Reasoning of temporal knowledge graphs based on tensor decomposition embedding
In response to the insufficient utilization of temporal information in existing extrapolation methods for reasoning of temporal knowledge graphs,inspired by the tensor decomposition model,the relational embedding was divided into static and dynamic(temporal)parts.The probability of the object entity was then calculated through the bilinear scoring function between head entity embedding,relational embedding,and all entity embedding.Finally,the experimental results on three datasets verified the effectiveness of this method.
万方数据
维普
