Explore a cutting-edge approach to temporal knowledge graph reasoning in this 15-minute conference talk from SIGIR 2024. Delve into the innovative Transformer-based method for learning evolutionary chains of events on temporal knowledge graphs. Discover how authors Zhiyu Fang, Shuai-Long Lei, Xiaobin Zhu, Chun Yang, Shi-Xue Zhang, Xu-Cheng Yin, and Jingyan Qin tackle the challenges of reasoning and knowledge representation in dynamic, time-dependent scenarios. Gain insights into the intersection of natural language processing, graph theory, and temporal logic as applied to evolving knowledge structures.
Transformer-based Reasoning for Learning Evolutionary Chain of Events on Temporal Knowledge Graphs
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 M1.2 [fp] Transformer-based Reasoning for Evolutionary Chain of Events
Taught by
Association for Computing Machinery (ACM)
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