Mining Social Web Service Repositories for Social Relationships to Aid Service Discovery

The Service Oriented Computing (SOC) paradigm promotes building new applications by discovering and then invoking services, i.e., software components accessible through the Internet. Discovering services means inspecting registries where textual descriptions of services functional capabilities are stored. To automate this, existing approaches index descriptions and associate users' queries to relevant services. However, the massive adoption of Web-exposed API development practices, specially in large service ecosystems such as the IoT, is leading to evergrowing registries which challenge the accuracy and speed of such approaches. The recent notion of Social Web Services (SWS), where registries not only store service information but also sociallike relationships between users and services opens the door to new discovery schemes. We investigate an approach to discover SWSs that operates on graphs with user-service relationships and employs lightweight topological metrics to assess service similarity. Then, "socially" similar services, which are determined exploiting explicit relationships and mining implicit relationships in the graph, are clustered via exemplar-based clustering to ultimately aid discovery. Experiments performed with the ProgrammableWeb.com registry, which is at present the largest SWS repository with over 15k services and 140k user-service relationships, show that pure topology-based clustering may represent a promising complement to content-based approaches, which in fact are more time-consuming due to text processing operations.