  In this paper, we describe an approach to populate an existing ontology with
instance information present in the natural language text provided as input. An
ontology is defined as an explicit conceptualization of a shared domain. This
approach starts with a list of relevant domain ontologies created by human
experts, and techniques for identifying the most appropriate ontology to be
extended with information from a given text. Then we demonstrate heuristics to
extract information from the unstructured text and for adding it as structured
information to the selected ontology. This identification of the relevant
ontology is critical, as it is used in identifying relevant information in the
text. We extract information in the form of semantic triples from the text,
guided by the concepts in the ontology. We then convert the extracted
information about the semantic class instances into Resource Description
Framework (RDF3) and append it to the existing domain ontology. This enables us
to perform more precise semantic queries over the semantic triple store thus
created. We have achieved 95% accuracy of information extraction in our
implementation.
