Advancing Domain-Specific Intelligence with LLMs & KGs: A Five-Year Journey Toward the Future

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B9 L2 R2325

This talk will cover my research journey in AI, focusing on developing AI-powered Knowledge Graph engines, LLM-driven chatbot platforms, and data science automation, leading to impactful collaborations and publications and striving towards LLM-powered generative AI in specialized domains, exploring their potential and limitations across various applications.

In this talk, I will share my five-year journey advancing AI, Knowledge Graphs (KGs), and data science automation to build cutting-edge systems. I developed an AI-enabled KG engine that optimizes AI infrastructure by bridging Graph DBs and Graph ML frameworks. Our system introduces novel training and inference accelerators. I also created an LLM-powered chatbot platform for KGs to enhance domain-specific question answering by using LLMs for understanding and linking tasks. Additionally, I advanced data science with semantic abstraction and KG-powered automation via graph neural networks. My work has led to around 10 top-tier publications (SIGMOD, PVLDB, ICDE) and open-sourced systems. I have collaborated with industry leaders like Google and IBM, Canadian banks like RBC and NBC, and research institutions like the National Research Council Canada.

I will also present my vision for LLM-powered generative AI in specialized domains. My talk will showcase how LLMs could enhance and reshape domain-specific intelligence while addressing their strengths and limitations. My group’s recent work explores LLMs as benchmark creators, data scientists, and security analysts. These examples demonstrate the industry impact of LLMs. Finally, I will introduce my framework for LLM-powered domain-specific intelligence and its role in the future of generative AI for specialized domains.

Presenters

Essam Mansour, Associate Professor, Computer Science and Software Engineering Concordia University, Montreal

Brief Biography

Dr. Essam Mansour is an associate professor in the Department of Computer Science and Software Engineering at Concordia University in Montreal and the head of the Cognitive Data Science lab (CoDS). Over the past decade, he has led pioneering research in AI for databases, AI infrastructure optimization, knowledge graphs (KGs), large language models (LLMs), graph neural networks, and distributed/parallel data systems. In the last five years, Dr. Mansour has developed a promising research program in linked data science for federated and heterogeneous datasets. This program has achieved significant milestones, securing over $750K in federal and industry funding and forming strategic research projects with industry leaders, such as Google, IBM, RBC, and National Bank of Canada (NBC). His group is developing AI-powered systems optimized for scalability on supercomputers and cloud platforms. His research has resulted in over 30 publications in top-tier conferences and journals, including SIGMOD, PVLDB, and ICDE. Dr. Mansour is a regular reviewer for prestigious journals such as ACM TODS, VLDB Journal, and IEEE TKDE, and has served on the program committees of PVLDB, SIGMOD, and ICDE.

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