Convolutional Complex Knowledge Graph Embeddings

• URL: http://arxiv.org/abs/2008.03130v3
• Date: Wed, 9 Jun 2021 12:25:01 GMT
• Title: Convolutional Complex Knowledge Graph Embeddings
• Authors: Caglar Demir and Axel-Cyrille Ngonga Ngomo
• Abstract summary: We present a new approach called ConEx, which infers missing links by leveraging 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.
• Score: 1.1650381752104297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/conex-kge/ConEx.

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