Mining ontological knowledge from domain-specific text documents

Traditional text mining systems employ shallow parsing techniques and focus on concept extraction and taxonomic relation extraction. This paper presents a novel system called CRCTOL for mining rich semantic knowledge in the form of ontology from domain-specific text documents. By using a full text parsing technique and incorporating both statistical and lexico-syntactic methods, the knowledge extracted by our system is more concise and contains a richer semantics compared with alternative systems. We conduct a case study wherein CRCTOL extracts ontological knowledge, specifically key concepts and semantic relations, from a terrorism domain text collection. Quantitative evaluation, by comparing with a state-of-the-art ontology learning system known as text-to-onto, has shown that CRCTOL produces much better precision and recall for both concept and relation extraction, especially from sentences with complex structures.

1. Introduction

An ontology is an explicit specification of a conceptualization [2], comprising a formal description of concepts, relations between concepts, and axioms about a target domain. Considered as the backbone of the Semantic Web [1], domain ontologies enable software agents to interact and carry out sophisticate tasks for users.