Householder Transformation-Based Temporal Knowledge Graph Reasoning
نویسندگان
چکیده
Knowledge graphs’ reasoning is of great significance for the further development artificial intelligence and information retrieval, especially over temporal knowledge graphs. The rotation-based method has been shown to be effective at modeling entities relations on a graph. However, due lack representation capability, existing approaches can only model partial relational patterns they cannot handle combination reasoning. In this regard, we propose HTTR: Householder Transformation-based Temporal graph Reasoning, which focuses characteristics that evolve time. HTTR first fuses relation in graph, then uses transformation obtain an orthogonal matrix about fused information, finally defines as rotation head-entity tail-entity calculates similarity between rotated vector tail entity. addition, compare three methods fusing information. We allow other fusion replace current one long dimensionality satisfies requirements. show able outperform state-of-the-art tasks ability learn infer all four time: symmetric reasoning, antisymmetric inversion
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ژورنال
عنوان ژورنال: Electronics
سال: 2023
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics12092001