SMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs

• URL: http://arxiv.org/abs/2507.13001v1
• Date: Thu, 17 Jul 2025 11:18:08 GMT
• Title: SMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs
• Authors: Kossi Amouzouvi, Bowen Song, Andrea Coletta, Luigi Bellomarini, Jens Lehmann, Sahar Vahdati,
• Abstract summary: We propose a framework that evaluates how well each relation fits with different geometric transformations.<n>Based on this ranking, the model can: (1) assign the best-matching transformation to each relation, or (2) use majority voting to choose one transformation type to apply across all relations.
• Score: 7.612535740166837
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge graph representation learning approaches provide a mapping between symbolic knowledge in the form of triples in a knowledge graph (KG) and their feature vectors. Knowledge graph embedding (KGE) models often represent relations in a KG as geometric transformations. Most state-of-the-art (SOTA) KGE models are derived from elementary geometric transformations (EGTs), such as translation, scaling, rotation, and reflection, or their combinations. These geometric transformations enable the models to effectively preserve specific structural and relational patterns of the KG. However, the current use of EGTs by KGEs remains insufficient without considering relation-specific transformations. Although recent models attempted to address this problem by ensembling SOTA baseline models in different ways, only a single or composite version of geometric transformations are used by such baselines to represent all the relations. In this paper, we propose a framework that evaluates how well each relation fits with different geometric transformations. Based on this ranking, the model can: (1) assign the best-matching transformation to each relation, or (2) use majority voting to choose one transformation type to apply across all relations. That is, the model learns a single relation-specific EGT in low dimensional vector space through an attention mechanism. Furthermore, we use the correlation between relations and EGTs, which are learned in a low dimension, for relation embeddings in a high dimensional vector space. The effectiveness of our models is demonstrated through comprehensive evaluations on three benchmark KGs as well as a real-world financial KG, witnessing a performance comparable to leading models

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