Abstract
Knowledge graph embedding models (KGEs) are actively utilized in many of the AI-based tasks, especially link prediction. Despite achieving high performances, one of the crucial aspects of KGEs is their capability of inferring relational patterns, such as symmetry, antisymmetry, inversion, and composition. Among the many reasons, the inference capability of embedding models is highly affected by the used loss function. However, most of the existing models failed to consider this aspect in their inference capabilities. In this paper, we show that disregarding loss functions results in inaccurate or even wrong interpretation from the capability of the models. We provide deep theoretical investigations of the already exiting KGE models on the example of the TransE model. To the best of our knowledge, so far, this has not been comprehensively investigated. We show that by a proper selection of the loss function for training a KGE e.g., TransE, the main inference limitations are mitigated. The provided theories together with the experimental results confirm the importance of loss functions for training KGE models and improving their performance.
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Acknowledgements
We acknowledge the support of the EU projects TAILOR (GA 952215), Cleopatra (GA 812997), the BmBF project MLwin, the EU Horizon 2020 grant 809965, and ScaDS.AI (01/S18026A-F).
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Nayyeri, M. et al. (2021). Loss-Aware Pattern Inference: A Correction on the Wrongly Claimed Limitations of Embedding Models. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12714. Springer, Cham. https://doi.org/10.1007/978-3-030-75768-7_7
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DOI: https://doi.org/10.1007/978-3-030-75768-7_7
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