Unsupervised Domain Ontology Learning from Text

Construction of Ontology is indispensable with rapid increase in textual information. Much research in learning Ontology are supervised and require manually annotated resources. Also, quality of Ontology is dependent on quality of corpus which may not be readily available. To tackle these problems, we present an iterative focused web crawler for building corpus and an unsupervised framework for construction of Domain Ontology. The proposed framework consists of five phases, Corpus Collection using Iterative Focused crawling with novel weighting measure, Term Extraction using HITS algorithm, Taxonomic Relation Extraction using Hearst and Morpho-Syntactic Patterns, Non Taxonomic relation extraction using association rule mining and Domain Ontology Building. Evaluation results show that proposed crawler outweighs traditional crawling techniques, domain terms showed higher precision when compared to statistical techniques and learnt ontology has rich knowledge representation.