2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-26.2
Paper Title TEMPORAL LINK PREDICTION VIA REINFORCEMENT LEARNING
Authors Ye Tao, Ying Li, Zhonghai Wu, Peking University, China
SessionMLSP-26: Reinforcement Learning 2
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-REI] Reinforcement learning
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract The availability of enormous event data with timestamps has aroused the study of Dynamic Knowledge Graphs (KGs). In dynamic KGs, temporal link prediction is an important task, which predicts future interactions between entities. Compared with conventional statistic link prediction tasks, temporal link prediction has three main challenges: i) How to deal with new entities that we have not observed before. ii) How to model the temporal evolutionary patterns. iii) How to adapt to the changes in KGs without re-training the model. To deal with these challenges, we present a novel reinforcement learning approach with an update mechanism to integrate temporal information. To predict future events, we train a time-aware agent to navigate the graph conditioned on the input query to find predictive paths. The experimental results indicate a clear improvement over the state-of-the-art methods.