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References

  1. Adomavicius, G., & Tuzhilin, A. (2011). Context-aware recommender systems. In Recommender Systems Handbook (pp. 217-253). Springer US.10.1007/978-0-387-85820-3_7
  2. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17(6), 734-749.10.1109/TKDE.2005.99
  3. Al-Mubaid, H., & Nguyen, H.A. (2006). Using MEDLINE as standard corpus for measuring semantic similarity of concepts in the biomedical domain, In Proc. of the IEEE 6th Symposium on Bioinformatics and Bioengineering, (pp. 315-318).10.1109/BIBE.2006.253295
  4. [4]Aydoğan, R., & Yolum, P. (2007, May). Learning consumer p REFERENCES using semantic similarity. In Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems (p. 229). ACM.10.1145/1329125.1329401
  5. Bandara, A., Payne, T., De Roure, D., & Lewis, T. (2007). A semantic approach for service matching in pervasive environments. Technical Report Number: ECSTR-IAM07-006, University of Southampton.
  6. Benazzouz, Y. (2012). Découverte de contexte pour une adaptation automatique de services en intelligence ambiante. Doctoral dissertation, Ecole Nationale superieure de mines, Saint- Etienne, France.
  7. Bisson, G. (2000), La similarite: Une notion symbolique/numerique. IMAG-CNRS, Projet SHERPA, Unité de recherche INRIA Rhone-Alpes, (p. 3).
  8. Broens, T., Pokraev, S., Van Sinderen, M., Koolwaaij, J., & Costa, P. D. (2004). Context- aware, ontology-based service discovery. In Ambient Intelligence (pp. 72-83). Springer Berlin Heidelberg.10.1007/978-3-540-30473-9_7
  9. Bulskov H., Knappe R., & Andreasen T. (2002). On measuring similarity for conceptual querying. In the Proc. of the 5th Int’l Conf. on Flexible Query Answering Systems (pp. 100-111). Springer-Verlag.10.1007/3-540-36109-X_8
  10. Capra, L., Emmerich, W., & Mascolo, C. (2001). Reflective middleware solutions for context-aware applications. In Metalevel Architectures and Separation of Crosscutting Concerns (pp. 126-133). Springer Berlin Heidelberg.10.1007/3-540-45429-2_10
  11. Chang, J., & Song, J. (2012, May). Research on context-awareness service adaptation mechanism in IMS under ubiquitous network. In Vehicular Technology Conference (VTC Spring), 2012 IEEE 75th (pp. 1-5). IEEE.
  12. Chen, A. (2005). Context-aware collaborative filtering system: Predicting the user’s preference in the ubiquitous computing environment. In Location-and Context-Awareness (pp. 244-253). Springer Berlin Heidelberg.10.1007/11426646_23
  13. Dalmau, M., Roose, P., & Laplace, S. (2009). Context-aware adaptable applications: A global approach. International Journal of Computer Science Issues, Vol.1, (pp. 13-25).
  14. d’Amato, Claudia (2007). Similarity-based learning methods for the semantic web, PhD thesis, Universita Degli Studi di Bari Faculta di Scienze Dipartimento di Informatica, pp 97 .
  15. d’Amato, C., Fanizzi, N., & Esposito, F. (2009). A semantic similarity measure for expressive description logics. Universita Degli Studi di Bari Faculta di Scienze Dipartimento di Informatica arXiv preprint arXiv:0911.5043.
  16. Dey, A.K. (2001). Understanding and using context. College of Computing & GVU Center, Georgia Institute of Technology, Atlanta,Personal and Ubiquitous Computing, Vol.5, (pp. 4-7).10.1007/s007790170019
  17. Dietze, S., Gugliotta, A., & Domingue, J. (2008). Bridging the gap between mobile application contexts and semantic web resources: Context-aware mobile and ubiquitous computing for enhanced usability: adaptive technologies and applications. Information Science Publishing (IGI Global).
