Modelling Multi-relations for Convolutional-based Knowledge Graph Embedding
This addresses the issue of multi-relation modeling in knowledge graphs for AI applications, representing an incremental improvement over existing methods.
The paper tackled the problem of disconnected semantic connections in knowledge graph embedding by proposing ConvMR, a convolutional and multi-relational model that encodes multi-relations into unified vectors and uses attention to weight relations, achieving consistent improvements in mean rank on FB15k-237 and WN18RR datasets.
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.