Fuzzy Knowledge Management through Knowledge Engineering and Fuzzy Logic

2010 ◽  
Vol 5 (3) ◽  
pp. 7-15 ◽  
Author(s):  
LienFu Lai ◽  
LiangTsung Huang ◽  
ChaoChin Wu ◽  
ShiShan Chen
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Management ◽  
Knowledge Engineering ◽  
Fuzzy Knowledge

scholarly journals Hybrid Fuzzy Knowledge Based Prediction Model for the Software Development and Maintenance Quality in Software Engineering Approach

2020 ◽  
Vol 9 (10) ◽  
pp. 316-321
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Knowledge Management ◽  
Software Engineering ◽  
Software Development ◽  
Average Error ◽  
Error Evaluation ◽  
Engineering Development ◽  
Fuzzy Knowledge

The main arena of Software Engineering development with ood design, development, coding, testing, implementation, deployment of the software, finally maintaining the software with good functionality. For the development of software many organizations are investing more and more budget in their revenue. Software Engineering development has several categories of data presented in software engineering such as Graphical User Interface, Usage graphs, writing text, realities and images. Significant information be able to be obtained from this composite data by well recognized data mining techniques such as association, classification, clustering etc. By discovery hidden patterns by data mining software engineering data is made illegal. Software Engineering development has many objectives in software engineering such as Code and Design optimization, Project documentation, Development cost estimation etc. Variety of significant data mining method in each phase of software development life cycle supports in realizing these objectives proficiently and the failure rate of software is decreased. . This paper focused a new hybrid model like combination of Fuzzy Logic and knowledge management offers a significant method for developing models for software quality prediction. This research paper explains about exercise of estimate and valuation at a particular organization by developments and represents the outcomes attained with a fuzzy based classification and knowledge model for the fuzzy knowledge management predication for the quality of software engineering Approach. This result illustrate that the significance of Average Error Evaluation Efficiency observed and used in fuzzy logic is lesser than Average Error Evaluation Efficiency used in another regression multiple regression; while the value of prediction is higher value that other prediction models is used before. Thus Results demonstrate that Hybrid fuzzy knowledge management predication for the quality of software engineering can be used as alternative for predicting the Software Development and Maintenance Quality (SDMQ).

Assessing Knowledge Management with Fuzzy Logic

2004 ◽  
pp. 425-432 ◽  
Author(s):  
Gholamreza Khoshsima ◽  
Caro Lucas ◽  
Ali Mohaghar
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Management

Ontologies and E-Learning

2005 ◽  
pp. 345-356
Author(s):  
Matteo Cristani
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Natural Language Processing ◽  
Knowledge Sharing ◽  
Language Processing ◽  
Information Integration ◽  
Knowledge Engineering ◽  
Formal Ontologies ◽  
E Learning ◽  
Intelligent Information

What is an ontology? Why is this relevant to a learning environment? It is quite well-established in recent investigations on information systems that formal ontologies area crucial problem to deal with, and in fact, received a lot of attention in several different communities, such as knowledge management, knowledge engineering, natural language processing, intelligent information integration, and so on (Fensel, 2000).Ontologies have been developed in artificial intelligence to facilitate knowledge sharing and reuse. The viewpoint we adopt here is taken from the general considerations on the use of philosophical issues in artificial intelligence: “the systematic, formal, axiomatic development of the logic of all forms and modes of being” (Cocchiarella,1991). Another commonly accepted definition is that an ontology is an explicit specification of a shared conceptualization that holds in a particular context.

Ontologies and E-Learning

2011 ◽  
pp. 322-331 ◽  
Author(s):  
Matteo Cristani
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Natural Language Processing ◽  
Knowledge Sharing ◽  
Language Processing ◽  
Information Integration ◽  
Knowledge Engineering ◽  
Formal Ontologies ◽  
E Learning ◽  
Intelligent Information

What is an ontology? Why is this relevant to a learning environment? It is quite well-established in recent investigations on information systems that formal ontologies area crucial problem to deal with, and in fact, received a lot of attention in several different communities, such as knowledge management, knowledge engineering, natural language processing, intelligent information integration, and so on (Fensel, 2000).Ontologies have been developed in artificial intelligence to facilitate knowledge sharing and reuse. The viewpoint we adopt here is taken from the general considerations on the use of philosophical issues in artificial intelligence: “the systematic, formal, axiomatic development of the logic of all forms and modes of being” (Cocchiarella,1991). Another commonly accepted definition is that an ontology is an explicit specification of a shared conceptualization that holds in a particular context.

Better Knowledge Management Through Knowledge Engineering: A Case Study in Drilling Optimisation

2001 ◽  
pp. 63-76 ◽  
Author(s):  
Alun Preece ◽  
Alan Flett ◽  
Derek Sleeman ◽  
David Curry ◽  
Nigel Meany ◽  
...  
Keyword(s):  
Knowledge Management ◽  
Knowledge Engineering
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