Abstract
Interpretation of medical document requires descriptors to define semantically meaningful relations but due to the ever changing demands in healthcare environment such information sources can be highly dynamic. In these situations the most challenging problem is frequent ontology search keeping with user’s interest. To manage this problem efficiently the paper suggests an ontology model using context aware properties of the system to facilitate the search process and allow dynamic ontology modification. The proposed method has been evaluated on Cancer datasets collected from publicly accessible sites and the results confirm its superiority over well known semantic similarity measures.
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Pei-Min Chen, Fong-Chou Kuo , An information retrieval system based on a user profile, The Journal of Systems and Software, vol. 54, pp. 3–8, 2000.
Dey, A.K., Salber, D. Abowd, G.D., “A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications”, Human-Computer Interaction Journal, Vol. 16(2–4), pp. 97–166, 2001.
Cimiano, P., Ontology Learning and Population from Text: Algorithms, Evaluation and Applications. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006.
Gargouri, Y., Lefebvre, B., Meunier, J. Ontology Maintenance using Textual Analysis. Proceedings of the Seventh World Multi-Conference on Systemics, Cybernetics and Informatics (SCI). Orlando, USA (2003).
M-Y. Chen, H-C. Chu, Y-M. Chen, Developing a semantic enable information retrieval mechanism. Expert Systems with Applications, 37:322–340, 2010.
Rohana K. Rajapakse, Michael. Denham, Text retrieval with more realistic concept matching and reinforcement learning. Information Processing and Management. 42(5), 1260–127, September 2006.
Ashraf, J., Khadeer Hussain, O., Khadeer Hussain, F.: A Framework for Measuring Ontology Usage on the Web. In: The Computer Journal. Oxford University Press, 2012.
J. Allan, et al. Challenges in information retrieval and language modeling. ACM SIGIR Forum, 37(1):31–47, 2003.
Fang Liu, Clement Yu, Personalized Web Search for Improving Retrieval Effectiveness, IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 1, January 2004.
Miyoung Cho, Hanil Kim, and Pankoo Kim. A new method for ontology merging based on concept using wordnet. In Advanced Communication Technology, 2006. ICACT 2006. The 8th International Conference, volume 3, pages 1573/1576, February 2006.
Philip Resnik, “Using Information Content to Evaluate Semantic Similarity in a taxonomy”, In Proceedings of the 14th International Joint Conference on Artificial Intelligence. Monte real, Canada, pp. 448–453, 1995.
C. Leacock, M. Chodorow. “Combining Local Context and WordNet Similarity for Word Sense Identification”, Computational Linguistics, vol. 24, no. 1, pp. 147–165, 1998.
Juanzi Li, Jie Tang, Yi Li, and Qiong LuoRiMOM: A Dynamic Multistrategy Ontology Alignment Framework, IEEE Transactions on Knowledge and Data Engineering, Vol. 21, No. 8, August 2009.
Ashraf, J., Khadeer Hussain, O., Khadeer Hussain, F.: A Framework for Measuring Ontology Usage on the Web. In: The Computer Journal. Oxford University Press, 2012.
David Riano; Francis Real; Joan Albert Lopez; Fabio Campana; Sara Ercolani; Patrizia Mecocci; Roberta Annicchiarico & Carlo Caltagirone. An Ontology based personalization of Healthcare knowledge to support clinical decisions for chronically ill patients, Journal of Biomedical Informatics, Elsevier, 429–446, 45 (2012).
http://www.cancer.org/research/index [Last accessed on 12th October, 2015].
http://www.cancercare.org/accessengagementreport [Last accessed on 12th October 2015].
http://www.oncolink.org/resources [Last accessed on 7th October, 2015].
http://med.stanford.edu/cancer.html [Last accessed on 25th September, 2015].
http://www.ncbi.nlm.nih.gov/pubmed [Last accessed on 21st September, 2015].
http://www.cdc.gov/cancer/ [Last accessed on 16th September, 2015].
Oh-Woog Kwon, Jong Hyeok Lee. Text categorization based on k-nearest neighbor approach for Web site classification, Information Processing and Management Volume 39, Issue 1, Pages 25–44, January 2003.
Guanyu Gao, Shengxiao Guan. Text categorization based on improved Rocchio algorithm. In Proc. of Systems and Informatics (ICSAI) IEEE International Conference, pp. 2247–2250, 2012.
Paulo Cremonesi, Yehuda Koren, Roberto Turrin. Performance of recommender algorithms on top-n recommendation tasks. In Proc. of the fourth ACM conference on Recommender Systems, pp. 39–46, ACM New York, USA, 2010.
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Anirban Chakrabarty, Sudipta Roy (2017). An Ontology Based Context Aware Protocol in Healthcare Services. In: Mandal, J., Satapathy, S., Sanyal, M., Bhateja, V. (eds) Proceedings of the First International Conference on Intelligent Computing and Communication. Advances in Intelligent Systems and Computing, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-10-2035-3_15
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DOI: https://doi.org/10.1007/978-981-10-2035-3_15
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