Abstract
This paper presents semi-automated system for establishing integrated ontology by merging two ontologies. Ituses two processes: matching and merging. Matching process uses string-based technique, this technique uses fourmethods: exact method to detect identical terms, and substring, suffix and prefix methods to compare between terms.Using these four methods altogether improve the effectiveness of matching process, matching process uses alsolanguage-based techniques; this technique uses WordNet Method to detect terms that have the same meaning. Thistechnique improves also the effectiveness of matching process. The proposed system presents a merging method oftaxonomies in effective way. The system solves redundancy and inconsistency problem in integrated ontology.Theproposed system is applied on the agricultural domain for Faba Bean crop to get an integrated ontology, it can beapplied also on all crops whatever field crops or horticulture crops. The evaluation of the system shows that theperformance of the system has high quality. The comparison of the proposed system and other systems shows that theproposed system has advantage of using five matching methods for mapping between terms that make the mappingbetween terms more perfect and efficient. The merger algorithm solves problems which appeared in other systems.
Highlights
‘Ontology matching is a key interoperability enabler for the semantic web, as well as a useful tactic in some classical data integration tasks dealing with the semantic heterogeneity problem
Language-based technique [2] relies on using Natural Language Processing (NLP) technique to helpextract the meaningful terms from an ontology
This paper solve inconsistency problem which has appeared during handling hierarchies ontologies lead to an inconsistent merged ontology, this means that some concepts in the integrated ontology are not in the correct place in the taxonomy; this problem is not explained in common systems
Summary
‘Ontology matching is a key interoperability enabler for the semantic web, as well as a useful tactic in some classical data integration tasks dealing with the semantic heterogeneity problem. It takes ontologies as input and determines as output an alignment, that is, a set of correspondences between the semantically related entities of those ontologies. Language-based technique [2] relies on using Natural Language Processing (NLP) technique to helpextract the meaningful terms from an ontology. It uses tokenization method andstopword elimination method These techniques help the proposed system to extract similarities between two ontologies to detect matched terms to reduce redundancy in the output of the merged ontology.
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