Abstract
Many online platforms employ networks of human workers to perform computational tasks that can be difficult for a machine (e.g. reporting travel disruption). Such systems have to make a range of decisions, for example, selection of suitable workers for a task. In this paper we present an approach that utilises Semantic Web technologies and provenance to support such decision-making processes.
The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.
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Balog, K., Fang, Y., De Rijke, M., Serdyukov, P., Si, L.: Expertise retrieval. Foundations and Trends in Information Retrieval 6, 127–256 (2012)
Difallah, D.E., Demartini, G., Cudré-Mauroux, P.: Pick-a-crowd: tell me what you like, and i’ll tell you what to do. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 367–374 (2013)
Hendler, J., Berners-Lee, T.: From the semantic web to social machines: A research challenge for AI on the world wide web. Artificial Intelligence 174(2), 156–161 (2009)
Malone, T.W., Laubacher, R., Dellarocas, C.N.: Harnessing crowds: Mapping the genome of collective intelligence. MIT Sloan Research Paper No. 4732-09. SSRN, http://ssrn.com/abstract=1381502
Minder, P., Bernstein, A.: Crowdlang: a programming language for the systematic exploration of human computation systems. In: Aberer, K., Flache, A., Jager, W., Liu, L., Tang, J., Guéret, C. (eds.) SocInfo 2012. LNCS, vol. 7710, pp. 124–137. Springer, Heidelberg (2012)
Missier, P., Dey, S., Belhajjame, K., Cuevas-Vicenttin, V., Ludaescher, B.: D-prov: extending the prov provenance model with workflow structure. Technical report, School of Computing Science, Newcastle University (2013)
Moreau, L., Missier, P.: Prov-dm: The prov data model. W3C Recommendation (April 2012), http://www.w3.org/TR/prov-dm/
Mukherjee, S., Davulcu, H., Kifer, M., Senkul, P., Yang, G.: Logic-based approaches to workflow modeling and verification. In: Logics for Emerging Applications of Databases, pp. 167–202. Springer (2004)
Pignotti, E., Edwards, P., Gotts, N., Polhill, P.: Enhancing workflow with a semantic description of scientific intent. Journal of Web Semantics: Science, Services and Agents on the World Wide Web 9, 222–244 (2010)
Rajbhandari, S., Contes, A., Rana, O.F., Deora, V., Wootten, I.: Trust assessment using provenance in service oriented applications. In: 10th IEEE International Conference on Enterprise Distributed Object Computing Workshops, EDOCW 2006, p. 65. IEEE (2006)
Robertson, D., Giunchiglia, F.: Programming the social computer. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987) (2013)
Schall, D., Satzger, B., Psaier, H.: Crowdsourcing tasks to social networks in BPELl4people. World Wide Web, 1–32 (2012)
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Markovic, M., Edwards, P., Corsar, D. (2013). Utilising Provenance to Enhance Social Computation. In: Alani, H., et al. The Semantic Web – ISWC 2013. ISWC 2013. Lecture Notes in Computer Science, vol 8219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41338-4_29
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DOI: https://doi.org/10.1007/978-3-642-41338-4_29
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