Semantic association rule mining in text using domain ontology. (2017) - Afolabi, Ibukun; Sowunmi, Olaperi; Daramola, Olawande
This paper reports a procedure for ontology-based association rule mining for knowledge extraction from text. Association rule mining (ARM) algorithms have the limitations of generating many non-interesting rules, huge number of discovered rules, and low algorithm performance. This research demonstrates a procedure for improving the performance of ARM in text mining by using domain ontology. A study context of Nigerian politics using news text from a Nigerian online newspaper was selected, and a methodology that combined natural language processing, ontology-based keywords extraction, and the modified Generating Association Rules based on Weighting (GARW) scheme was applied. The result revealed significant rule reduction in the number of generated rules, and produced rules, which are more semantically related to the problem context when compared to when ARM approaches that are not ontology-based is used. The study shows that domain ontology can improve the performance of ARM algorithms when dealing with unstructured textual data.