Modelling Multi-relations for Convolutional-based Knowledge Graph Embedding

• URL: http://arxiv.org/abs/2210.11711v1
• Date: Fri, 21 Oct 2022 03:43:06 GMT
• Title: Modelling Multi-relations for Convolutional-based Knowledge Graph Embedding
• Authors: Sirui Li, Kok Wai Wong, Dengya Zhu, Chun Che Fung
• Abstract summary: It is considered that such approaches disconnect the semantic connection of multi-relations between an entity pair. We propose a convolutional and multi-relational learning model, ConvMR. We show that ConvMR is efficient to deal with less frequent entities.
• Score: 0.2752817022620644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Representation learning of knowledge graphs aims to embed entities and relations into low-dimensional vectors. Most existing works only consider the direct relations or paths between an entity pair. It is considered that such approaches disconnect the semantic connection of multi-relations between an entity pair, and we propose a convolutional and multi-relational representation learning model, ConvMR. The proposed ConvMR model addresses the multi-relation issue in two aspects: (1) Encoding the multi-relations between an entity pair into a unified vector that maintains the semantic connection. (2) Since not all relations are necessary while joining multi-relations, we propose an attention-based relation encoder to automatically assign weights to different relations based on semantic hierarchy. Experimental results on two popular datasets, FB15k-237 and WN18RR, achieved consistent improvements on the mean rank. We also found that ConvMR is efficient to deal with less frequent entities.

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