Since long, organizations have been looking for information sources that can store data which can provide structured organization and focuses on meaning access of data. Sematic web helps a lot in this context to retrieve meaningful data. This paper emphasizes on mapping developed ontology terms and their retrieval with the help of Graph Stream. We developed an ontology for Pulmonary diseases which consists of classes , objects, relations, and their properties and division of diseases is given as sub classes and Levenshtein’s Edit system algorithm has been used for similarity calculations. The generated ontology has been sent for preprocessing and is fed to Graph Stream for graph generation. The results produced are comparable to the results of human annotators.

Ontology mapping, pulmonary diseases, similarity calculations, graph generation, human evaluation

[1] A Kivela, E Hyvonen .”Ontological theories for the Semantic Web”, Helsinki: HIIT Publications, 2002, pp.111- 136
[2] T Gruber, ”Towards principles for the design of ontologies used for knowledge sharing”, International Journal of Human-Computer Studies .Vol. 43(5/6), pp.907-928. 1995
[3] Harmelen, Frank & Lifschitz, Vladimir & Porter, Bruce. (2007).” The Handbook of Knowledge Representation”. Elsevier Science San Diego, USA. 1034.
[4] Marvin Minsky. 1974. A Framework for Representing Knowledge. Technical Report. Massachusetts Institute of Technology, Cambridge, MA, USA.
[5] J. Han and Y. Fu. “Dynamic generation and renement of concept hierarchies for knowledge discovery in databases”. In Proc. AAAI`94 Workshop on Knowledge Discovery in Databases(KDD`94), Seattle, WA, 157-168, 1994.
[6] J. Han. Mining knowledge at multiple concept levels. In Proc. 4th Int. Conf. on Information and Knowledge Management (CIKM`95), Baltimore, Maryland, 19-24, 1995.
[7] Bali, R.K., 2000. Towards a qualitative informed model for EPR implementation: Considering organizational culture. Proceeding of the IEEE International Conference on Information Technology Applications in Biomedicine (ITABITIS), Arlington, USA., pp: 353-358.
[8] Bose, R., 2003. Knowledge management-enabled health care management systems: Capabilities, infrastructure and decision-support. Exp. Syst. Appl., 24: 59-71
[9]A. Gómez-PérezOntology evaluationHandbook on Ontologies (first ed.), Springer, Berlin (2004)pp. 251–275
[10] Zweigenbaum, J. Bouaud, B. Bachimont, J. Charlet, B. Séroussi, J.- Boisvieux From text to knowledge: a unifying document-oriented view of analyzed medical language Methods Inf. Med., 37 (4–5) (1998), pp. 384-393
[11] Audrey Baneyx, Jean Charlet, Marie-Christine Jaulent, Building an ontology of pulmonary diseases with natural language processing tools using textual corpora, International Journal of Medical Informatics,Volume 76, Issues 2–3,2007.
[12]J. Simon, M. Dos Santos, J. Fielding, B. SmithFormal ontology for natural language processing and the integration of biomedical databases Int. J. Med. Inf., 75 (3–4) (2006), pp. 224-231
[13]J. Charlet, B. Bachimont, M.-C. JaulentBuilding medical ontologies by terminology extraction from texts: a proposalComput. Biol. Med. (2005)
[14] J. Euzenat and P. Shvaiko Ontology Matching, 2007, ISBN: 978-3 540-49611-3, Springer Berlin Heidelberg, New York
[15] Rishin, Haldar, Debajyoti Mukhopadhyay, Levenshtein Distance Technique in Dictionary Lookup Methods: An Improved Approach, arXiv preprint arXiv: 1101.1232, 2011.
[16] C. Dharma Devi, S. Thaddeus “Information Retrieval Mechanism for Dynamic Health Care” International Journal of Computer Sciences and Engineering Vol.-6, Issue-9, Sept. 2018