Building ontology for a specific field of research is a very tedious task; yet, very important. Ontologies can help in defining the boundaries of a discipline and identifying new emerging streams of research. Automating this process reduces, if not eliminates, the overhead associated with manual ontology building methods and gives a big jump to continue refining and improving the generated ontology. Growing Hierarchical Self-Organizing Map (GHSOM) is a promising unsupervised artificial neural networks architecture that can help in identifying hierarchical relations embedded into datasets. Our project-in-progress is exploring the use of GHSOM to generate ontology for the Information Systems (IS) published research.
['Mohammad Saad Al-Ahmadi', 'Ramesh Sharda']
Automating Ontology Generation for Information Systems Research Using GHSOM