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IADIS International Conference WWW/Internet 2010display, such as whether to display or not resources returned by recommendation algorithms which mayalready be tagged by a target user (see approach 1 in the design section).It should be noted that the recommendation of friends and that of resources to a target user is closelyrelated. This is because friends may be recommended on the basis of how much they have shown interest ona resource which an approach has deemed as similar. Conversely, similar resources may be recommended onthe basis of how much they have been tagged by users which have been deemed as similar. This is alsoalluded to in the design section.7. CONCLUSIONThe major strength of this project is that it would aid a user in searching for resources. Through semanticdescription of data, the system is able to recommend friends and resources as well as allowing users to rateboth resources and other friends. In addition, the system is also able to find the experts in the social networktogether with their main area of expertise. The project has been extensively tested and in general allrecommendation algorithms that were implemented function as expected.8. FUTURE WORK Recommending friends via same tags Changing recommended friends’ positions Giving weight to concepts depending on the levels in the ontology Computing the experts’ scores via technical terms Automatic Generation of ontologyAn ontology may be built both automatically or manually. Automatic building of it would entail languageprocessing techniques and resource (or document) automatic classification, an area which the project doesnot delve into. Although the project does not directly propose such automatic ontology generation techniques,it discusses concerns which such a process must address. For example, the fact that resources may pertain tothe same part (or branch) of an ontology must be treated with care. Such a process should try to ascertain thatthe classification of a resource to a concept is as relatively detailed as can be. The process should therefore beexhaustive, not stopping short of classifying resources into sub-categories rather than categories wherepossible. Moreover, usage of a stemming algorithm is also needed in this regard, in that documents maycontain different versions of the same word. When classifying documents, it is crucial to ascertain thatdifferent versions of the same word are treated as the same word, with the prefixes and suffixes beingremoved.REFERENCESBennett, Brandon and Fellbaum, Christine. 2006. Formal Ontology In Information Systems. s.l. : Netherlands : IOS Press,2006. 1-58603-685-8.Ji, Ae-Ttie, et al. 2007. Collaborative Tagging in Recommender Systems. 2007.Kardan, Ahmad A., Speily, Omid R. b. and Modaberi, Somayyeh. Recommender Systems for Smart Lifelong Learning.Iran : s.n.Monachesi, P., et al. 2008. What ontologies can do for eLearning? . In: Proceedings of The Third InternationalConferences on interactive Mobile and Computer Aided Learning. 2008.Prud'homeaux, Eric and Seaborne, Andy. 2008. SPARQL Query Language for RDF. W3C Recommendation. [Online] 0115, 2008. http://www.w3.org/TR/rdf-sparql-query/.Rosenberg, Marc J. 2006. Beyond E-Learning. San Francisco : Pfeiffer, 2006.Terveen, Loren and Hill, Will. 2001.Beyond Recommender Systems: Helping People Help Each Other. HCI In the Millenium, J. Carroll, Ed. Addison-Wesley, Reading, MA. . 2001.Weaver, A. C. and Morrison, B. B. 2008. Social Networking. 2008, Vol. 41, 2.381