  18. Doulkeridis, C., Loutas, N., & Vazirgiannis, M. (2006). A system architecture for context- aware service discovery. Electr. Notes Theor. Comput. Sci., 146(1), 101-116.10.1016/j.entcs.2005.11.010
  19. Efstratiou, C. (2004). Coordinated adaptation for adaptive context-aware applications. Doctoral dissertation, Computing Department, Lancaster University, UK, (pp. 173).
  20. Ehrig, M., Haase, P., Hefke, M., & Stojanovic, N. (2005). Similarity for ontologies-a comprehensive framework. ECIS 2005 Proceedings, 127.
  21. El Sayed, A., Hacid, H., & Zighed, D. (2007). A new context-aware measure for semantic distance using a taxonomy and a text corpus. In Information Reuse and Integration. IEEE International Conference on (pp. 279-284). IEEE.10.1109/IRI.2007.4296634
  22. Ganter, B., & Stumme, G., 2002, Formal concept analysis: Methods and applications in computer science. TU Dresden, http://www.aifb.uni-karlsruhe.de/WBS/gst/FBA03.shtml.
  23. García-Crespo, A., Chamizo, J., Rivera, I., Mencke, M., Colomo-Palacios, R., & Gómez- Berbís, J.M. (2009). SPETA: Social pervasive e-tourism advisor. Telematics and Informatics, 26(3), 306-315.10.1016/j.tele.2008.11.008
  24. Ge, J., & Qiu, Y. (2008). Concept similarity matching based on semantic distance. In Semantics, Knowledge and Grid, 2008. SKG’08. Fourth International Conference on (pp. 380383). IEEE.10.1109/SKG.2008.24
  25. Germán, S. (2010). Adaptation d’architectures logicielles collaboratives dans les environnements ubiquitaires. Contribution à l’interopérabilité par la sémantique. Doctoral dissertation, Systèmes (EDSYS), France.
  26. Gicquel, P.Y (2012). Similarités sémantiques et contextuelles pour l’apprentissage informel en mobilité. RJC EIAH’2012, 45.
  27. Gomaa, W.H., & Fahmy, A.A. (2013). A survey of text similarity approaches. International Journal of Computer Applications, 68(13), 13-18.10.5120/11638-7118
  28. Gonzalez-Castillo, J., Trastour, D., & Bartolini, C. (2001). Description logics for matchmaking of services. HP Laboratories technical report, 265.
  29. Harispe, S., Ranwez, S., Janaqi, S., & Montmain, J.(2013). Semantic measures for the comparison of units of language, concepts or instances from text and knowledge representation analysis, A Comprehensive Survey and a Technical Introduction to Knowledge-based Measures Using Semantic Graph Analysis, LGI2P/EMA Research Center, Parc scientifique, France.
  30. Hartmann, M., Zesch, T., Muhlhauser, M., & Gurevych, I. (2008). Using similarity measures for context-aware user interfaces. In Semantic Computing, 2008 IEEE International Conference on (pp. 190-197). IEEE.10.1109/ICSC.2008.94
  31. [31]Henricksen, K., Indulska, J., & Rakotonirainy, A. (2006). Using context and p REFERENCES to implement self-adapting pervasive computing applications. Software: Practice and Experience, 36(11-12), 1307-1330.
  32. Hirst, G., & St Onge, D. (1998). Lexical chains as representations of context for the detection and correction of malapropisms. In C. Fellbaum (ed.), WordNet: An Electronic Lexical Database, Cambridge, MA: The MIT Press.
  33. Janowicz, K. (2008). Kinds of contexts and their impact on semantic similarity measurement. In Pervasive Computing and Communications. Sixth Annual IEEE International Conference on (pp. 441-446). IEEE.10.1109/PERCOM.2008.35
  34. Jiang J.J., & Conrath D.W. (1997). Semantic similarity based on corpus statistics and lexical taxonomy. Proceedings of International Conference on Research in Computational Linguistics, August 22-24; Taipei, Taiwan.
