Word Vector Embeddings and Domain Specific Semantic based Semi-Supervised Ontology Instance Population

Abstract

An ontology defines a set of representational primitives which model a domain of knowledge or discourse. With the arising fields such as information extraction and knowledge management, the role of ontology has become a driving factor of many modern day systems. Ontology population, on the other hand, is a inherently problematic process, as it needs manual intervention to prevent the conceptual drift. The semantic sensitive word embedding has become a popular topic in natural language processing with its capability to cope with the semantic challenges. Incorporating domain specific semantic similarity with the word embeddings could potentially improve the performance in terms of semantic similarity in specific domains. Thus, in this study we propose a novel way of semi-supervised ontology population through word embeddings and domain specific semantic similarity as the basis. We built several models including traditional benchmark models and new types of models which are based on word embeddings. Finally, we ensemble them together to come up with a synergistic model which outperformed the candidate models by 33% in comparison to the best performed candidate model