Enhancing the Results of Recommender Systems using Implicit Ontology Relations

Lamiaa Abdelazziz, Khaled Nagi

2012

Abstract

Sharing unstructured knowledge between peers is a must in virtual organizations. The huge number of doc-uments available for sharing makes modern recommender systems indispensable. Recommender systems use several information retrieval techniques to enhance the quality of their results. Unfortunately, every peer has his/her own point of view to categorize his/her own data. The problem arises when a user tries to search for some information in his/her peers’ exposed data. The seeker categories must be matched with its responders categories. In this work, we propose a way to enhance the recommendation process based on using simple implicit ontology relations. This helps in recognizing better matched categories in the exposed data. We show that this approach improves the quality of the results with an acceptable increase in computation cost.

References

  1. ACM, 1998. The ACM computing classification system, http://www.acm.org/about/class/ccs98-html.
  2. Balabanovic, M., and Shoham, Y., 1997. Content-based, collaborative recommendation. In Communication of ACM 40(3), 66-72.
  3. Bridge, D., Göker, M., McGinty, L., Smyth, B., 2006. Case-based recommender systems. In The Knowledge Engineering review, 20(3), 315-320.
  4. Burke, R., 2007. Hybrid web recommender systems. In The Adaptive Web, pp. 377-408. Springer, Berlin/Heidelberg.
  5. Deng, S. and Peng, H., 2006. Document Classification Based on Support Vector Machine Using A Concept Vector Model. In the IEEE/WIC/ACM International Conference on Web Intelligence.
  6. Fuehrer, E. C., Ashkanasy, N. M., 1998. The Virtual organization: defining a Weberian ideal type from the inter-organizational perspective. Paper presented at the Annual Meeting of the Academy of Management, SanDiego, USA.
  7. Gomez Ludermir, P., Guizzardi-Silva Souza, R., and Sona, D., 2005. Finding the right answer: an information retrieval approach supporting knowledge sharing. In Proceedings of AAMAS 2005 Workshop. Agent Mediated Knowledge Management, The Netherlands.
  8. Gomez Ludermir, P., 2005. Supporting Knowledge Management using a Nomadic Service for Artifact Recommendation. Thesis for a Master of Science degree in Telematics, from the University of Twente Enschede, The Netherlands.
  9. Guizzardi-Silva Souza, R., Gomes Ludermir, P., and Sona, D., 2007. A Recommender Agent to Support Knowledge Sharing in Virtual Enterprises. In Protogeros, N. (Ed.). Agent and Web Service Technologies in Virtual Enterprises, Idea Group Publishing.
  10. Hatcher, E., Gospodnetic, O. 2004. Lucene in Action. Manning Publications.
  11. Manning, C., 2008. Introduction to Information Retrieval. Cambridge University Press, Cambridge.
  12. OWL, 2009. OWL2 Web Ontology Language. Document Overview. In W3C Recommendation, http://www.w3.org/TR/owl2-overview/.
  13. Ricci, F., Rokach, L., and Shapira, B., 2011. Recommender Systems Handbook, Springer Science+Business Media.
  14. Schafer, J. B., Frankowski, D., Herlocker, J., Sen, S., 2007. Collaborative filtering recommender systems. In The Adaptive Web, pp. 291-324. Springer, Berlin / Heidelberg.
  15. Tudorache, T., Noy, N. F., Tu, S. W., Musen, M.A., 2008. Supporting collaborative ontology development in Protégé. In Seventh International Semantic Web Conference, Karlsruhe, Germany, Springer.
Download


Paper Citation


in Harvard Style

Abdelazziz L. and Nagi K. (2012). Enhancing the Results of Recommender Systems using Implicit Ontology Relations . In Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2012) ISBN 978-989-8565-30-3, pages 5-14. DOI: 10.5220/0004105700050014


in Bibtex Style

@conference{keod12,
author={Lamiaa Abdelazziz and Khaled Nagi},
title={Enhancing the Results of Recommender Systems using Implicit Ontology Relations},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2012)},
year={2012},
pages={5-14},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004105700050014},
isbn={978-989-8565-30-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2012)
TI - Enhancing the Results of Recommender Systems using Implicit Ontology Relations
SN - 978-989-8565-30-3
AU - Abdelazziz L.
AU - Nagi K.
PY - 2012
SP - 5
EP - 14
DO - 10.5220/0004105700050014