A Framework for Service Semantic Description Based on Knowledge Graph

Abstract

To construct a large-scale service knowledge graph is necessary. We propose a method, namely semantic information extension, for service knowledge graphs. We insist on the information of services described by Web Services Description Language (WSDL) and we design the ontology layer of web service knowledge graph and construct the service graph, and using the WSDL document data set, the generated service knowledge graph contains 3738 service entities. In particular, our method can give a full performance to its effect in service discovery. To evaluate our approach, we conducted two sets of experiments to explore the relationship between services and classify services that develop by service descriptions. We constructed two experimental data sets, then designed and trained two different deep neural networks for the two tasks to extract the semantics of the natural language used in the service discovery task. In the prediction task of exploring the relationship between services, the prediction accuracy rate reached 95.1%, and in the service classification experiment, the accuracy rate of TOP5 reached 60.8%. Our experience shows that the service knowledge graph has additional advantages over traditional file storage when managing additional semantic information is effective and the new service representation method is helpful for service discovery and composition tasks.

New roles of research data infrastructure in research paradigm evolution

“…Thanks to intelligent computing, RDIs with a scaling effect have become a goldmine for scientific discoveries. In recent years, AI scientists and robot scientists emerged in various disciplines, which force the data and knowledge service intelligently (Sun & Han, 2021). ChatGPT, Large Language Models (LLM) molded AI research assistants, and might be capable of works from fine-tuning language, extracting references and metadata from papers, to helping evaluate the trustworthiness of findings by aggregating the heuristics of many experts (Cramer, 2021;Davies et al, 2021;OECD, 2023;Zhao et al, 2023).…”