Knowledge Representation and Reasoning (KRR) is a core area of artificial intelligence concerned with how information, concepts, relationships, rules, and domain knowledge can be captured in a form that machines can understand and reason with. Modern AI excels at learning patterns from data, but making decisions that are explainable, consistent, and grounded in real-world constraints often requires structured knowledge and logical reasoning.
As AI expands into fields like healthcare, education, agriculture, governance, disaster response, and scientific research, the need for systems that can reason, not just predict, continues to grow. Knowledge representation enables complex information to be organized into structured and meaningful representations. It helps capture expert knowledge, connect siloed data from diverse sources, and support reasoning over this knowledge to infer new insights, identify inconsistencies, answer complex questions, and guide intelligent decision-making.
Our research develops the methods, frameworks, and tools that allow AI systems to represent domain knowledge effectively and reason over it reliably and transparently. This work spans ontologies, knowledge graphs, semantic technologies, rule-based systems, logic-based reasoning, neuro-symbolic AI, and explainable AI.
Through this research, CAIR-Nepal aims to build intelligent systems that are not only data-driven but also knowledge-aware for solving real-world problems.
Sustainable Development Goals:
Our research on responsible AI aligns with the following Sustainable Development Goals of the United Nations:
