Abstract
Knowledge graph (KG) completion aims to fill the missing facts in a KG, where a fact is represented as a triple in the form of (subject, relation, object). Current KG completion models compel two-thirds of a triple provided (e.g., subject and relation) to predict the remaining one. In this paper, we propose a new model, which uses a KG-specific multi-layer recurrent neutral network (RNN) to model triples in a KG as sequences. It outperformed several state-of-the-art KG completion models on the conventional entity prediction task for many evaluation metrics, based on two benchmark datasets and a more difficult dataset. Furthermore, our model is enabled by the sequential characteristic and thus capable of predicting the whole triples only given one entity. Our experiments demonstrated that our model achieved promising performance on this new triple prediction task.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos. 61872172 and 61772264).
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Guo, L., Zhang, Q., Ge, W., Hu, W., Qu, Y. (2019). DSKG: A Deep Sequential Model for Knowledge Graph Completion. In: Zhao, J., Harmelen, F., Tang, J., Han, X., Wang, Q., Li, X. (eds) Knowledge Graph and Semantic Computing. Knowledge Computing and Language Understanding. CCKS 2018. Communications in Computer and Information Science, vol 957. Springer, Singapore. https://doi.org/10.1007/978-981-13-3146-6_6
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