ABSTRACT
Progress in artificial intelligence has always led people to believe in intelligent machines. But software agents that are expected to replicate human intelligence are typically knowledge-based systems. These systems are built with domain knowledge acquired from domain experts and other information sources. As these systems grow in size and become more complicated, acquiring the knowledge to build them becomes a challenge. In this context, automating the process of knowledge generation becomes a desired objective. The World Wide Web consortium, which has been working to formulate standards for an intelligent web or Semantic Web, has presented ontologies as an ideal technology for representing knowledge. An ontology encodes a shared vocabulary that can be used for modeling a domain. This vocabulary contains the terms of the domain, mainly the types of concepts that represent the domain, their properties, and the relations between them. In fact, it has been established that ontologies are an excellent medium for capturing domain knowledge. An ontology can also be used to reason about the concepts and properties of that domain to generate new knowledge about the domain, if required. Thus a domain ontology (or domain-specific ontology) successfully models a specific domain, or part of the world.
