Self-learning knowledge base using Naïve Bayes classifier

Article type: Research Article

Authors: Lodhi, Pooja | Mishra, Omji | Jain, Shikha^*

Affiliations: Department of Computer Science, Jaypee Institute of Information Technology, Noida, India

Correspondence: [*] Corresponding author: Shikha Jain, Department of Computer Science, Jaypee Institute of Information Technology, Noida, India. E-mail: shi_81@rediffmail.com.

Abstract: With the abundance of technology, digital natives prefer to be more comfortable and effortless. They expect an instant, correct and crisp response to their queries. Keeping this in mind, this paper proposes an agent, HELP, which fetches the unstructured data from the web, aligns it with the structured knowledge base by transforming it into rules and updates the CLIPS knowledge base progressively. It helps to conquer the issue of format non-uniformity. The proposed model is simulated for a technical education university. The objective is to provide an interface to the newcomers who are unfamiliar with the rules, regulations, and policies of the new environment and at the same time reluctant to pose to seniors or faculty.

Keywords: Self-learning knowledge base, Naïve Bayes classifier, learning agent, CLIPS knowledge base, automatic rules extraction, expert system

DOI: 10.3233/IDT-180352

Journal: Intelligent Decision Technologies, vol. 12, no. 4, pp. 491-498, 2018