WeExt: A Framework of Extending Deterministic Knowledge Graph Embedding Models for Embedding Weighted Knowledge Graphs

With the further development of knowledge graphs, many weighted graphs (WKGs) have been published and greatly promote various applications. However, current deterministic graph embedding algorithms cannot encode well. This paper gives a promising framework WeExt that can extend models to enable them learn embeddings. In addtion, we introduce link prediction evaluate models’ performance on completing WKGs. Finally, give concrete implementation based two translational distance semantic matching models. Our experimental results show proposed achieves in prediction, weight prediction.