Abstract
This paper introduces a new initialization method for knowledge graph (KG) embedding that can leverage ontological information in knowledge graph completion problems, such as link classification and link prediction. Although the initialization method is general and applicable to different KG embedding approaches in the literature, such as TransE or RESCAL, this paper experiments with deep learning and specifically with the neural tensor network (NTN) model. The experimental results show that the proposed method can improve link classification for a given relation by up to 15%. In a second contribution, the proposed method allows for addressing a problem not studied in the literature and introduced here as “KG completion with fresh entities”. This is the use of KG embeddings for KG completion when one or several of the entities in a triple (head, relation, tail) has not been observed in the training phase.
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Notes
- 1.
The implementation used in this work, as well as the employed datasets are available at https://github.com/Elviish/ntn-pytorch-ontological-info.
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Acknowledgements
This research work is supported by the Spanish Ministry of Science, Innovation and Universities under the program “Estancias de movilidad en el extranjero José Castillejo para jóvenes doctores” (CAS18/00229) and by the “Universidad Politécnica de Madrid” under the programs: “Ayudas al Personal Docente e Investigador para Estancias Breves en el Extranjero”, “Ayudas dirigidas a Jóvenes Investigadores para Fortalecer sus Planes de Investigación”, and “Ayudas para Contratos Predoctorales para la Realización del Doctorado”.
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Amador-Domínguez, E., Hohenecker, P., Lukasiewicz, T., Manrique, D., Serrano, E. (2020). An Ontology-Based Deep Learning Approach for Knowledge Graph Completion with Fresh Entities. In: Herrera, F., Matsui , K., Rodríguez-González, S. (eds) Distributed Computing and Artificial Intelligence, 16th International Conference. DCAI 2019. Advances in Intelligent Systems and Computing, vol 1003 . Springer, Cham. https://doi.org/10.1007/978-3-030-23887-2_15
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