HIP Network: Historical Information Passing Network for Extrapolation Reasoning on Temporal Knowledge Graph
IJCAI(2024)
摘要
In recent years, temporal knowledge graph (TKG) reasoning has received
significant attention. Most existing methods assume that all timestamps and
corresponding graphs are available during training, which makes it difficult to
predict future events. To address this issue, recent works learn to infer
future events based on historical information. However, these methods do not
comprehensively consider the latent patterns behind temporal changes, to pass
historical information selectively, update representations appropriately and
predict events accurately. In this paper, we propose the Historical Information
Passing (HIP) network to predict future events. HIP network passes information
from temporal, structural and repetitive perspectives, which are used to model
the temporal evolution of events, the interactions of events at the same time
step, and the known events respectively. In particular, our method considers
the updating of relation representations and adopts three scoring functions
corresponding to the above dimensions. Experimental results on five benchmark
datasets show the superiority of HIP network, and the significant improvements
on Hits@1 prove that our method can more accurately predict what is going to
happen.
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