报告题目： From Database to Data Science: The Evolution of Knowledge Extraction, Representation, and Reasoning
时　　间：2024年3月5日晚上19:00，
地点：榆中校区天山堂C301

报告内容： This talk explores the research journey from semantic mapping in traditional databases to the realm of data science. We emphasize the pivotal role of knowledge graphs in extracting, retrieving, and reasoning about information. Knowledge graphs, characterized by directed labeled graphs, serve as powerful tools for representing domain knowledge, enabling effective information retrieval and recommendation.
The process of converting raw data into meaningful information and further extracting knowledge poses significant challenges. We address these challenges by developing innovative semantic mapping methods between diverse data representations. Our ultimate goal is to integrate distributed and disparate information into a cohesive knowledge graph, creating a comprehensive and interconnected knowledge base.
Specifically, we delve into the application of these knowledge graph methods in the materials science domain, with a focus on metal-organic frameworks. For future direction, we highlight the emerging synergy between large language models (LLMs) and knowledge graphs. This collaboration promises to open new frontiers in information processing, enabling more sophisticated and context-aware knowledge extraction, representation, and reasoning.

安远老师简介: Dr. Yuan An is currently a faculty member in the College of Computing and Informatics at Drexel University, Philadelphia, USA. His research centers around semantic information extraction and integration through AI and ML. He has collaborated with domain experts in healthcare and materials science with applications of AI/ML. Dr. Yuan An earned a PhD degree from the Department of Computer Science, University of Toronto, Canada. He also has a MEng degree and a BEng degree from the Department of Automation, Tsinghua University, Beijing, China.