Abstract
Knowledge graph embedding aims to learn low-dimensional embedding vector representations for entities and relations, which can be used in further machine learning tasks. However, previous knowledge graph embedding models perform poorly when dealing with unbalanced relations which occupy a large proportion in knowledge graphs. In addition, modeling connections between entities and relations accurately is still a big challenge. In this paper, we propose a novel knowledge graph embedding model called ConnectER. It can solve the above problems through a “Connection-Classification” architecture. Experiment results show consistent improvements compared with state-of-the-art baselines.
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Notes
- 1.
The proportion of correct entities ranked in the top 10.
- 2.
Mapping properties of relations follows the same protocol in TransE [5].
References
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Acknowledgements
This work is supported by the Research Foundation of Science and Technology Plan Project in Guangdong Province and Guangzhou City (2014B030301007, 2015A030401057, 2016B030307002, 2014SY000013, 2017B030308007) and CCF-Tencent Open Research Fund.
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Huang, Z., Li, B., Yin, J. (2018). Knowledge Graph Embedding by Learning to Connect Entity with Relation. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10987. Springer, Cham. https://doi.org/10.1007/978-3-319-96890-2_33
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