MoA: OWL Ontology Merging and Alignment Tool for the Semantic Web

Kim, Jae Hong; Jang, Minsu; Ha, Young-Guk; Sohn, Joo-Chan; Lee, Sang Jo

doi:10.1007/11504894_100

Cited by 15 publications

(8 citation statements)

References 3 publications

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“…Another multi-technique system named as MoA (Dou et al 2002) follows a hybrid mechanism adopting features of both approaches. Its intermediate output is related to ONION (Mitra and Wiederhold 2002) as it produces articulation rules, and final output as a new merged ontology like other semiautomatic system Prompt (Kim et al 2005).…”

Section: Related Workmentioning

confidence: 99%

Merging of axiomatic definitions of concepts in the complex OWL ontologies

Fahad

2016

Artif Intell Rev

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In the last decade, ontology matching and mapping research has shown a measurable progress. This topic draws substantial attention within the research community, though it is not fully researched so far and new complex and effective solutions are needed. Current works are limited in finding alignments or mappings between concepts of heterogeneous ontologies. But, once ontology mappings are found, then how they (or their class expressions) are to be integrated automatically is left open for the ontology merging research. This paper elaborates the mapping of class expressions of concepts and contributes an algorithm for their merging in an automatic ontology merging process without any human intervention. However, the challenge of mapping axiomatic definitions is the most difficult task for merging concept definitions of the source ontologies, but it reveals significant increase in precision and recall values. In addition, with the study of these algorithms, we conclude that ontology merging facilitates when one wants to get ontology with the better quality as the combined rich axioms are added in the merged ontology. We also discuss the results of our first successful participation in the Conference, OA4QA and Anatomy track of OAEI 2015.

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Section: Related Workmentioning

confidence: 99%

Merging of axiomatic definitions of concepts in the complex OWL ontologies

Fahad

2016

Artif Intell Rev

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“…However, merging ontologies raises the problem of solving the inconsistencies or incoherences that might arise, which are difficult problems for which several distinct theoretic approaches have been proposed [8], and much effort has been put on the development of tools to assist with ontology merging, see e.g. [24].…”

Section: Related Workmentioning

confidence: 99%

Information Flow within Relational Multi-context Systems

Cruz-Filipe

Gaspar

Nunes

2014

Lecture Notes in Computer Science

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Abstract. Multi-context systems (MCSs) are an important framework for heterogeneous combinations of systems within the Semantic Web. In this paper, we propose generic constructions to achieve specific forms of interaction in a principled way, and systematize some useful techniques to work with ontologies within an MCS. All these mechanisms are presented in the form of general-purpose design patterns. Their study also suggests new ways in which this framework can be further extended.

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“…Nearly all of the approaches seek to learn similarity and equivalence relations between components across schemas or ontologies; only a few have sought to learn others [8], [9], and even those are restricted to hypernymy, hyponymy and inclusion. Some solutions discover subsumption [10]. Therefore, acquiring additional properties between ontological components remains a research challenge [11], [3].…”

Section: Related Workmentioning

confidence: 99%

Acquiring advanced properties in ontology mapping

Stoutenburg

2008

Proceedings of the 2nd PhD Workshop on Information and Knowledge Management

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With the emergence of the Semantic Web, ontologies are being exposed on the Web to support a variety of applications, including enhanced search and discovery, rapid enterprise integration and cross-domain knowledge sharing. This explosion of knowledge sharing on the web is also leading to the formation of knowledge silos and a growing need for integration of these sources. Automated solutions to mapping ontologies are emerging that address this growing need with very promising results. However, most approaches have focused on mapping ontologies using relationships of similarity and equivalence and very few have applied knowledge in upper ontologies. We built algorithms to acquire relationships between ontological components beyond similarity and equivalence that include hyponymy and more importantly, algorithms to map ontologies based on relationships that are specified within the ontologies. These algorithms employ the semantics of OWL in conjunction with online linguistic resources and upper ontologies. Initial test results are promising.

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MoA: OWL Ontology Merging and Alignment Tool for the Semantic Web

Cited by 15 publications

References 3 publications

Merging of axiomatic definitions of concepts in the complex OWL ontologies

Merging of axiomatic definitions of concepts in the complex OWL ontologies

Information Flow within Relational Multi-context Systems

Acquiring advanced properties in ontology mapping

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