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Temporal householder transformation embedding for temporal knowledge graph completion
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AI
DOI：10.1016/j.knosys.2026.115406.png)
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Abstract
En 中文
Knowledge Graph Embedding (KGE) has been widely used to address the incompleteness of Knowledge Graph (KG) by predicting missing facts. Temporal Knowledge Graph Embedding (TKGE) extends KGE by incorporating temporal information into fact representations. However, most existing research focuses on static graphs and ignores the temporal dynamics of facts in TKG, which poses significant challenges for link prediction. Furthermore, current TKGE models still struggle with effectively capturing and representing crucial relation patterns, including symmetry, antisymmetry, inversion, composition, and temporal, along with complex relation mapping properties like 1-to-N, N-to-1, and N-to-N. To overcome these challenges, we propose a Temporal Householder Transformation Embedding model called TeHTE, which fuses temporal information with Householder transformation to capture both static and temporal features within TKG effectively. In the static module, TeHTE constructs static entity embeddings by reflecting the head entity through a transfer matrix and represents each relation with a pair of vectors to capture relational semantics. In the temporal module, TeHTE integrates temporal information into the entity representation through the time transfer matrix and shared time window, thereby enhancing its ability to capture temporal features. To further enhance modeling capacity, TeHTE learns a set of Householder transformations parameterized by relations to obtain structural embeddings for entities. Moreover, we theoretically demonstrate the ability of TeHTE to model various relation patterns and mapping properties. Experimental results on four benchmark datasets indicate that TeHTE substantially surpasses most existing TKGE approaches on temporal link prediction tasks. Ablation studies further validate the contribution of each component within the TeHTE framework.
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