Abstract
Knowledge Graphs (KGs) contain rich semantic information and are of importance to many downstream tasks. In order to enhance practical utilization of KGs, KG completion task, which is also called link prediction, is a newly emerging hot research topic. During KG embedding model training, negative sampling is a fundamental method for obtaining negative samples. Inspired by an adversarial learning framework KBGAN, this paper proposes a new knowledge selective adversarial network, named as KSGAN, using a knowledge selector for high-quality negative sampling to benefit link prediction. The performances of our model KSGAN are evaluated on three standard knowledge completion datasets: FB15k-237, WN18 and WN18RR. The results show that KSGAN outperforms a list of baseline models on all the datasets, demonstrating the effectiveness of the proposed model.
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Acknowledgement
This work is supported by National Natural Science Foundation of China (No. 61772146) and Guangdong Natural Science Foundation (No. 2016A030313441, 2018A030310051).
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Hu, K., Liu, H., Hao, T. (2019). A Knowledge Selective Adversarial Network for Link Prediction in Knowledge Graph. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11838. Springer, Cham. https://doi.org/10.1007/978-3-030-32233-5_14
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