Knowledge engineering: principles and methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
Database middleware for distributed ontologies in state and federal family & social services
dg.o '04 Proceedings of the 2004 annual national conference on Digital government research
Ontology visualization methods—a survey
ACM Computing Surveys (CSUR)
Design of a performance-oriented workplace e-learning system using ontology
Expert Systems with Applications: An International Journal
Class expression learning for ontology engineering
Web Semantics: Science, Services and Agents on the World Wide Web
Journal of Biomedical Informatics
Ontology technology to assist learners' navigation in the concept map learning system
Expert Systems with Applications: An International Journal
Ontology for E-Learning: A Bayesian Approach
IEEE Transactions on Education
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Ontology plays a very important role in supporting knowledge-based applications. In cloud computing, ontology learning technology is facing new challenges in dealing with heterogeneous data sources from different domains and researchers, which may contain various particular concepts and relations. Traditional ontology learning frameworks usually focus only on the extraction of concepts and taxonomic relations from the multi-structured corpus. However, former researches rarely studied the interactions during ontology learning process among different researchers. Lack of interactions among people who build ontology in different domains may cause inconsistent ontology. Besides, lack of incentive during the ontology building process will also result in low efficiency. To address these challenges, this paper specifies a novel solution to perform ontology learning. The solution includes a service-oriented ontology interaction framework, a service-oriented ontology learning strategy. It shows that it advances ontology learning to a higher level of performance and portability with a number of experiments in demo system.