Overview
This seminar explores the role of Neurosymbolic AI in building trustworthy knowledge graphs from text, speech, and images. It discusses how Large Language Models (LLMs) can be combined with symbolic reasoning and formal verification techniques to improve the accuracy, reliability, and explainability of automatically generated knowledge. The session will also present practical frameworks, ontology validation methods, and real-world applications of verified knowledge graphs.
The session will present state-of-the-art frameworks for evidence-based knowledge extraction, automated verification, ontology quality assessment, and semantic validation using OWL reasoning, SHACL, and OOPS!. Participants will gain insights into how verified knowledge can be systematically constructed, evaluated, and deployed for real-world applications, with demonstrations using a food and health knowledge graph. The seminar highlights the importance of integrating AI-driven automation with formal semantic technologies to develop reliable and explainable knowledge engineering systems.
Objectives of Event
- To introduce the fundamentals of neurosymbolic knowledge graph construction.
- To explain the opportunities and challenges of using LLMs for automated knowledge extraction.
- To demonstrate evidence-based verification and ontology validation techniques for ensuring trustworthy knowledge.
- To present state-of-the-art tools and frameworks for knowledge graph construction and evaluation.
- To highlight real-world applications of reliable and explainable AI in domains such as healthcare, food, and semantic technologies.
Convener Details
Prof. Dr. Sanju Tiwari, Professor CSA and Member CAIMIF, Sharda University
Prof Dr. Ashok Kumar, Professor SBSR and Head CAIMIF, Sharda University
Co-ordinators:
Naveen Lamba, Research Scholar
