1 Introduction Temporal Knowledge Graphs(TKGs)provide a dynamic framework for modeling evolving events and relationships over time,with applications ranging from stock market to international politics.As to stock mark...1 Introduction Temporal Knowledge Graphs(TKGs)provide a dynamic framework for modeling evolving events and relationships over time,with applications ranging from stock market to international politics.As to stock market,TKGs can model how these relationships change over time,enabling the prediction of stock price movements,market trends,and potential risks.While graph-based methods such as Graph Neural Networks(GNNs)[1,2]have been widely adopted for TKG extrapolation,we argue that their structural focus often overshadows the critical role of historical information.Historical periodicity and temporal patterns serve as the foundation for effective temporal reasoning,particularly in forecasting future events.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62020106005,42050105,62061146002)Shanghai Pilot Program for Basic Research-Shanghai Jiao Tong University.
文摘1 Introduction Temporal Knowledge Graphs(TKGs)provide a dynamic framework for modeling evolving events and relationships over time,with applications ranging from stock market to international politics.As to stock market,TKGs can model how these relationships change over time,enabling the prediction of stock price movements,market trends,and potential risks.While graph-based methods such as Graph Neural Networks(GNNs)[1,2]have been widely adopted for TKG extrapolation,we argue that their structural focus often overshadows the critical role of historical information.Historical periodicity and temporal patterns serve as the foundation for effective temporal reasoning,particularly in forecasting future events.
