AI Tutor: A Computer Science Domain Knowledge Graph-Based QA System on JADE platform
In this paper, we proposed an AI Tutor using ontology and natural language process techniques to generate a computer science domain knowledge graph and answer users’ questions based on the knowledge graph. We define eight types of relation to extract relationships between entities according to the computer science domain text. The AI tutor is separated into two agents: learning agent and Question-Answer (QA) agent and developed on JADE (a multi-agent system) platform. The learning agent is responsible for reading text to extract information and generate a corresponding knowledge graph by defined patterns. The QA agent can understand the users’ questions and answer humans’ questions based on the knowledge graph generated by the learning agent.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 738
 R. S. T. Lee, Fuzzy-Neuro Approach to Agent Applications. Berlin: Springer, 2006, pp. 56-68.
 Z. Memon, A. H. Jalbani, M. Shaikh, R. N. Memon, and A. Ali, "Multi-Agent Communication System with Chatbots," Mehran University Research Journal of Engineering & Technology, vol. 37, pp. 663-672, July 2018.
 F. Bellifemine, G. Caire, and D. Greenwood, Developing Multi-Agent Systems With JADE. England: John Wiley & Sons Ltd, 2007.
 B. Hettige and A. S. Karunananda, "Octopus: A Multi Agent Chatbot," in Proc. 8th International Research Conference, KDU, 2015, pp. 41-47.
 R. Kumar and C. P. Rosé, "Architecture for Building Conversational Agents that Support Collaborative Learning," IEEE Trans. Learning Technologies, vol. 4, pp. 21-34, January-March 2011.
 E. H. Y. Lim, J. N. K. Liu, and R. S. T. Lee, Knowledge Seeker- Ontology Modeling for Information Search and Management. Berlin: Springer, 2011.
 R. M. Reese, Natural Language Processing with Java. Birmingham: Packt, 2015.