ISG Talks are sponsored by Couchbase.
- This event has passed.
Prof. Eduard Dragut (Temple University): Toward Scalable Knowledge Extraction with Weak Supervision and Large Language Models
April 24 @ 1:00 pm - 2:00 pm
Friday April 24, 2026, 1:00 PM – 2:00 PM
Donald Bren Hall 3011, ICS, UC Irvine
Zoom:
https://uci.zoom.us/j/
Lunch will be provided.
Title: Toward Scalable Knowledge Extraction with Weak Supervision and Large Language Models
Abstract: Information extraction is a foundational capability for transforming unstructured text into structured knowledge, enabling downstream applications such as knowledge graph construction, semantic search, question answering, and scientific discovery. However, building high-quality extraction systems traditionally depends on large manually annotated datasets, which are costly to create and often impractical in specialized domains. In this talk, I will present recent advances toward scalable information extraction under limited supervision. I will discuss methods for improving the quality of weakly supervised training data through automatic label cleaning, show how richer benchmarks over full scientific documents expose new challenges for scientific information extraction beyond simplified abstract-level settings, and demonstrate how large language models can be leveraged in many-shot in-context learning regimes to perform competitive named entity recognition and generate high-quality annotations for low-resource domains. Together, these results suggest a promising path toward scalable knowledge extraction pipelines that reduce reliance on expensive manual annotation while improving the robustness and adaptability of systems used to build next-generation knowledge graphs and AI applications.
Bio: Eduard Dragut is a Professor in the Department of Computer and Information Sciences at Temple University. He is a senior member of the IEEE. He received his Ph.D. in Computer Science from the University of Illinois at Chicago. His research focuses on data management, information retrieval, and applied artificial intelligence, with an emphasis on building scalable systems for extracting and integrating knowledge from large and heterogeneous data sources. He also pursues interdisciplinary AI projects for social good, including work on assistive technologies such as augmentative and alternative communication (AAC) and AI-driven tools for knowledge discovery. He has published widely in leading venues in databases, natural language processing, and data mining.