ChatGPT

Towards a Universal Knowledge Graph Chatbot: ChatGPT versus Traditional Question Answering for Knowledge Graphs

Prof.Essam Mansour, Computer Science and Software Engineering, Concordia University

May 1, 12:00 - 13:00

B9 L2 H2 H2

ChatGPT conversational AI QASs knowledge graphs

Conversational AI and Question-Answering systems (QASs) for knowledge graphs (KGs) are both emerging research areas: they empower users with natural language interfaces for extracting information efficiently and effectively. While Conversational AI simulates human-like conversations, its effectiveness is limited by the available training data. However, QASs retrieve the most up-to-date information from KGs by translating natural language queries into formal queries that the database engine can process. In this talk, we examine the characteristics of existing approaches for combining Conversational AI and QASs to create novel KG chatbots. We also introduce KGQAn, a universal QA system that can be applied to any KG without the need for customization.

KAUST-CEMSE-CS-Vision-CAIR-Mohamed-Elhoseiny

Mohamed Elhoseiny

Associate Professor, Computer Science

artificial intelligence Computer Vision ChatGPT MiniGPT

Professor Elhoseiny’s research focuses on developing affective artificial intelligence that understands and generates novel visual content. He has contributed to and led numerous seminal works of affective AI art creation.