Shiva Prasad KM, Department of Computer Science and Engineering, Rao Bahadur Y Mahabaleswarappa Engineering College, Affiliated to VTU Belagavi Karnataka India
T. Hanumantha Reddy, Department of Computer Science and Engineering, Rao Bahadur Y Mahabaleswarappa Engineering College, Affiliated to VTU Belagavi Karnataka India
Huge text is available in digital form. Topic modelling which is meant for assigning meaningful labels for discovering the topics in various documents has gained a lot of attention in this area of research. There are digital books and the complex thesis which are written and available for use. Searching for relevant content inside this large corpus is difficult. It is possible to search with keywords if they are present or not, but the relative importance of those keywords inside this large corpus is still difficult to establish. In this paper Ontology-based Biterm Topic Modelling Approach (O-BTM) is proposed which embeds the concepts of ontology instead of words alone which employ squat-text topic models to establish important topics inside an automated summarized context of the large text. The outcomes of our inspections carried on thesis-oriented datasets by accessing the Google drive show that incorporating ontological notions as additional, richer properties between subjects, terms, and defining concept themes provides an adequate method of creating meaningful tags for the learned topics. The experimental analysis of O_BTM model is found more effective and the efficiency of this model is 85% compared to other traditional models such as R_BTM, BTM and LDA model.
Squat Text, Summarization, Context Analysis, Topic Modelling, Topic Labelling, Statistical Learning, Ontology
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APA:
Prasad KM, S.,& Reddy, T. H. (2020). Onto-Btm: Ontology-Based Biterm Topic Modelling for Automatic Topic Labelling. International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, 144, 17-34. doi: 10.33832/ijast.2020.144.02.
MLA:
Prasad KM, Shiva et al. “Onto-Btm: Ontology-Based Biterm Topic Modelling for Automatic Topic Labelling.” International Journal of Advanced Science and Technology, ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 144, 2020, pp. 17-34. IJAST, http://article.nadiapub.com/IJAST/Vol144/2.html.
IEEE:
[1] S. Prasad KM, and T. Hanumantha Reddy, "Onto-Btm: Ontology-Based Biterm Topic Modelling for Automatic Topic Labelling." International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 144, pp. 17-34, November 2020.
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