Temporal knowledge subgraph inference based on time-aware relation representation

doi:10.1007/s10489-023-04833-9

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Temporal knowledge subgraph inference based on time-aware relation representation

2023-07-18

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DOI：10.1007/s10489-023-04833-9

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Abstract

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Temporal knowledge graphs (TKGs) extend traditional knowledge graphs by incorporating temporal information to represent valid time. TKG reasoning involves inferring and predicting missing or future facts within various time intervals, and applications in critical domains include political event prediction and financial analysis. Current TKG reasoning methods rely on path-based or subgraph-based strategies to achieve enhanced interpretability. However, they mostly focus on entity interaction information within paths or subgraphs, and they fail to exploit the semantic and temporal information of relations during reasoning. To comprehensively utilize such information for refining the reasoning process, we propose a subgraph reasoning model based on time-aware relation representation (TiAR) for TKGs. TiAR learns the subgraph structures around query nodes and reasons on facts happening in future timestamps. It encodes the relation features by combining the temporal displacement values between events and extracts the semantic correlations between query and adjacency relations via an attention-based neighborhood feature aggregation module. TiAR extracts a query subgraph of interest by iteratively sampling and diffusing attention through the whole graph. In particular, TiAR includes a path constraint module to further capture the semantic correlations between the structural features of queries and subgraphs. Our evaluation conducted on four benchmark datasets demonstrates that TiAR outperforms the current state-of-the-art models, indicating its effectiveness in achieving improved TKG reasoning. In particular, for the two large ICEWS05-15/18 datasets, our model yields better performance than the latest model. Moreover, The development of TKG reasoning methods is essential for improving the reliability and interpretability of models in such domains, and our work contributes to this area of research.

Keywords:

Temporal knowledge forecasting

Time-aware relation representation

Path representation constraint