Knowledge Engineering and Knowledge Management

2012 ◽  
Keyword(s):  
Knowledge Management ◽  
Knowledge Engineering

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.

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

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

Formal Knowledge Engineering Methods for Knowledge Discovery

2002 ◽  
Vol 01 (02) ◽  
pp. 141-154
Author(s):  
Satheesh Ramachandran
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Life Cycle ◽  
Decision Support ◽  
Knowledge Discovery ◽  
Knowledge Engineering ◽  
The Other ◽  
Full Potential ◽  
Formal Knowledge

This paper presents a framework for the integrated use of formal knowledge engineering methods and data mining based knowledge discovery methods. Knowledge is a key enterprise asset, and organizations are adopting both knowledge engineering and knowledge discovery paradigms for better knowledge management and enhanced decision support capability. Although there exists a useful interdependence between these endeavors, not much effort has been focused on using the full potential of one for the other. This paper presents a framework for the integrated use of established formal knowledge engineering methods and knowledge discovery processes with the ultimate intent of better managing the enterprise knowledge life cycle. It provides a brief overview of the knowledge discovery processes, and introduces a class of formal knowledge engineering methods and the perceived role of these methods in supporting the integration between the two worlds of knowledge discovery and knowledge engineering.

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