Web 2.0 is shifting work to online, virtual environments. At the same time social networking technologies are accelerating the discovery of experts, increasing the effectiveness of online knowledge acquisition and collaborative efforts. Nowadays it is possible to harness potentially unknown (large) groups of networked specialists for their abilities to amass large-scale collections of data and to solve complex business and technical problems, in the process known as crowdsourcing. Large global enterprises and entrepreneurs are increasingly adopting crowdsourcing because of its promise to give simple, low cost, access to a scalable workforce online. Enterprise crowdsourcing examples abound, taking many different shapes and forms, from mass data collection to enabling end-user driven customer support. This chapter identifies requirements for common protocols and reusable service components, extracting from existing crowdsourcing applications, in order to enable standardized interfaces supporting crowdsourcing capabilities.

The availability of encyclopedic Linked Open Data (LOD) paves the way to a new generation of knowledge-intensive applications able to exploit the information encoded in the semantically-enriched datasets freely available on the Web. In such applications, the notion of relatedness between entities plays an important role whenever, given a query, we are looking not only for exact answers but we are also interested in a ranked list of related ones. In this paper we present an approach to build a relatedness graph among resources in the DBpedia dataset that refer to the IT domain. Our final aim is to create a useful data structure at the basis of an expert system that, looking for an IT resource, returns a ranked list of related technologies, languages, tools the user might be interested in. The graph we created is a basic building block to allow an expert system to support the user in entity search tasks in the IT domain (e.g. software component search or expert finding) that goes beyond string matching typical of pure keyword-based approaches and is able to exploit the explicit and implicit semantics encoded within LOD datasets. The graph creation relies on different relatedness measures that are combined with each other to compute a ranked list of candidate resources associated to a given query. We validated our tool through experimental evaluation on real data to verify the effectiveness of the proposed approach. (C) 2015 Elsevier Ltd. All rights reserved.

People-driven service engagements involve communication over channels such as chat and email. Such engagements should be understood at the level of the commitments that the participants create and manipulate. Doing so provides a grounding for the communications and yields a business-level accounting of the progress of a service engagement. Existing work on commitment-based service engagements is limited to design-time model creation and verification. In contrast, we present a novel approach for capturing commitment-based engagements that are created dynamically in conversations. We monitor commitments identifying their creation, delegation, completion, or cancellation in the conversations. We have developed a prototype and evaluated it on real-world chat and email datasets. Our prototype captures commitments with a high F-measure of 90% in emails (Enron email corpus) and 80% in chats (HP IT support chat dataset) and provides promising results for capturing additional commitment operations.