Ontology development through concept map and text analytics: The case of automotive safety ontology

Abstract

Ontology development is an expensive and time-consuming process. The development of real-world organizational ontology-based knowledge management systems is still in early stages. Some existing ontologies with simple tuples and properties are not designed for domain specific requirement, or does not utilize existing knowledge from organizational database or documents. Here we propose our concept map approach to first semi-automatically create a detailed level entities/concepts as a keyword list by applying natural language processing, including word dependency and POS tagging. Then this list can be used to extract entities/concepts for the same domain. This approach is applied to automotive safety domain. The results are further mapped to existing ontology and aggregated to form a concept map. We implement our approach in KNIME with Stanford NLP parser and generate a concept map from automotive safety complaint dataset. The final results expand the existing ontology, and also bridge the gap between ontology and real-world organization ontology-based knowledge management systems.