Complementation method and system for knowledge graph in numerical control programming field

Abstract

The invention discloses a few-sample knowledge graph completion method in the field of numerical control programming. The problems that a numerical control programming template generated under the guidance of a numerical control programming knowledge graph is low in reliability and large in limitation in the actual production process, programming personnel still need to repeatedly modify the numerical control programming template, and training entity samples are few are solved. The method comprises the following steps: firstly, designing an entity neighborhood information aggregation network based on a relation path, evaluating and distributing the importance of neighborhood entities through a multi-head self-attention mechanism, and aggregating features; then, according to the semantic similarity of the entity pairs, modeling a semantic map to represent the semantic relationship between the entities, and outputting global semantic features; and finally combining the neighborhood aggregation features and the semantic features to carry out link prediction. On a public data set complemented by a few-sample knowledge graph, the effectiveness and the accuracy of the algorithm are verified by performing a contrast experiment with a mainstream algorithm.