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Matching biodiversity and ecology ontologies: challenges and evaluation results

Part of: Ontology Alignment: Algorithms and Evaluation

Published online by Cambridge University Press:  09 March 2020

Naouel Karam Open the ORCID record for Naouel Karam [Opens in a new window] ,
Abderrahmane Khiat ,
Alsayed Algergawy Open the ORCID record for Alsayed Algergawy [Opens in a new window] ,
Melanie Sattler ,
Claus Weiland Open the ORCID record for Claus Weiland [Opens in a new window]  and
Marco Schmidt Open the ORCID record for Marco Schmidt [Opens in a new window]
Naouel Karam
Affiliation:
Fraunhofer FOKUS, Berlin, Germany e-mail: [email protected]
Abderrahmane Khiat
Affiliation:
Fraunhofer IAIS, Sankt Augustin, Germany e-mail: [email protected]
Alsayed Algergawy
Affiliation:
Friedrich-Schiller University of Jena, Germany e-mail: [email protected]
Melanie Sattler
Affiliation:
MARUM, University of Bremen, Germany e-mail: [email protected]
Claus Weiland
Affiliation:
Senckenberg Biodiversity and Climate Research Center, Frankfurt, Germany e-mail: [email protected]
Marco Schmidt
Affiliation:
Senckenberg Biodiversity and Climate Research Center, Frankfurt, Germany e-mail: [email protected] Palmengarten der Stadt Frankfurt, Germany e-mail: [email protected]
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Abstract

Biodiversity research studies the variability and diversity of organisms, including variability within and between species with particular focus on the functional diversity of traits and their relationship to environment. Managing biodiversity data implies dealing with its heterogeneous nature using semantics and tailored ontologies. These are themselves differently conceived, and combining them in semantically enabled applications necessitates an effective alignment between their concepts. This paper describes the ontology matching of biodiversity- and ecology-related ontologies. We illustrate diverse challenges introduced by this kind of ontologies to ontology matching in general. Real use cases requiring pairwise alignments between environment and trait ontologies are introduced. We describe our experience creating a new track at the Ontology Alignment Evaluation Initiative designed for this specific domain and report on the results obtained by state-of-the-art participating systems. The biodiversity and ecology use case turns out to be a strong one for ontology matching, introducing new interesting challenges. Even if most of the matching systems perform relatively well in the proposed matching tasks, there is still room for improvement. We highlight possible directions in that matter and elaborate on our plan to further progress with the track.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 35 , 2020 , e9
Copyright
© Cambridge University Press 2020

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