Authors:
Omar Qawasmeh
1
;
Maxime Lefrançois
2
;
Antoine Zimmermann
2
and
Pierre Maret
1
Affiliations:
1
Univ. Lyon, CNRS, Lab. Hubert Curien, UMR 5516, F-42023 Saint- Étienne and France
;
2
Univ. Lyon, MINES Saint- Étienne, CNRS, Lab. Hubert Curien, UMR 5516, F-42023 Saint- Étienne and France
Keyword(s):
Ontology, Ontology Evolution, Ontology Adaptation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaboration and e-Services
;
Communication and Software Technologies and Architectures
;
Data Engineering
;
e-Business
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Semantic Web
;
Soft Computing
;
Symbolic Systems
Abstract:
Ontology evolution is the process of maintaining an ontology up to date with respect to the changes that arise in the targeted domain or in the requirements. Inspired by this definition, we introduce two concepts related to observe the impact and the adaptation to the evolution of an imported ontology. In the first one we target the evolution of an imported ontology (if ontology O uses ontology O0, and then O0 evolves). The second one targets the adaptation to the evolution of the imported ontology. Based on our definition we provide a systematic categorization of the different cases that can arise during the evolution of ontologies (e.g. a term t is deleted from O0, but O continues to use it). We led an experiment to identify and count the occurrences of the different cases among the ontologies referenced on two ontology portals: 1. the Linked Open Vocabulary (LOV) ontology portal which references 648 different ontologies, 88 of them evolved. We identified 74 cases that satisfy our
definition, involving 28 different ontologies. 2. the BioPortal which references 770 different ontologies, 485 of them evolved. We identified 14 cases that satisfy our definition, involving 10 different ontologies. We present the observation results from this study and we show the number of different cases that occurred during the evolution. We conclude by showing that knowledge engineers could take advantage of a methodological framework based on our study for the maintenance of their ontologies.
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