MODELING A CONTEXT-AWARE FRIEND-OF-A-FRIEND(FOAF) APPLICATIONS FOR MOBILE PLATFORMS

Yıl 2017, Cilt: 5 Sayı: 1, 434 - 442, 30.06.2017

Bujar Raufi Florije Ismaili Jaumin Ajdari Xhemal Zenuni

https://doi.org/10.17261/Pressacademia.2017.621

Öz

We live in
an era where the evolution of social networks and developments in the field of
information technology witnesses and exponential growth. On a daily basis, we
have an ever growing, an ever expanding social network users and as a result we
affirm an increase of data that is distributed across different platforms. In
this paper, we aim to treat the semantic web from the prism of creating a
suitable infrastructure for data integration on the web. One of the most
promising applications of the semantic web is the presentation of profiles
using an RDF (Resource Description Framework) schema called Friend-of-Friend
(FOAF) which represents a machine-processable ontology for describing persons,
their activities and relations to other people and objects. In order to
demonstrate the concept on how to integrate, relate and share information using
FOAF, we model and develop a mobile application called "Find
professional" which consists in creating, finding and interacting in a
context aware fashion with other users via FOAF. The “Find Professional” mobile
app can create a FOAF profile either by asking user’s direct input or it can
utilize other social network profiles such as Facebook for importing user
public profiles into FOAF. User profiles represent RDF files that can be read and
queried by semantic query languages like SPARQL, which returns data to their
user profiles as well as finds professionals in the FOAF app based on user’s
geographic location. The application is also evaluated with a field usability
testing performed against ten randomly picked user from various profiles and
backgrounds. Usability testing process did not yield severe problems during
application evaluation and comments are more related to user interface rather
than difficulty in user task completion. 

Anahtar Kelimeler

Semantic web, foaf dictionary, semantic social network, context-awareness

Kaynakça

• Allemang, D., & Hendler, J. (2011). Semantic Web for the Working Ontologist: Effective Modelling in RDFS and OWL. Elsevier.
• Becker, C., & Bizer, C. (2008). DBpedia Mobile: A Location-Enabled Linked Data Browser. Ldow, 369, 2008.
• Becker, C., & Bizer, C. (2009). Exploring the geospatial semantic web with dbpedia mobile. Web Semantics: Science, Services and Agents on the World Wide Web, 7(4), 278-286.
• Berners-Lee, T., & Jaffe, J. (2016). W3C DATA ACTIVITY Building the Web of Data. Retrieved February 15, 2017, from https://www.w3.org/2013/data/
• Bizer, C., Heath, T., & Berners-Lee, T. (2009). Linked data-the story so far. Semantic services, interoperability and web applications: emerging concepts, 205-227.
• Brickley, D., & Miller, L. (2016). The Friend Of A Friend (FOAF) vocabulary specification, November 2007. URL http://xmlns. com/foaf/spec
• Chen, G., & Kotz, D. (2000). A survey of context-aware mobile computing research (Vol. 1, No. 2.1, pp. 2-1). Technical Report TR2000-381, Dept. of Computer Science, Dartmouth College.
• Dan, O., & Davison, B. D. (2016). Measuring and Predicting Search Engine Users’ Satisfaction. ACM Computing Surveys (CSUR), 49(1), 18.
• Ding, L., Finin, T., & Joshi, A. (2004). Analyzing social networks on the semantic web. IEEE Intelligent Systems (Trends & Controversies), 8(6), 815-820.
• Edoh-Alove, E., Bimonte, S., Pinet, F., & Bédard, Y. (2016). New design approach to handle spatial vagueness in spatial OLAP datacubes: application to agri-environmental data. In Geospatial Research: Concepts, Methodologies, Tools, and Applications (pp. 1859-1880). IGI Global.
• Gantz, J., & Reinsel, D. (2016). THE DIGITAL UNIVERSE IN 2020–Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East, IDC IView.
• Grawe, P. H. (2016). Nonparametric Statistics for the Behavioral Sciences. Numeracy: Advancing Education in Quantitative Literacy, 9 (1)
• Heath, T., & Bizer, C. (2011). Linked data: Evolving the web into a global data space. Synthesis lectures on the semantic web: theory and technology, 1(1), 1-136.
• Henzinger, M. R., Motwani, R., & Silverstein, C. (2002, September). Challenges in web search engines. In ACM SIGIR Forum (Vol. 36, No. 2, pp. 11-22). ACM.
• Höffner, K., Lehmann, J., & Usbeck, R. (2016, October). CubeQA—Question Answering on RDF Data Cubes. In International Semantic Web Conference (pp. 325-340). Springer International Publishing.
• Jiang, C. (2007). A location-based service provides Bluetooth cell phone user rich content related to objects/people nearby (Doctoral dissertation, New York University).
• Jung, J. J. (2010). Integrating Social Networks for Context Fusion in Mobile Service Platforms. J. UCS, 16(15), 2099-2110.
• Kaikkonen, A., Kekäläinen, A., Cankar, M., Kallio, T., & Kankainen, A. (2005). Usability testing of mobile applications: A comparison between laboratory and field-testing. Journal of Usability studies, 1(1), 4-16.
• Lei, Y., Uren, V., & Motta, E. (2006, October). Semsearch: A search engine for the semantic web. In International Conference on Knowledge Engineering and Knowledge Management (pp. 238-245). Springer Berlin Heidelberg.
• Meng, R., Chen, L., Tong, Y., & Zhang, C. (2017). Knowledge Base Semantic Integration using Crowdsourcing. IEEE Transactions on Knowledge and Data Engineering.
• Protocol, S. P. A. R. Q. L. (2016). RDF Query Language. RDF Data Access Working Group (DAWG) Std.
• Rana, J., Kristiansson, J., Hallberg, J., & Synnes, K. (2009, July). An architecture for mobile social networking applications. In Computational Intelligence, Communication Systems and Networks, 2009. CICSYN'09. First International Conference on (pp. 241-246). IEEE.
• Robles, R. J., & Kim, T. H. (2010). Review: context aware tools for smart home development. International Journal of Smart Home, 4(1).
• Schaffert, S., Eder, J., Grünwald, S., Kurz, T., & Radulescu, M. (2009, May). KiWi–a platform for semantic social software. In European Semantic Web Conference (pp. 888-892). Springer Berlin Heidelberg.
• Serena, F. D. (2017). U.S. Patent No. 9,536,268. Washington, DC: U.S. Patent and Trademark Office.
• Sheth, A. (2009). Citizen sensing, social Signals, and enriching human experience. IEEE Internet Computing, 13(4).
• Sun, X., & May, A. (2013). A comparison of field-based and lab-based experiments to evaluate user experience of personalised mobile devices. Advances in Human-Computer Interaction, 2013, 2.
• Swaminathan, S. N., & Elmasri, R. (2016, June). Quantitative Analysis of Scalable NoSQL Databases. In Big Data (BigData Congress), 2016 IEEE International Congress on (pp. 323-326). IEEE.
• Thyagaraju, G. S., & Kulkarni, U. P. (2012). Design and implementation of user context aware recommendation engine for mobile using Bayesian network, fuzzy logic and rule base. International Journal of Pervasive Computing and Communications, 8(2), 133-157.
• Torres, B. P., & González, A. G. (2017). Evolution of the Semantic Web Towards the Intelligent Web: From Conceptualization to Personalization of Contents. In Media and Metamedia Management (pp. 419-427). Springer International Publishing.