2016 IEEE International Conference on Computer and Information Technology (CIT) 2016
DOI: 10.1109/cit.2016.115
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Ontology Techniques for Representing the Problem of Discourse: Design of Solution Application Perspective

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Cited by 2 publications
(1 citation statement)
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“…Similarity measures play a vital role in case retrieval, and Liao et al focused on similarity measuring methods for CBR and proposed a hybrid similarity measure for comparing cases with a mixture of crisp and fuzzy features [28]. Ontology techniques enable one to define the structures of knowledge components and their relationship, which has been widely introduced in design cases for representing the problem universe of discourse [29]. Armaghanab suggested introducing the multi-criteria decision concept in problem representation description and proposed decision models such as the ELECTRE-I and ELECTRE-II based on knowledge acquisition, which could seek solutions from non-compensatory multi-criteria decision aids [30].…”
Section: Literature Reviewmentioning
confidence: 99%
“…Similarity measures play a vital role in case retrieval, and Liao et al focused on similarity measuring methods for CBR and proposed a hybrid similarity measure for comparing cases with a mixture of crisp and fuzzy features [28]. Ontology techniques enable one to define the structures of knowledge components and their relationship, which has been widely introduced in design cases for representing the problem universe of discourse [29]. Armaghanab suggested introducing the multi-criteria decision concept in problem representation description and proposed decision models such as the ELECTRE-I and ELECTRE-II based on knowledge acquisition, which could seek solutions from non-compensatory multi-criteria decision aids [30].…”
Section: Literature Reviewmentioning
confidence: 99%