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

Minervini, P; d’Amato, C; Fanizzi, N; (2016) Efficient energy-based embedding models for link prediction in knowledge graphs. Journal of Intelligent Information Systems , 47 (1) pp. 91-109. 10.1007/s10844-016-0414-7. Green open access

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Abstract

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.

Type: Article
Title: Efficient energy-based embedding models for link prediction in knowledge graphs
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s10844-016-0414-7
Publisher version: https://doi.org/10.1007/s10844-016-0414-7
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Energy-based embedding models, Link predictions, RDF knowledge graphs
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10072114
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