基于 GAT 的异构模型元素语义相似度匹配方法

Semantic similarity matching of model elements is a key technology for constructing semantic mapping rules and realizing Heterogeneous Model (HM) transformation. The accuracy and objectivity of semantic matching for HM model elements has always been a difficult problem. Current methods exhibit a strong dependence on data a-mount and expert experience, with high uncertainty and subjectivity in matching models, lacking objective methods for effective matching. To address this, the semantic similarity matching method for model elements based on Graph Attention Networks (GAT) was proposed. The heterogeneous graph networks were constructed for HM graph data format representation. Node and edge feature representations were embedded in each model's graph to obtain corre-sponding node and edge embedding vectors. The semantic similarity of HM model elements was computed by GAT. Finally, taking the Systems Modeling Language (SysML) State machine metamodel and timed-automata metamodel as example, the effectiveness and rationality of the proposed method was validated. Compared to the existing methods, the proposed method could significantly reduce the dependency on pre-trained data and expert experience. It also lowered the uncertainty and subjectivity of matching models, as well as the cost and difficulty of semantic matching.