CONTEXT 2015: Modeling and Using Context pp 214-225 | Cite as
Identifying Context Information in Datasets
Abstract
Datasets are used in various applications assisting in performing reasoning and grouping actions on available data (e.g., clustering, classification, recommendations). Such sources of information may contain aspects relevant to context. In order to use to the fullest this context and draw useful conclusions, it is vital to have intelligent techniques that understand which portions of the dataset are relevant to context and what kind of context they represent. In this work we address the above issue by proposing a context extraction technique from existing datasets. We present a process that maps the given data of a dataset to a specific context concept. The prototype of our work is evaluated through an initial collection of datasets collected from various online sources.
Keywords
Context extraction Dataset Context matchmakingReferences
- 1.Abowd, G.D., Dey, A.K.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 304–307. Springer, Heidelberg (1999) CrossRefGoogle Scholar
- 2.Aciar, S.: Mining context information from consumers reviews. In: Proceedings of Workshop on Context-Aware Recommender System, vol. 201. ACM (2010)Google Scholar
- 3.Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. (TOIS) 23(1), 103–145 (2005)CrossRefGoogle Scholar
- 4.Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, New York (2011) CrossRefGoogle Scholar
- 5.Baltrunas, L., Kaminskas, M., Ricci, F., Rokach, L., Shapira, B., Luke, K.H.: Best usage context prediction for music tracks. In: Proceedings of the 2nd Workshop on Context Aware Recommender Systems (2010)Google Scholar
- 6.Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., Riboni, D.: A survey of context modelling and reasoning techniques. Pervasive Mob. Comput. 6(2), 161–180 (2010)CrossRefGoogle Scholar
- 7.Cantador, I., Brusilovsky, P., Kuflik, T.: 2nd workshop on information heterogeneity and fusion in recommender systems (hetrec 2011). In: Proceedings of the 5th ACM conference on Recommender systems. RecSys 2011. ACM, New York (2011)Google Scholar
- 8.Chen, G., Kotz, D., et al.: A survey of context-aware mobile computing research. Technical Report TR2000-381, Department of Computer Science, Dartmouth College (2000)Google Scholar
- 9.Cohen, W., Ravikumar, P., Fienberg, S.: A comparison of string metrics for matching names and records. In: KDD Workshop on Data Cleaning and Object Consolidation. vol. 3, pp. 73–78 (2003)Google Scholar
- 10.Domingues, M.A., Jorge, A.M., Soares, C.: Using contextual information as virtual items on top-n recommender systems. arXiv preprint arXiv:1111.2948 (2011)
- 11.Marianne, H., Mathieu, L., Clémentine, N., Jean-Rémy, F.: Metamodel matching for automatic model transformation generation. In: Ober, I., Uhl, A., Völter, M., Bruel, J.-M., Czarnecki, K. (eds.) MODELS 2008. LNCS, vol. 5301, pp. 326–340. Springer, Heidelberg (2008) CrossRefGoogle Scholar
- 12.Hansen, J., Sato, M., Ruedy, R., Lo, K., Lea, D.W., Medina-Elizade, M.: Global temperature change. Proc. Nat. Acad. Sci. 103(39), 14288–14293 (2006)CrossRefGoogle Scholar
- 13.Kaluža, B., Mirchevska, V., Dovgan, E., Luštrek, M., Gams, M.: An agent-based approach to care in independent living. In: de Ruyter, B., Wichert, R., Keyson, D.V., Markopoulos, P., Streitz, N., Divitini, M., Georgantas, N., Mana Gomez, A. (eds.) AmI 2010. LNCS, vol. 6439, pp. 177–186. Springer, Heidelberg (2010) CrossRefGoogle Scholar
- 14.Kapitsaki, G.M., Achilleos, A.P.: Model matching for web services on context dependencies. In: Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services, pp. 45–53. ACM (2012)Google Scholar
- 15.Lombardi, S., Anand, S.S., Gorgoglione, M.: Context and customer behaviour in recommendation (2009)Google Scholar
- 16.Lovett, T., O’Neill, E. (eds.): Mobile Context Awareness. Springer, London (2012)Google Scholar
- 17.Mettouris, C., Papadopoulos, G.A.: Cars context modelling (2014)Google Scholar
- 18.Mettouris, C., Papadopoulos, G.A.: Ubiquitous recommender systems. Computing 96(3), 223–257 (2014)CrossRefGoogle Scholar
- 19.Munguia Tapia, E.: Activity recognition in the home setting using simple and ubiquitous sensors. Ph.D. thesis, Massachusetts Institute of Technology (2003)Google Scholar
- 20.Schmidt, A., Beigl, M., Gellersen, H.W.: There is more to context than location. Comput. Graph. 23(6), 893–901 (1999)CrossRefGoogle Scholar
- 21.Sielis, G.A., Mettouris, C., Papadopoulos, G.A., Tzanavari, A., Dols, R.M., Siebers, Q.: A context aware recommender system for creativity support tools. J. UCS 17(12), 1743–1763 (2011)Google Scholar
- 22.Sielis, G.A., Mettouris, C., Tzanavari, A., Papadopoulos, G.A.: Context-aware recommendations using topic maps technology for the enhancement of the creativity process. In: Educational Recommender Systems and Technologies: Practices and Challenges: Practices and Challenges, p. 43 (2011)Google Scholar
- 23.Stark, M.M., Riesenfeld, R.F.: Wordnet: an electronic lexical database. In: Proceedings of 11th Eurographics Workshop on Rendering. MIT Press (1998)Google Scholar
- 24.Suen, C.Y.: n-gram statistics for natural language understanding and text processing. IEEE Trans. PAMI-Pattern Anal. Mach. Intell. 1(2), 164–172 (1979)CrossRefGoogle Scholar
- 25.Suthaharan, S., Alzahrani, M., Rajasegarar, S., Leckie, C., Palaniswami, M.: Labelled data collection for anomaly detection in wireless sensor networks. In: 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 269–274. IEEE (2010)Google Scholar
- 26.Heinze, T., Voigt, K.: Metamodel matching based on planar graph edit distance. In: Gogolla, M., Tratt, L. (eds.) ICMT 2010. LNCS, vol. 6142, pp. 245–259. Springer, Heidelberg (2010) CrossRefGoogle Scholar
- 27.Zheng, Y., Burke, R., Mobasher, B.: Differential context relaxation for context-aware travel recommendation. In: Lops, P., Huemer, C. (eds.) EC-Web 2012. LNBIP, vol. 123, pp. 88–99. Springer, Heidelberg (2012) CrossRefGoogle Scholar
- 28.Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th international conference on World Wide Web, pp. 22–32. ACM (2005)Google Scholar