Notice of Violation of IEEE Publication Principles - Knowledge Discovery from Virtual Enterprise Model Based on Semantic Annotation

Sun, Chengzhu; Xu, Xiaofei; Li, Xiangyang; Deng, Shengchun

doi:10.1109/fskd.2008.150

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Cited by 3 publications

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“…One of the most important factors in KM is knowledge discovery. Proliferation of data has created a completely new and different area of KM 17 requiring the extraction of knowledge from abundant data and the organization and merging of this knowledge with existing knowledge. Existing knowledge supports organizations in creating new knowledge and updating the overall KB.…”

Section: Literature Surveymentioning

confidence: 99%

Description logic–based knowledge merging for concrete- and fuzzy-domain ontologies

Kumar

Harding

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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Section: Literature Surveymentioning

confidence: 99%

Description logic–based knowledge merging for concrete- and fuzzy-domain ontologies

Kumar

Harding

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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Fine-grained Association Rules toward knowledge discovery

Safaei

2009

2009 Fifth International Conference on Soft Computing, Computing With Words and Perceptions in System Analysis, Decision and Co

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A Semantic Mapping Method based on Ontology Structure Graph

Sun¹,

Chen

Zhong

et al. 2015

Proceedings of the 4th International Conference on Information Technology and Management Innovation

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Efficient information sharing and exchanging are becoming increasingly important , but model heterogeneity hinder the communication and collaboration from different enterprises. To solve this problem, a semantic mapping method based on ontology structure graph is put forward in this paper. The requirement of model semantic interoperability among multiple enterprise model is analyzed , and model semantic interoperability is defined. Then, semantic clash between enterprise models is summarized and illustrated. Moreover, the formalization of semantic mapping rules is presented to support semantic mapping process from enterprise model to ontology. Besides, based on the description of ontology representation schema called ontology structure graph (OSG) and definition of semantic similarity function, semantic mapping process including model element choice, ontology elements selection, 3-level semantic matching based on OSG, semantic mapping rules generation , semantic mapping rules executing is illustrated in detail. Finally, the conclusions and future work are presented.

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Notice of Violation of IEEE Publication Principles - Knowledge Discovery from Virtual Enterprise Model Based on Semantic Annotation

Cited by 3 publications

References 10 publications

Description logic–based knowledge merging for concrete- and fuzzy-domain ontologies

Description logic–based knowledge merging for concrete- and fuzzy-domain ontologies

Fine-grained Association Rules toward knowledge discovery

A Semantic Mapping Method based on Ontology Structure Graph

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