Parallel U-net: a novel Unet for link prediction in knowledge graph embedding

Parallel U-net: a novel Unet for link prediction in knowledge graph embedding

연구 분야: Databases

학회: International Journal of Data Science and Analytics

A knowledge graph is a tool for representing relationships between data. Since this graph is constructed based on existing knowledge, incomplete knowledge results in an incomplete graph. To resolve this issue, it is necessary to determine the existence and type of edges between nodes. This paper addresses this challenge by introducing a model for converting graph nodes to each other under an edge called a “relation”. Moreover, this design simultaneously considers both the global and local structures of the graph. This model is bidirectional, with two distinct inputs: a head and a tail. The features are extracted in each path and combined with those in the opposite path. This combination task is performed within a designated block known as a ’mirror’. The paths are separated to extract all features from each datum. Furthermore, a layer based on Laplace computations of the graph was considered to incorporate the graph direction as a feature. This layer is based on the geometric structure on the graph. The designed model was evaluated using four parameters that represent its quality and accuracy. The results show that the proposed model achieved an approximate 95% accuracy for all four parameters.