  35. Kakousis, K., Paspallis, N., & Papadopoulos, G.A. (2010). A survey of software adaptation in mobile and ubiquitous computing. Enterprise Information Systems, 4(4), 355-389.10.1080/17517575.2010.509814
  36. Kang, S., Kim, D., Lee, Y., Hyun, S.J., Lee, D., & Lee, B. (2007). A semantic service discovery network for large-scale ubiquitous computing environments. ETRI journal, 29(5), 545558.10.4218/etrij.07.0106.0281
  37. Keßler, C. (2007). Similarity measurement in context. In Modeling and Using Context (pp. 277-290). Springer Berlin Heidelberg.10.1007/978-3-540-74255-5_21
  38. Keßler, C., Raubal, M., & Janowicz, K. (2007). The effect of context on semantic similarity measurement. In On the Move to Meaningful Internet Systems: OTM 2007 Workshops (pp. 1274-1284). Springer Berlin Heidelberg.
  39. Kirsch-Pinheiro, M., Vanrompay, Y., & Berbers, Y. (2008). Context-aware service selection using graph matching. In 2nd Non Functional Properties and Service Level Agreements in Service Oriented Computing Workshop (NFPSLA-SOC’08), ECOWS. CEUR Workshop proceedings (Vol. 411).
  40. Kirsch-Pinheiro, M., Villanova-Oliver, M., Gensel, J., & Martin, H. (2006). A personalized and context-aware adaptation process for web-based groupware systems. In 4th International Workshop on Ubiquitous Mobile Information and Collaboration Systems, CAiSE’06 Workshop (pp. 884-898).
  41. Klein, M., & Bernstein, A. (2004). Towards high-precision service retrieval. IEEE Internet Computing, January, 30-36.10.1109/MIC.2004.1260701
  42. Lavirotte, S., Lingrand, D., & Tigli, J.Y. (2005). Définition du contexte: fonctions de coût et méthodes de sélection. In Proceedings of the 2nd French-speaking Conference on Mobility and Ubiquity Computing (pp. 9-12). ACM.10.1145/1102613.1102616
  43. Leacock, C., & Chodorow, M. (1998). Combining local context and WordNet similarity for word sense identification. In WordNet: An Electronic Lexical Database, C. Fellbaum, MIT Press.
  44. Lee, J.S., & Lee, J.C. (2007). Context awareness by case-based reasoning in a music recommendation system. In Ubiquitous Computing Systems (pp. 45-58). Springer Berlin Heidelberg.10.1007/978-3-540-76772-5_4
  45. Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., & Ma, W.Y. (2008). Mining user similarity based on location history. In Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (p. 34). ACM.10.1145/1463434.1463477
  46. Li, Y., Bandar, Z.A., & McLean, D. (2003). An approach for measuring semantic similarity between words using multiple information sources. IEEE Transactions on Knowledge and Data Engineering, 15(4), 871-882.10.1109/TKDE.2003.1209005
  47. Lin, D (1998). An information-theoretic definition of similarity. In Proceedings of the 15th International Conference on Machine Learning, July 24-27 1998; Madison, Wisconsin, USA.
  48. Li, L., & Horrocks, I. (2004). A software framework for matchmaking based on semantic web technology. International Journal of Electronic Commerce, 8(4), 39-60.10.1080/10864415.2004.11044307
  49. Liu, L., Lecue, F., Mehandjiev, N., & Xu, L. (2010). Using context similarity for service recommendation. In Semantic Computing (ICSC), 2010 IEEE Fourth International Conference on (pp. 277-284). IEEE.10.1109/ICSC.2010.39
  50. Liu, Q., Ma, H., Chen, E., & Xiong, H. (2013). A survey of context-aware mobile recommendations. International Journal of Information Technology & Decision Making, 12(1), 139-172.10.1142/S0219622013500077
  51. Maedche, A., & Staab, S. (2002). Measuring similarity between ontologies. In Knowledge Engineering and Knowledge Management: Ontologies and the Semantic Web (pp. 251-263). Springer Berlin Heidelberg.
