Ontology Merging Using the Weak Unification of Concepts

Knowledge representation and manipulation in knowledge-based systems typically rely on ontologies. The aim of this work is to provide a novel weak unification-based method and an automatic tool for OWL ontology merging to ensure well-coordinated task completion in the context of collaborative agents...

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Main Authors: Norman Kuusik, Jüri Vain
Format: Article
Language:English
Published: MDPI AG 2024-08-01
Series:Big Data and Cognitive Computing
Subjects:
Online Access:https://www.mdpi.com/2504-2289/8/9/98
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author Norman Kuusik
Jüri Vain
author_facet Norman Kuusik
Jüri Vain
author_sort Norman Kuusik
collection DOAJ
description Knowledge representation and manipulation in knowledge-based systems typically rely on ontologies. The aim of this work is to provide a novel weak unification-based method and an automatic tool for OWL ontology merging to ensure well-coordinated task completion in the context of collaborative agents. We employ a technique based on integrating string and semantic matching with the additional consideration of structural heterogeneity of concepts. The tool is implemented in Prolog and makes use of its inherent unification mechanism. Experiments were run on an OAEI data set with a matching accuracy of 60% across 42 tests. Additionally, we ran the tool on several ontologies from the domain of robotics. producing a small, but generally accurate, set of matched concepts. These results clearly show a good capability of the method and the tool to match semantically similar concepts. The results also highlight the challenges related to the evaluation of ontology-merging algorithms without a definite ground truth.
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spelling doaj-art-51659580183b4e9ebd9c4d110bb0080b2025-08-20T01:56:05ZengMDPI AGBig Data and Cognitive Computing2504-22892024-08-01899810.3390/bdcc8090098Ontology Merging Using the Weak Unification of ConceptsNorman Kuusik0Jüri Vain1Department of Software Science, Tallinn University of Technology, 19086 Tallinn, EstoniaDepartment of Software Science, Tallinn University of Technology, 19086 Tallinn, EstoniaKnowledge representation and manipulation in knowledge-based systems typically rely on ontologies. The aim of this work is to provide a novel weak unification-based method and an automatic tool for OWL ontology merging to ensure well-coordinated task completion in the context of collaborative agents. We employ a technique based on integrating string and semantic matching with the additional consideration of structural heterogeneity of concepts. The tool is implemented in Prolog and makes use of its inherent unification mechanism. Experiments were run on an OAEI data set with a matching accuracy of 60% across 42 tests. Additionally, we ran the tool on several ontologies from the domain of robotics. producing a small, but generally accurate, set of matched concepts. These results clearly show a good capability of the method and the tool to match semantically similar concepts. The results also highlight the challenges related to the evaluation of ontology-merging algorithms without a definite ground truth.https://www.mdpi.com/2504-2289/8/9/98ontology mergingRDF/OWLroboticsPrologsemantic derivation
spellingShingle Norman Kuusik
Jüri Vain
Ontology Merging Using the Weak Unification of Concepts
Big Data and Cognitive Computing
ontology merging
RDF/OWL
robotics
Prolog
semantic derivation
title Ontology Merging Using the Weak Unification of Concepts
title_full Ontology Merging Using the Weak Unification of Concepts
title_fullStr Ontology Merging Using the Weak Unification of Concepts
title_full_unstemmed Ontology Merging Using the Weak Unification of Concepts
title_short Ontology Merging Using the Weak Unification of Concepts
title_sort ontology merging using the weak unification of concepts
topic ontology merging
RDF/OWL
robotics
Prolog
semantic derivation
url https://www.mdpi.com/2504-2289/8/9/98
work_keys_str_mv AT normankuusik ontologymergingusingtheweakunificationofconcepts
AT jurivain ontologymergingusingtheweakunificationofconcepts