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Domain ontology is used as a reliable source of knowledge in information retrieval systems such as question answering systems. Automatic ontology construction is possible by extracting concept relations from unstructured large-scale text. In this paper, we propose a methodology to extract concept relations from unstructured text using a syntactic and semantic probability-based Naïve Bayes classifier. We propose an algorithm to iteratively extract a list of attributes and associations for the given seed concept from which the rough schema is conceptualized. A set of hand-coded dependency parsing pattern rules and a binary decision tree-based rule engine were developed for this purpose. This ontology construction process is initiated through a question answering process. For each new query submitted, the required concept is dynamically constructed, and ontology is updated. The proposed relation extraction …