Domain-Specific Ontology Concept Extraction and Hierarchy Extension

Abstract

The domain-specific vernaculars and notations have been a hurdle to automatic ontology building and augmentation, since most of the ontology learning methods are essentially based on the natural language studies and lexicosyntactic pattern explorations. This paper proposes two robust approaches to ontology hierarchical enhancement, in particular, adding new terms to the ontology graph. We designed our learning models from a computational vantage point, examining the inter-relationship between documents, ontology dictionary terms, and the graph structure of the seed ontology. We then take advantage of late studies of neural networks and machine learning to perform classification over the inter-related data, and insert the new term at the most desirable nodal place on the domain ontology graph.