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DSKG: A Deep Sequential Model for Knowledge Graph Completion

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Authors: Lingbing Guo,Qingheng Zhang,Weiyi Ge,Wei Hu,Yuzhong Qu
ArXiv: 1810.12582
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Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1810.12582v2

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 neural 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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