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Efficient energy-based embedding models for link prediction in knowledge graphs

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We focus on the problem of link prediction in Knowledge Graphs, with the goal of discovering new facts. To this purpose, Energy-Based Models for Knowledge Graphs that embed entities and relations in continuous vector spaces have been largely used. The main limitation in their applicability lies in the parameter learning phase, which may require a large amount of time for converging to optimal solutions. In this article, we first propose an unified view on different Energy-Based Embedding Models. Hence, for improving the model training phase, we propose the adoption of adaptive learning rates. We show that, by adopting adaptive learning rates during training, we can improve the efficiency of the parameter learning process by an order of magnitude, while leading to more accurate link prediction models in a significantly lower number of iterations. We extensively evaluate the proposed learning procedure on a variety of new models: our result show a significant improvement over state-of-the-art link prediction methods on two large Knowledge Graphs, namely WordNet and Freebase.

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  1. 1

  2. 2 This description is taken from the Freebase KG (Bollacker et al. 2008)

  3. 3 For readability reasons, we describe entities and relations using an intuitive way of writing down triples as text rather than using the pure RDF syntax.

  4. 4 State of the LOD Cloud 2014:

  5. 5 Available at

  6. 6 If X is a continuous random variable, then \(Z(\beta ) = {\int }_{\tilde {x} \in \mathcal {X}} e^{- \beta E(\tilde {x})}\).

  7. 7


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Correspondence to Claudia d’Amato.

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Minervini, P., d’Amato, C. & Fanizzi, N. Efficient energy-based embedding models for link prediction in knowledge graphs. J Intell Inf Syst 47, 91–109 (2016).

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