Abstract
Service discovery is one of the main goals of a service-oriented architecture, helping to discover complex applications. Knowledge graph is a cross-cutting research hotspot in the fields of computer, knowledge engineering and information science. This paper makes in-depth research on the key issues of knowledge graph matching for service-oriented discovery. Firstly, construct a knowledge graph for service discovery, and then design a template to match the knowledge graph. By designing a matching template, the service discovery mechanism is transformed into a knowledge graph knowledge query. Finally, the validity of the knowledge graph matching method proposed in this paper is verified by experiments.
Highlights
Driven by the rapid development of the Internet, Web services have shown an explosive growth trend in both quantity and type [1]
Tim Berners-Lee proposed the concept of semantic web in 2000, which laid the foundation for semantic web service discovery
(4) The section IV is the construction of knowledge graph matching template for polygon service discovery
Summary
Driven by the rapid development of the Internet, Web services have shown an explosive growth trend in both quantity and type [1]. Web services are self-describing, self-contained, modular applications that can be accessed through the Internet They provide an effective way to solve business process execution and application integration [2]. The main research content of this paper is the matching problem of knowledge graph for service-oriented discovery. This section mainly introduces the background, research purpose and significance of knowledge graph for service discovery. This section mainly introduces the research status of Web service discovery. (4) The section IV is the construction of knowledge graph matching template for polygon service discovery. This section mainly introduces matching template for service discovery based on knowledge graph. We verified the effectiveness of knowledge graph for service discovery by experiments, which contains user’s intent classification validity and accuracy of entity recognition. Guodong et al.: Research on Service Discovery Methods Based on Knowledge Graph
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