Harvesting Domain Specific Ontologies from Text

Ontologies are a vital component of most knowledge-based applications, including semantic web search, intelligent information integration, and natural language processing. In particular, we need effective tools for generating in-depth ontologies that achieve comprehensive converge of specific application domains of interest, while minimizing the time and cost of this process. Therefore we cannot rely on the manual or highly supervised approaches often used in the past, since they do not scale well. We instead propose a new approach that automatically generates domain-specific ontologies from a small corpus of documents using deep NLP-based text-mining. Starting from an initial small seed of domain concepts, our OntoHarvester system iteratively extracts ontological relations connecting existing concepts to other terms in the text, and adds strongly connected terms to the current ontology. As a result, OntoHarvester (i) remains focused on the application domain, (ii) is resistant to noise, and (iii) generates very comprehensive ontologies from modest-size document corpora. In fact, starting from a small seed, OntoHarvester produces ontologies that outperform both manually generated ontologies and ontologies generated by current techniques, even those that require very large well-focused data sets.