A Semantic-based Clustering Method to Build Domain Ontology from Multiple Heterogeneous Knowledge Sources

In order to improve the efficiency of ontology construction from heterogeneous knowledge sources, a semantic-based approach is presented. The ontology will be constructed with the application of cluster technique in an incremental way. Firstly, terms will be extracted from knowledge sources and congregate a term set after pretreat-ment. Then the concept set will be built via semantic-based clustering according to semanteme of terms provided by WordNet. Next, a concept tree is constructed in terms of mapping rules between semant^me relationships and concept relationships. The semi-automatic approach can avoid non-consistence due to knowledge engineers having different understanding of the same concept and the obtained ontology is easily to be expanded.

Using the KISO's Framework to Integrate Knowledge Sources

In complicated application environment such as CIMS (Computer Integrated Manufacture System) enterprise, it will bring great benefits to integrate distributed knowledge sources. But the difficulties of knowledge sharing and reuse seriously encumbrance the implementation of knowledge integration. In this paper, we describe how a framework of knowledge-integrated system based on ontology (KISO) can be used to support integrating distributed knowledge sources.

Epistemological Attitudes Towards Sources of Knowledge Semantic Questionnaire

Given the everlasting significance of knowledge in society and academia,this article proposes a theoretical and methodological perspective on conceptualizing and investigating it.Specifically,it aims to explore the epistemological attitude(EA)theory and its semantic approach to assessing sources of knowledge.The article provides a concise overview of the EA theory,which advocates for a systemic perspective on cognition and knowledge.It introduces and elaborates on the core concept and model,which serve as the foundation for the proposed methodology.This methodology suggests examining knowledge objects through subjective,contextual,and epistemological realms as multi-level knowledge constructs.Emphasizing the importance of semantics in studying knowledge,categories,and meanings,the article proposes an epistemological attitude towards sources of knowledge semantic questionnaire.The article delves into the methodology,reflecting on its four consecutive stages.It begins with the formal and substantive stages,which involve selecting sources,choosing academic experts as target participants,and developing content.The procedural stage follows,in which an expert review approach is employed to assess the content validity of the method.Finally,the article discusses the semantic method,elucidating its structure,features,semantic categories,and assessment procedure.The proposed method provides a unique contribution by enabling the analysis of the epistemological and socio-psychological meanings of sources,representing them as semantic constructs.

Artificial Neural Network Method Based on Expert Knowledge and Its Application to Quantitative Identification of Potential Seismic Sources

In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.