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Fact splices and entity aggregation networks for sparse temporal knowledge graph completion
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AI
DOI：10.1016/j.knosys.2026.115387.png)
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Abstract
En 中文
Advancements in artificial intelligence has markedly highlighted the importance of temporal knowledge graphs. However, since factors such as limitations in data collection and the immaturity of knowledge extraction techniques, temporal knowledge graphs often remain incomplete. Moreover, the rapid growth of real-world information has significantly exacerbated the sparsity of these graphs, severely affecting their practical application effects. Precisely for these reasons, the enhancement of sparse temporal knowledge graphs has become a significant focus of investigation in current academic research. In the context of completing sparse temporal knowledge graphs, previous methods have enriched entity representations through neighbor information aggregation, alleviated the sparsity of the graphs, and improved the completion effect. However, these methods have limitations. On the one hand, they focus on the information of neighboring entities and neglect the role of the relationship vectors between the current entity and its neighbor entities. On the other hand, they fail to distinguish the aggregation weights according to the roles of adjacent entities, thus limiting the further improvement of the completion effect. Accordingly, this paper investigates an information aggregation method based on relevant facts and a role-oriented attention network to enrich entity representations. Given that the importance of relationship vectors is often overlooked, we propose a fact vector generation strategy through a chained relation extractor and a fact vector splicer to excavate the information of relationship vectors. Aiming at the problem that previous methods failed to distinguish the role weights of adjacent entities, we propose a role-oriented attention network. This network aggregates context information and assigns weights according to the roles of the aggregated information, thereby generating more accurate entity representations. According to the experimental results, our model outperforms state-of-the-art baseline models in the selected metrics.
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