Abstract
Knowledge graph completion aims to predict missing relations between known entities. In this paper, we consider the method of knowledge graph embedding for hyper-relational data, which is common in knowledge graphs. Previous models such as Trans(E, H, R) and CTransR either are insufficient to embed hyper-relational data or focus on projecting an entity into multiple embeddings, which might not be plausible for generalization and might not ideally reflect the real knowledge. To overcome the issues, we propose a novel model named TransHR, which transforms the vectors of hyper-relations between a pair of entities into an individual vector acting as a translation between them. We experimentally evaluate our model on two typical tasks including link prediction and triple classification. The results demonstrate that TransHR significantly outperforms Trans(E, H, R) and CTransR especially for hyper-relational data.
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Zhou, M., Zhang, C., Han, X., Ji, Y., Hu, Z., Qiu, X. (2016). Knowledge Graph Completion for Hyper-relational Data. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_20
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DOI: https://doi.org/10.1007/978-3-319-42553-5_20
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