Author :
Sun, Chengzhu ; Xu, Xiaofei ; Li, Xiangyang ; Deng, Shengchun
Abstract :
Notice of Violation of IEEE Publication Principles
"Knowledge Discovery from Virtual Enterprise Model Based on Semantic Annotation,"
by Chengzhu Sun, Xiaofei Xu, Xiangyang Li, Shengchun Deng,
in the Proceedings of the Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008. FSKD \´08, vol.5, pp.546-551, Oct. 2008
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
"A Controlled Language for Semantic Annotation and Interoperability in e-Business Applications"
by M.Missikoff, F.Schiappelli , F.Taglino
in the Proceedings of the Semantic Integration Workshop, collocated with the Second International Semantic Web Conference (ISWC-03), October 2003
Discovering knowledge from virtual enterprise model is becoming increasingly important, as numerical models established for virtual enterprise are difficult to support interoperability of virtual enterprise. To solve this problem, a knowledge discovery method based on semantic annotation is put forward in this paper. A graphic ontology representation schema for virtual enterprise model is described, which is called ontology structure graph (OSG). Based on applications of semantic annotation, the process of knowledge discovery in the virtual enterprise model base is given and activities such as model selection, semantic annotation, data transformation, knowledge extraction and ontology interoperation in knowledge discovery process are illustrated in detail. Then, several critical issues influencing knowledge discovery are explained, including the organization of domain vocabulary, - the definition of semantic annotation rules and semantic affinity function, the formulation of reference ontology. Finally, an instance is given to demonstrate the knowledge discovery method and results of knowledge discovery are presented.
Keywords :
data mining; ontologies (artificial intelligence); virtual enterprises; data transformation; domain vocabulary; graphic ontology representation; knowledge discovery; knowledge extraction; model selection; ontology interoperation; ontology structure graph; reference ontology; semantic affinity function; semantic annotation; virtual enterprise; Knowledge discovery; ontology; semantic annotation; virtual enterprise model;
