Graph neural network-based similarity relationship construction model for geospatial services

During the development of service-based software systems, Geospatial Service (GS) replacement is often performed, which requires the discovery of functionally similar services in service registries to replace failed services. Compared to real-time similarity computations, direct extraction of similar services from constructed similarity relationships can yield higher replacement efficiency. However, missing and inconsistent service-registry information impedes accurate similarity relationship construction. Here, we propose a Graph Neural Network (GNN)-based model for GS Similarity Relationship construction considering service descriptions and tags, which is named GSSR-GNN. As the sparsity of the service similarity relationship graph constructed based on labeled samples limits the information propagation ability, a graph augmentation method for similarity relationship construction among second-order neighbors is proposed. Considering the differences in the semantic-information feature distributions, such as the service descriptions and tags, a feed-forward neural network-based fusion method is designed to embed them into the same vector space. Pre-trained Bidirectional Encoder Representations from Transformers (BERT) and WordNet models are introduced to enhance the service-representation expressiveness. When an enhanced service representation is input to the GNN, the similarity is calculated and the service similarity relationship is obtained. Experimental results show that the proposed model constructs service similarity relationships with high precision, thus improving the service replacement efficiency and reducing the computational cost of service registry during service replacement.