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Convolutional Complex Knowledge Graph Embeddings

2020-08-07 12:49:01
Caglar Demir, Axel-Cyrille Ngonga Ngomo
arXiv_CL
           
Abstract

In this paper, we study the problem of learning continuous vector representations of knowledge graphs for predicting missing links. We present a new approach called ConEx, which infers missing links by leveraging the composition of a 2D convolution with a Hermitian inner product of complex-valued embedding vectors. We evaluate ConEx against state-of-the-art approaches on the WN18RR, FB15K-237, KINSHIP and UMLS benchmark datasets. Our experimental results show that ConEx achieves a performance superior to that of state-of-the-art approaches such as RotatE, QuatE and TuckER on the link prediction task on all datasets while requiring at least 8 times fewer parameters. We ensure the reproducibility of our results by providing an open-source implementation which includes the training, evaluation scripts along with pre-trained models at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2008.03130

PDF

https://arxiv.org/pdf/2008.03130.pdf

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