Lingbing Guo,Qingheng Zhang,Weiyi Ge,Wei Hu,Yuzhong Qu
Artifact development version:
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.