Abstract
Knowledge graph (KG) embedding, which transforms both the entities and relations into continuous low-dimensional continuous vector space, has attracted considerable research. A large amount of models have been proposed for knowledge graph embedding. However, most previous approaches only regard the knowledge graph as a set of triples, ignoring the categories of the entities. In this paper, we take advantages of category information by modelling the category-specific embedding. Specially, we see the interaction between the category embedding and KG embedding as a closed loop, in which the category embedding and KG embedding are promoted mutually. Triples along with their categories are represented in a unified framework, in which way the embedding of triples are category-aware. We evaluate our model on multiple real-world KGs, and it show impressive improvements on link prediction and triple classification compared with other baselines.
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Acknowledgments
The authors would like to acknowledge the support provided by the Research Planning Project of National Language Committee (No. YB135-40) and the Humanity and Social Science Youth Foundation of Ministry of Education of China (No. 15YJC870029).
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Zhang, M., Wang, Q., Xu, Z., Zhu, J., Sun, S., Wen, Y. (2018). Category-Embodied Knowledge Embedding. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11303. Springer, Cham. https://doi.org/10.1007/978-3-030-04182-3_3
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DOI: https://doi.org/10.1007/978-3-030-04182-3_3
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