Abstract
Knowledge graph, a semantic network, to organize and store data is increasingly interested in the research community and businesses such as Google, Facebook, Amazon. For the machine learning models to work well in this data, we need to prepare good quality negative samples. Generating these negative examples is challenging in the knowledge graph because it is pretty hard to determine whether a link that does not appear in the graph is a negative or positive sample. In this paper, we apply the generative adversarial network to the ConvKB method to generate negative samples, thereby producing a better graph embedding. Experiments show that our approach has quality improvement compared to the original method on well-known datasets.
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Acknowledgements
This research is funded by the Faculty of Information Technology, University of Science, VNU-HCM, Vietnam, Grant number CNTT 2021-03 and Advanced Program in Computer Science.
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Le, T., Pham, T., Le, B. (2021). Negative Sampling for Knowledge Graph Completion Based on Generative Adversarial Network. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_1
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