Semantic wiki-based knowledge management system by interleaving ontology mapping tool
|Semantic wiki-based knowledge management system by interleaving ontology mapping tool|
|Published in||International Journal of Software Engineering and Knowledge Engineering|
|Keyword(s)||collaborative editing, Knowledge management system, ontology mapping, semantic annotation, semantic consistency, semantic wiki (Extra: Collaborative Editing, Knowledge management system, Ontology mapping, Semantic annotations, Semantic consistency, Semantic wiki, Knowledge acquisition, Knowledge based systems, Mapping, Semantics)|
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Semantic wiki-based knowledge management system by interleaving ontology mapping tool is a 2013 journal article written in English by Jung J.J. and published in International Journal of Software Engineering and Knowledge Engineering.
Many organizations have been employing Knowledge Management Systems (KMS) to improve task performance. In this paper, we propose a novel KMS by using semantic wiki framework based on a centralized Global Wiki Ontology (GWO). The main aim of this system is (i) to collect as many organizational resources as possible, and (ii) to maintain semantic consistency of the system. When enriching the KMS in a particular domain, not only linguistic resources but also conceptual structures can be efficiently captured from multiple users, and more importantly, the resources can be automatically integrated with the GWO of the KMS in real time. Once users add new organization resources, the proposed KMS can formalize and contextualize them into a set of triplets by referring to a predefined pattern-triplet mapping table and the GWO. Especially, since the ontology mapper is interleaved, the KMS can determine whether the new resources are semantically conflicted with the GWO. To evaluate the proposed methodology, we have implemented the semantic wiki-based KMS. As a case study, two user groups were invited to collect the organization resources. We found that the group with the proposed KMS has shown better performance than the other group with traditional wiki-based KMS.
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