Semantically Enhanced Web Service for Global Supply Chain Disruption Management

The recent coronavirus pandemic has now unleashed a global supply chain crisis across a huge number of organizations, stemming from a lack of understanding and flexibility of the multiple layers of their global supply chains and a lack of diversification in their sourcing strategies. One of the technical options to mitigate the pandemic is to automate business processes by which heterogeneous data integration is encouraged. The convergence of Semantic Web with service-oriented computing is manifested by Semantic Web services technology. It addresses the major challenge of automated, interoperable, and meaningful coordination of web service composition in industrial applications – such as apparel business. Automatic service composition may dramatically improve the development efficiency of web service applications. This chapter proposes an approach to automatically process semantic service composition (SSC) using description logics (DLs) to provide well-defined semantics. Also, this chapter explains the role of ontologies in the architecture of the Semantic Web.

Semantic Service Search in IT Crowdsourcing Platform: A Knowledge Graph-Based Approach

Understanding user’s search intent in vertical websites like IT service crowdsourcing platform relies heavily on domain knowledge. Meanwhile, searching for services accurately on crowdsourcing platforms is still difficult, because these platforms do not contain enough information to support high-performance search. To solve these problems, we build and leverage a knowledge graph named ITServiceKG to enhance search performance of crowdsourcing IT services. The main ideas are to (1) build an IT service knowledge graph from Wikipedia, Baidupedia, CN-DBpedia, StuQ and data in IT service crowdsourcing platforms, (2) use properties and relations of entities in the knowledge graph to expand user query and service information, and (3) apply a listwise approach with relevance features and topic features to re-rank the search results. The results of our experiments indicate that our approach outperforms the traditional search approaches.