Knowledge Extraction from Domain-Specific Documents

Background

Ontology, in its original meaning, is a branch of philosophy (specifically, metaphysics) concerned with the nature of existence. It includes the identification and study of the categories of things that exist in the universe. One scenario of ontology is in Artificial Intelligence, where it is defined as “An ontology is a formal, explicit specification of shared conceptualization. This definition is given by Gruber (Gruber, 1993) which is most commonly used by knowledge engineering community. Here Conceptualization is a “world view” that often present as a set of concepts and their relations. It is the abstract representation of a real world entity (view) with the help of domain relevant concepts (Bhowmick, 2010). Since the ontologist has huge amount of knowledge which is unstructured and it should be organized. Conceptualization helps to organize and structures the acquired knowledge using external representations that are independent of the implementation languages and environments (Arabshian, 2012). Ontology refers to the shared understanding of a domain of interest and is represented by a set of domain relevant concepts, the relationships among the concepts, functions and instances (Bhowmick, 2010).Ontology is used for representing the knowledge of a domain in a formal and machine understandable form in areas like intelligent information processing. Thus it provides the platform for effective extraction of information and many other applications (Choudhary, 2012). It is very useful for expressing and sharing the knowledge of semantic web.