Abstract
Finding suitable workers for specific functions largely relies on human assessment. In web-scale environments this assessment exceeds human capability. Thus we introduced the CRAWL approach for Adaptive Case Management (ACM) in previous work. For finding experts in distributed social networks, CRAWL leverages various Web technologies. It supports knowledge workers in handling collaborative, emergent and unpredictable types of work. To recommend eligible workers, CRAWL utilizes Linked Open Data, enriched WebID-based user profiles and information gathered from ACM case descriptions. By matching case requirements against profiles, it retrieves a ranked list of contributors. Yet it only takes statements people made about themselves into account. We propose the CRAWL·E: approach to exploit the knowledge of people about people available within social networks. We demonstrate the recommendation process for by prototypical implementation using a WebID-based distributed social network.
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Appelquist, D., et al.: A Standards-based, Open and Privacy-aware Social Web: W3C Incubator Group Report. Tech. rep., W3C (2010)
Balog, K., Azzopardi, L., de Rijke, M.: Formal models for expert finding in enterprise corpora. In: Proceedings of the 29th Annual International ACM SIGIR Conference, pp. 43–50. ACM, New York (2006)
Becerra-Fernandez, I.: Searching for experts on the Web: A review of contemporary expertise locator systems. ACM TOIT 6(4), 333–355 (2006)
Berk, R.A.: Linkedin Triology: Part 3. Top 20 Sources for Connections and How to Add Recommendations. The Journal of Faculty Development 28(2), 1–13 (2014)
Bider, I., Johannesson, P., Perjons, E.: Do workflow-based systems satisfy the demands of the agile enterprise of the future? In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 59–64. Springer, Heidelberg (2013)
Bozzon, A.,et al.: Choosing the Right Crowd: Expert Finding in Social Networks Categories and Subject Descriptors. In: Proceedings of the 16th International Conference on Extending Database Technology, New York, NY, USA, pp. 637–348 (2013)
Brabham, D.C.: Crowdsourcing as a Model for Problem Solving: An Introduction and Cases. Convergence: The International Journal of Research into New Media Technologies 14(1), 75–90 (2008)
Clair, C.L., Miers, D.: The Forrester WaveTM: Dynamic Case Management, Q1 2011. Tech. rep., Forrester Research (2011)
Davenport, T.H.: Rethinking knowledge work: A strategic approach. McKinsey Quarterly (2011)
Donston-Miller, D.: What LinkedIn Endorsements Mean To You (2012), http://www.informationweek.com/infrastructure/networking/what-linkedin-endorsements-mean-to-you/d/d-id/1106795
Doyle, A.: How To Use LinkedIn Endorsements (2012), http://jobsearch.about.com/od/linkedin/qt/linkedin-endorsements.htm
Dustdar, S., Gaedke, M.: The social routing principle. IEEE Internet Computing 15(4), 80–83 (2011)
Goel, S., Muhamad, R., Watts, D.: Social search in “small-world” experiments. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 701–710. ACM, New York (2009)
Gupta, S.: Geographic trends in skills using LinkedIn’s Endorsement feature (2013), http://engineering.linkedin.com/endorsements/geographic-trends-skills-using-linkedins-endorsement-feature
Heil, S., et al.: Collaborative Adaptive Case Management with Linked Data. To appear in WWW 2014 Companion: Proceedings of the 23rd International Conference on World Wide Web Companion, Seoul, Korea (2014)
Lv, H., Zhu, B.: Skill ontology-based semantic model and its matching algorithm. In: CAIDCD 2006, pp. 1–4. IEEE (2006)
Mundbrod, N., Kolb, J., Reichert, M.: Towards a system support of collaborative knowledge work. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 31–42. Springer, Heidelberg (2013)
Pérez-Rosés, H., Sebé, F., Ribó, J.M.: Endorsement Deduction and Ranking in Social Networks. In: 7th GraphMasters Workshop, Lleida, Spain (2013)
Perugini, S., Goncalves, M.A., Fox, E.A.: A connection-centric survey of recommender systems research. Journal of Intelligent Information Systems 23(2), 107–143 (2004)
Stufflebeam, D.: Evaluation Models. New Directions for Evaluation 2001(89), 7–98 (2001)
Swenson, K.D.: Position: BPMN Is Incompatible with ACM. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 55–58. Springer, Heidelberg (2013)
Uddin, M.N., Duong, T.H., Oh, K.-j., Jo, G.-S.: An ontology based model for experts search and ranking. In: Nguyen, N.T., Kim, C.-G., Janiak, A. (eds.) ACIIDS 2011, Part II. LNCS, vol. 6592, pp. 150–160. Springer, Heidelberg (2011)
Wild, S., Chudnovskyy, O., Heil, S., Gaedke, M.: Customized Views on Profiles in WebID-Based Distributed Social Networks. In: Daniel, F., Dolog, P., Li, Q. (eds.) ICWE 2013. LNCS, vol. 7977, pp. 498–501. Springer, Heidelberg (2013)
Wild, S., Chudnovskyy, O., Heil, S., Gaedke, M.: Protecting User Profile Data in WebID-Based Social Networks Through Fine-Grained Filtering. In: Sheng, Q.Z., Kjeldskov, J. (eds.) ICWE Workshops 2013. LNCS, vol. 8295, pp. 269–280. Springer, Heidelberg (2013)
Xu, Y., et al.: Combining social network and semantic concept analysis for personalized academic researcher recommendation. Decision Support Systems 54(1), 564–573 (2012)
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Heil, S., Wild, S., Gaedke, M. (2014). CRAWL·E: Distributed Skill Endorsements in Expert Finding. In: Casteleyn, S., Rossi, G., Winckler, M. (eds) Web Engineering. ICWE 2014. Lecture Notes in Computer Science, vol 8541. Springer, Cham. https://doi.org/10.1007/978-3-319-08245-5_4
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DOI: https://doi.org/10.1007/978-3-319-08245-5_4
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