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Bayesian hypernetwork collaborates with time-difference evolutional network for temporal knowledge prediction
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DOI：10.1016/j.neunet.2024.106146.png)
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A Temporal Knowledge Graph (TKG) is a sequence of Knowledge Graphs (KGs) attached with time information, in which each KG contains the facts that co-occur at the same timestamp. Temporal knowledge prediction (TKP) aims to predict future events given observed historical KGs in TKGs, which is essential for many applications to provide intelligent analysis services. However, most existing TKP methods focus on entity and relation prediction tasks but ignore the importance of time prediction tasks. Furthermore, there is uncertainty in time prediction, and it is difficult for prediction models to model it completely. In this work, we propose a collaboration framework with Bayesian Hypernetwork and Time -Difference Evolutional Network (BH-TDEN) to address these problems. First, we begin with the time prediction task, and we present a Bayesian hypernetwork to model the uncertainty of events time. For the input of Bayesian hypernetwork, we design a novel timedifference evolutional network to obtain the entities and relations embedding. Specifically, we propose an auto -regressive time gate parameterized by the time difference of adjacent KGs in entity and relation encoder to learn the time -sensitive TKG embedding, which not only learns the relationship between the given time information and TKG embedding but also provides more expressive TKG embedding for Bayesian hypernetwork to accurately predict the time of future events. Furthermore, we also present a novel relation updating mechanism that employs the neighbor relations of the subject corresponding to the current relation to learn more adaptive relation embedding. Extensive experiments demonstrate that the proposed method obtains considerable time prediction and link prediction performance on four TKG benchmark datasets.
Keyword:
Temporal Knowledge Graphs
Time-difference evolutional network
Bayesian hypernetwork
Prediction
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6.3
论文数:
8.2K
被引数:
3.0W
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