Ontology for Task and Quality Management in Crowdsourcing

Reham Alabduljabbar, Hmood Al-Dossari

Abstract


This paper suggests an ontology for task and quality control mechanisms representation in crowdsourcing systems. The ontology is built to provide reasoning about tasks and quality control mechanisms to improve tasks and quality management in crowdsourcing. The ontology is formalized in OWL (Web Ontology Language) and implemented using Protégé. The developed ontology consists of 19 classes, 7 object properties, and 32 data properties. The development methodology of the ontology involves three phases including Specification (identifying scope, purpose and competency questions), Conceptualization (data dictionary, UML, and instance creation), and finally Implementation and Evaluation.


Keywords


Crowdsourcing; Quality control; Task ontology; Ontology engineering; OWL.

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References


L. Ponciano, F. Brasileiro, N. Andrade, and L. Sampaio, “Considering human aspects on strategies for designing and managing distributed human computation,” Journal of Internet Services and Applications, vol. 5, no. 1, p. 10, 2014.

L. Litman, J. Robinson, and C. Rosenzweig, “The relationship between motivation, monetary compensation, and data quality among US- and India-based workers on Mechanical Turk.,” Behavior research methods, Springer US, Jun. 2014.

R. Buettner, “A Systematic Literature Review of Crowdsourcing Research from a Human Resource Management Perspective,” in Proceedings of the 48th Hawaii International Conference on System Sciences, 2015.

R. Alabdujabbar and H. Al-Dossari, “Towards a Classification Model for Tasks in Crowdsourcing,” in the ACM proceedings of the second International Conference on Internet of Things, Data and Cloud Computing (ICC 2017), Cambridge city, United Kingdom. (In Press).

M. Allahbakhsh, B. Benatallah, A. Ignjatovic, H. Motahari-Nezhad, E. Bertino, and S. Dustdar, “Quality Control in Crowdsourcing Systems: Issues and Directions,” IEEE Internet Computing, vol. 17, no. 2, pp. 76–81, 2013.

M. Gan, X. Dou, and R. Jiang, “From ontology to semantic similarity: Calculation of ontology-based semantic similarity,” The Scientific World Journal, 2013.

[7] R. Alabduljabbar and H. Al-Dossari, “A Dynamic Model for Quality Control in Crowdsourcing Systems,” in Proceedings of the IKE’16 - The 15th International Conference on Information & Knowledge Engineering,July 2016, Las Vegas, USA.

G. Brusa, M. Caliusco, and O. Chiotti, “A process for building a domain ontology: an experience in developing a government budgetary ontology,” Proceedings of the second Australasian workshop on Advances in ontologies, vol. 72, no. c, pp. 7–15, 2006.

A. Gómez-Pérez, M. Fernández-López, and O. Corcho, Ontological engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. London: Springer, 2004.

M. Gruninger and M. S. Fox, “Methodology for the Design and Evaluation of Ontologies,” Industrial Engineering, vol. 95, pp. 1–10, 1995.

M. Uschold and M. Gruninger, “Ontologies: principles, methods and applications,” The Knowledge Engineering Review, vol. 11, no. February, pp. 93–136, 1996.

L. Hetmank, “A Lightweight Ontology for Enterprise Crowdsourcing,” in Proceedings of the 22nd European Conference on Information Systems (ECIS 2014), 2014, p. Paper 886.

J. DE Bruijn and D. Fensel, “Ontology Definitions,” in Encyclopedia of Library and Information Science, Marcel Dekker, inc., 2005.

M. Hepp, “Possible ontologies: How reality constrains the development of relevant ontologies,” IEEE Internet Computing, vol. 11, no. 1, pp. 90–96, 2007.

MTurk, “MTurk: Amazon Mechanical Turk,” 2016. [Online]. Available: http://www.mturk.com/. [Accessed: 01-Jan-2016].

Upwork, “Upwork,” 2016. [Online]. Available: https://www.upwork.com/. [Accessed: 24-Mar-2016].

Freelancer, “Freelancer,” 2016. [Online]. Available: https://www.freelancer.com/. [Accessed: 24-Mar-2016].

“Protégé.” [Online]. Available: http://protege.stanford.edu. [Accessed: 01-May-2016].

K. El Maarry, W. Balke, H. Cho, S. Hwang, and Y. Baba, “Skill Ontology-Based Model for Quality Assurance in Crowdsourcing,” Database Systems for Advanced Applications, LNCS, vol. 8505, pp. 376–387, 2014.

http://www.ksl.stanford.edu/software/ontolingua/ , Access: 10 June 2016.

http://www.daml.org/ontologies/ , Access: 10 June 2016.

http://protege.cim3.net/cgi-bin/wiki.pl?ProtegeOntologiesLibrary , Access: 10 June 2016.

http://swoogle.umbc.edu/ , Access: 10 June 2016.

http://jena.sourceforge.net/ , Access: 10 June 2016.


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