Abstract
Knowledge management (KM) facilitates the capture, storage, and dissemination of knowledge using information technology. In this paper, we propose a FKM (Fuzzy Knowledge Management) approach to managing fuzzy knowledge through knowledge engineering and fuzzy logic. First, fuzziness is introduced into CGs (Conceptual Graphs) for constructing fuzzy knowledge models. Fuzzy knowledge models are used to organize and express various types of fuzzy knowledge through fuzzy CGs. Fuzzy inference rules in fuzzy CGs are identified to offer the deduction capability for reasoning about fuzzy knowledge. Second, fuzzy knowledge models can be classified and stored in a hierarchical ontology system. Ontologies serve as the common understanding of fuzzy knowledge and facilitate the finding of specific fuzzy knowledge relevant to a given domain.
| Original language | English |
|---|---|
| Pages (from-to) | 7-15 |
| Number of pages | 9 |
| Journal | Journal of Convergence Information Technology |
| Volume | 5 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2010 May |
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All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Computer Networks and Communications
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Fuzzy knowledge management through knowledge engineering and fuzzy logic. / Lai, Lien Fu; Huang, Liang Tsung; Wu, Chao Chin; Chen, Shi Shan.
In: Journal of Convergence Information Technology, Vol. 5, No. 3, 05.2010, p. 7-15.Research output: Contribution to journal › Article
TY - JOUR
T1 - Fuzzy knowledge management through knowledge engineering and fuzzy logic
AU - Lai, Lien Fu
AU - Huang, Liang Tsung
AU - Wu, Chao Chin
AU - Chen, Shi Shan
PY - 2010/5
Y1 - 2010/5
N2 - Knowledge management (KM) facilitates the capture, storage, and dissemination of knowledge using information technology. In this paper, we propose a FKM (Fuzzy Knowledge Management) approach to managing fuzzy knowledge through knowledge engineering and fuzzy logic. First, fuzziness is introduced into CGs (Conceptual Graphs) for constructing fuzzy knowledge models. Fuzzy knowledge models are used to organize and express various types of fuzzy knowledge through fuzzy CGs. Fuzzy inference rules in fuzzy CGs are identified to offer the deduction capability for reasoning about fuzzy knowledge. Second, fuzzy knowledge models can be classified and stored in a hierarchical ontology system. Ontologies serve as the common understanding of fuzzy knowledge and facilitate the finding of specific fuzzy knowledge relevant to a given domain.
AB - Knowledge management (KM) facilitates the capture, storage, and dissemination of knowledge using information technology. In this paper, we propose a FKM (Fuzzy Knowledge Management) approach to managing fuzzy knowledge through knowledge engineering and fuzzy logic. First, fuzziness is introduced into CGs (Conceptual Graphs) for constructing fuzzy knowledge models. Fuzzy knowledge models are used to organize and express various types of fuzzy knowledge through fuzzy CGs. Fuzzy inference rules in fuzzy CGs are identified to offer the deduction capability for reasoning about fuzzy knowledge. Second, fuzzy knowledge models can be classified and stored in a hierarchical ontology system. Ontologies serve as the common understanding of fuzzy knowledge and facilitate the finding of specific fuzzy knowledge relevant to a given domain.
UR - http://www.scopus.com/inward/record.url?scp=78651531512&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78651531512&partnerID=8YFLogxK
U2 - 10.4156/jcit.vol5.issue3.1
DO - 10.4156/jcit.vol5.issue3.1
M3 - Article
AN - SCOPUS:78651531512
VL - 5
SP - 7
EP - 15
JO - Journal of Convergence Information Technology
JF - Journal of Convergence Information Technology
SN - 1975-9320
IS - 3
ER -