  52. McGovern, J. (2013). Context similarity evaluation: Inferring how users can collectively collaborate together in a pervasive environment. In Cloud and Green Computing (CGC), 2013 Third International Conference on (pp. 553-557). IEEE.10.1109/CGC.2013.93
  53. Meissen, U., Pfennigschmidt, S., Voisard, A., & Wahnfried, T. (200,). Context-and situation-awareness in information logistics. In Current Trends in Database Technology-EDBT 2004 Workshops (pp. 335-344). Springer Berlin Heidelberg.10.1007/978-3-540-30192-9_33
  54. Meng, L., Huang, R., & Gu, J. (2013). A review of semantic similarity measures in wordnet. International Journal of Hybrid Information Technology, 6(1), 1-12.
  55. Michel, M.D., & Deza, E. (2007). Dictionnaire des distances. In Encyclopedia of Distances.
  56. Mihalcea, R., Corley, C., & Strapparava, C. (2006). Corpus-based and knowledge-based measures of text semantic similarity. In AAAI (Vol. 6, pp. 775-780).
  57. Mokhtar, S.B., Preuveneers, D., Georgantas, N., Issarny, V., & Berbers, Y. (2008). EASY: Efficient semAntic Service discoverY in pervasive computing environments with QoS and context support. Journal of Systems and Software, 81(5), 785-808.10.1016/j.jss.2007.07.030
  58. Moon, H. J., Kim, S., Moon, J., & Lee, E. S. (2008). An Effective data processing method for fast clustering. In Computational Science and its Applications–ICCSA 2008 (pp. 335-347). Springer Berlin Heidelberg.10.1007/978-3-540-69848-7_27
  59. Nicklas, D., & Henricksen, K. (2008). Context modeling and reasoning: Key concepts for Pervasive computing. 5th IEEE Workshop on Context Modeling and Reasoning (CoMoRea’08) @PerCom Hong Kong.
  60. Paolucci, M., Kawamura, T., Payne, T.R., & Sycara, K. (2002). Semantic matching of web services capabilities. Lecture Notes in Computer Science, 2342, 333–347.10.1007/3-540-48005-6_26
  61. Petit, M., (2005). L’informatique contextuelle. Technical Report, South Britany University (UBS), France.
  62. Pirró, G., & Euzenat, J. (2010). A feature and information theoretic framework for semantic similarity and relatedness. In The Semantic Web–ISWC 2010 (pp. 615-630). Springer Berlin Heidelberg.10.1007/978-3-642-17746-0_39
  63. Preuveneers, D., Victor, K., Vanrompay, Y., Rigole, P., Pinheiro, M.K., & Berbers, Y. (2009). Context-aware adaptation in an ecology of applications. Context-Aware Mobile and Ubiquitous Computing for Enhanced Usability: Adaptive Technologies and Applications, 1-25.10.4018/978-1-60566-290-9.ch001
  64. Ramparany, F., Benazzouz, Y., Gadeyne, J., & Beaune, P. (2011). Automated context learning in ubiquitous computing environments. In SSN (pp. 9-21).
  65. Ranganathan, A., Shankar, C., & Campbell, R. (2005). Application polymorphism for autonomic ubiquitous computing. Multiagent and Grid Systems, 1(2), 109-129.10.3233/MGS-2005-1205
  66. Rada, R., Bicknell, H., Mili, E., & Blettner, M (1989). Development and application of a metric on semantic nets. IEEE Transaction on Systems, Man, and Cybernetics, 1(19), 17-30.10.1109/21.24528
  67. Resnik, P (1995). Using information content to evaluate semantic similarity. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, August 20-25; Montréal Québec, Canada.
  68. Rodriguez M.A., & Egenhofer, M.J. (2003). Determining semantic similarity among entity classes from different ontologies. IEEE Transactions on Knowledge and Data Engineering, 15, 442-456.10.1109/TKDE.2003.1185844
  69. Rubinstein, H. & Goodenough, J.B. (1965). Contextual correlates of synonymy. Communications of the ACM, 8(10).10.1145/365628.365657
  70. Ruta, M., Scioscia, F., Di Sciascio, E., & Piscitelli, G. (2012). Semantic matchmaking for location-aware ubiquitous resource discovery. International Journal on Advances in Intelligent Systems, 4(3/4), 113-127.
