karam, Naouel  
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS  
Laadhar, Amir
Universität Stuttgart  
Michel, Franck
Université Côte d'Azur
Mainwork
Ontology Matching 2022  
Project(s)
SFB 1076: AquaDiva: Forschungsverbund zum Verständnis der Verknüpfungen zwischen der oberirdischen und unterirdischen Biogeosphäre
Biodiversität, Ökologie und Umweltdaten  
Nationale Forschungsdateninfrastruktur für Materialwissenschaft & Werkstofftechnik  
Funder
Deutsche Forschungsgemeinschaft -DFG-, Bonn  
Deutsche Forschungsgemeinschaft -DFG-, Bonn  
Deutsche Forschungsgemeinschaft -DFG-, Bonn  
Conference
International Workshop on Ontology Matching 2022  
International Semantic Web Conference 2022  
Open Access
DOI
10.24406/publica-1003
File(s)
om2022_too_big.pdf (930.96 KB)
Rights
CC BY 4.0: Creative Commons Attribution
Language
English
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS  
Keyword(s)
  • ontology matching

  • large taxonomies

  • NCBITAXON

  • TAXREF-LD

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