Large-scale ontology storage and query using graph database-oriented approach: The case of Freebase

Ontology has been increasingly recognised as an instrumental artifact to help make sense of large amounts of data. However, the challenges of Big Data significantly overburden the process of ontology storage and query particularly. In this respect, the paper aims to convey considerations in relation to improving the practice of storing or querying large-scale ontologies. Initially, a systematic literature review is conducted with the aim of thoroughly inspecting the state-of-the-art in literature. Subsequently, a graph database-oriented approach is proposed, considering ontology as a large graph. The approach endeavours to address the limitations encountered within traditional relational models. Furthermore, scalability and query efficiency of the approach are verified based on empirical experiments using a subset of Freebase data. The Freebase subset is utilised to build a large-scale ontology graph composed of more than 500K nodes, and 2M edges.