  71. Sánchez, D., Batet, M., Isern, D., & Valls, A. (2012). Ontology-based semantic similarity: A new feature-based approach. Expert Systems with Applications, 39(9), 7718-7728.10.1016/j.eswa.2012.01.082
  72. Saruladha, K. (2011). Semantic similarity measures for information retrieval systems using ontology. Doctoral dissertation, Department of Computer Science, School of Engineering and Technology, Pondicherry University, chapter 2.
  73. Saruladha, K., Aghila, G., & Raj, S. (2010). A survey of semantic similarity methods for ontology based information retrieval. In Machine Learning and Computing (ICMLC), Second International Conference on (pp. 297-301). IEEE.10.1109/ICMLC.2010.63
  74. Schilit, B., Adams, N., & Want, R. (1994). Context-aware computing applications. In IEEE Workshop on Mobile Computing Systems and Applications . Santa Cruz, CA, US.10.1109/WMCSA.1994.16
  75. Sharma, L., & Gera, A. (2013). A survey of recommendation system: Research challenges. International Journal of Engineering Trends and Technology (IJETT), 4(5), 19891992.
  76. Simonin, J., & Carbonell, N. (2007). Interfaces adaptatives, Adaptation dynamique à l’utilisateur courant. arXiv preprint arXiv:0708.3742.
  77. Sussna M. (1993). Word sense disambiguation for free-text indexing using a massive semantic network. In Proc. of Second Int’l Conf. Information Knowledge Management (CIKM ‘93).10.1145/170088.170106
  78. Thompson, M.S. (2006). Service discovery in pervasive computing environments. Doctoral dissertation, Virginia Polytechnic Institute and State University.
  79. Tversky, A (1977). Features of similarity. Psycological Review, 84(4).10.1037/0033-295X.84.4.327
  80. Van Setten, M., Pokraev, S., & Koolwaaij, J. (2004). Context-aware recommendations in the mobile tourist application COMPASS. In Adaptive Hypermedia and Adaptive Web-based Systems (pp. 235-244). Springer Berlin Heidelberg.10.1007/978-3-540-27780-4_27
  81. Viterbo, J., Mazuel, L., Charif, Y., Endler, M., Sabouret, N., Breitman, K., & Briot, J. (2008). Ambient intelligence: Management of distributed and heterogeneous context knowledge. In CRC Studies in Informatics Series (pp. 1-44). Chapman & Hall.
  82. Wu, Z., & Palmer, M. (1994). Verb semantics and lexical selection. In Proceedings of the 32nd Annual Meeting of the Associations for Computational Linguistics (pp. 133-138).10.3115/981732.981751
  83. Yau, S.S., & Huang D. (2006). Mobile middleware for situation-aware service discovery and coordination. In P. Bellavista and A. Corradi (eds.), Handbook of Mobile Middleware, (pp. 10591088).10.1201/9781420013153.ch39
  84. Zhang, F., Liu, W., & Bi, Y. Review on Wordnet-based ontology construction in China, International Journal on Smart Sensing and Intelligent Systems, vol. 6, No. 2, April 2013.10.21307/ijssis-2017-558
  85. Zhong, J., Zhu, H., Li, J., & Yu, Y. (2002). Conceptual graph matching for semantic search. In Proceedings of the 10th International Conference on Conceptual Structures (ICCS) (pp. 92196). Springer-Verlag, London.10.1007/3-540-45483-7_8
  86. Zouari, M. (2011). Architecture logicielle pour l’adaptation distribuée: Application à la réplication de données. Doctoral dissertation, Université Rennes 1, France.
Language: English
Page range: 125 - 158
Submitted on: Oct 6, 2014
Accepted on: Jan 8, 2015
Published on: Mar 1, 2015
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2015 Djamel Guessoum, Moeiz Miraoui, Chakib Tadj, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.