AMCEN: An Attention Masking-based Contrastive Event Network for Two-stage Temporal Knowledge Graph Reasoning
This addresses temporal knowledge graph completion for applications needing future event prediction, representing an incremental improvement over existing methods.
The paper tackles the problem of temporal knowledge graph reasoning accuracy being impacted by imbalance between new and recurring events, proposing AMCEN which achieves considerable improvements in Hits@1 metrics on four benchmark datasets.
Temporal knowledge graphs (TKGs) can effectively model the ever-evolving nature of real-world knowledge, and their completeness and enhancement can be achieved by reasoning new events from existing ones. However, reasoning accuracy is adversely impacted due to an imbalance between new and recurring events in the datasets. To achieve more accurate TKG reasoning, we propose an attention masking-based contrastive event network (AMCEN) with local-global temporal patterns for the two-stage prediction of future events. In the network, historical and non-historical attention mask vectors are designed to control the attention bias towards historical and non-historical entities, acting as the key to alleviating the imbalance. A local-global message-passing module is proposed to comprehensively consider and capture multi-hop structural dependencies and local-global temporal evolution for the in-depth exploration of latent impact factors of different event types. A contrastive event classifier is used to classify events more accurately by incorporating local-global temporal patterns into contrastive learning. Therefore, AMCEN refines the prediction scope with the results of the contrastive event classification, followed by utilizing attention masking-based decoders to finalize the specific outcomes. The results of our experiments on four benchmark datasets highlight the superiority of AMCEN. Especially, the considerable improvements in Hits@1 prove that AMCEN can make more precise predictions about future occurrences.