Two-stage Temporal Knowledge Graph Reasoning Based on Jointly Evolutional Modeling of Multiple History Sequences
Temporal knowledge graphs integrate temporal information into traditional knowledge graphs and describe such dynamic event knowledge by sequences of knowledge graphs with timestamps.The temporal knowledge graph reasoning task aims to predict future events based on the historical event quadruples(subject entity,relation(event type),object entity,timestamp).To characterize the evolution process of the historical events comprehensively,this paper proposes a two-stage model,called MENet(Multi-sequence Evolution Network),based on jointly evolu-tional modeling of multiple history sequences.Specifically,in the first candidate entity selection stage,a candidate entity selection strategy is designed via heuristic rules,thus effectively reducing the number of entities to be mod-eled.In the second stage,it combines the long-term historical sequence of multiple entities to form a graph se-quence,and models the evolution process of entities by capturing the structural dependency of concurrent events,the time value information of events,and the temporal dependencies across different timestamps.Experimental re-sults on three standard datasets show that the proposed model outperforms state-of-the-art ones.
万方数据
维普
