Decision Support Tools for Ontological Engineering - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 49

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Decision Support Tools
for Ontological Engineering
Simon Suigen Guo, Christine W. Chan and Robert Harrison
Energy Informatics Laboratory,
Faculty of Engineering and Applied Science
University of Regina
Canada
1. Introduction
Development cost of knowledge-based systems is often high because knowledge bases are
often constructed from scratch in a distributed and heterogeneous environment involving
different locations, system types, knowledge representation mechanisms and stakeholders.
Hence, it is difficult to share and re-use existing knowledge base components.
Sharing and re-using knowledge can help reduce costs and make it possible to create
systems capable of more powerful problem solving. The Semantic Web (SW) is the next
evolutionary step for the World Wide Web (Web). The SW aims to provide semantics to
data on the Web, enabling computers to more easily share and perform problem solving on
the data (Berners-Lee et al. 2001). SW technology can be used to share and re-use knowledge
between KBSs in a distributed and heterogeneous environment.
Semantic data networks on the Web provide the bases of the SW, enabling knowledge that is
distributed over the Web to be easily shared and processed by a machine. A semantic data
structure is required for representing data or knowledge in a shareable format, and an
ontology is designed to fulfill this objective. According to the World Wide Web Consortium,
an ontology is “a specification of a conceptualization” (Gruber, 1993); ontologies are the
vocabularies of the SW that define the concepts and relationships used to describe an area of
interest. An ontology represents the definitions of concepts, axioms and facts which discribe
real world phenomenon of interest. As suggested in (Gruber, 1993), the declarative
formalisms and the set of objects represented as domain knowledge in a knowledge-based
system (KBS) can be described by an ontology. Hence, an ontology can also become the
basis for building knowledge-based systems. Ontologies implemented in XML 1 based
languages, such as RDF 2 and OWL 3 , enable different KBS development groups or different
Semantic Web applications to share and re-use their knowledge and data.
Knowledge Acquisition (KA) is an important step for building knowledge-based systems
(KBS). In the process, software tools are often used for helping knowledge engineers
construct ontologies and knowledge bases more efficiently and effectively. The objective of
1 Extensible Markup Language, http://www.w3.org/XML/
2 Resource Description Framework (RDF), http://www.w3.org/RDF/
3 OWL Web Ontology Language, http://www.w3.org/TR/owl-features/