International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Semantic Ontology Based Querying On Biomedical Concepts

Author : Thayyaba Khatoon Mohammed ¹ Dr.A.Govardhan ²

Date of Publication :7th August 2016

Abstract: Biomedical knowledge resources, such as terminologies and on tologies, are important for community-based annotation and sharing of data. Creating and maintaining these resources is challenging given the rapid growth of scientific knowledge. Hence query processing on biomedical data become very slow which increases complexity in performance and efficiency query processing on biomedical data and also there is a lack of tools to ease the integration and ontology based semantic queries in biomedical databases, which are often annotated with ontology concepts. We have developed a system called ONTOBIO, which tells about semantic query engine that provides semantic reasoning and query processing, and translates the queries into ontology repository operations on biomedical data. The system provides an interface for executing queries and bio ontologies which organize taxonomies information. This paper addresses about attributes and describes relationships between biomedical concepts. The system is also compatible for integration and deployable with databases and description logic based ontologies. ONTOBIO integrates the two schemes by Bio medical concepts using a set of semantic rules. We have applied our method to aknowledge base of autism phenotype definitions, which are modeled using the web ontology language, and its rule language, SWRL.

Reference :

1. Cimino JJ. and Zhu X. The practical impact of ontologies on biomedical informatics. Methods Inf Med 45 Suppl 1 2006:124–35.
2. Rubin DL, Shah NH. andNoy NF. Biomedical ontologies: a functional perspective. Brief Bioinform 2007.
3. NCBO BioPortal. bioportal.bioontology.org/. (accessedJul 2013).
4. A. Budanitsky and G. Hirst, “Evaluating WordNetbased measures of semantic distance,” Comput. Linguistics, vol. 32, no. 1, pp. 13–47, 2006.
5. J. E. Caviedes and J. J. Cimino, “Towards the development of a conceptual distance metric for the UMLS,” J. Biomed. Inf., vol. 37, no. 2, pp. 77–85,2004.
6. A. Hliaoutakis, “Semantic similarity measures in MeSH ontology and their application to information retrieval on Medline,” Master’s thesis,Tech. Univ. Crete, Chani´a, Crete, 2005.
7. Caviedesa, J.E., Cimino, J.J.: Towards the development of a conceptual distance metricfor the UMLS. Biomedical Informatics 37(2), 77–85 (2004).
8. Ashburner, M., Sim, I., Hute, C.G., Solbrig, H., Storey, M.A., Smith, B., Day-Richter, J.,Noy, N.F., Musen, M.A.: National Center for Biomedical Ontology: AdvancingBiomedicine through Structured Organization of Scientific Knowledge. OMICS AJournal of Integrative Biology 10(2), 185–198 (2006)Ontology-based Queries over Cancer DataAlejandra Gonz´alez-Beltr´an1,2, Ben Tagger1, and Anthony Finkelstein.
9. Enabling Ontology Based Semantic Queries In Biomedical Database Systems,Shuai Zheng.
10. Semantic Data Integration Environment for Biomedical Research, (Ontological Framework for e-Science & Imaginizer)V. Astakhov, B. Sanders, Jeffrey S. Grethe, E. Ross, D. Little, A. Gupta, T. Astakhova.
11. An Evaluation Framework for Large-ScaleOntologybased Biomedical Data IntegratedSystemsby, Gilbert Maiga.
12. Biomedical Data Management: a ProposalFrameworkDouglas TEODOROa,1, Rémy CHOQUETb, Emilie PASCHEa, Julien GOBEILLa,c,
13. Mining Biomedical Ontologies and Data Using RDFHypergraphsHaishan Liu, Dejing Dou.
14. Current status of ontologies in Biomedical and Clinical Informatics,Rishi Kanth Saripalle.
15. http://www.immunetolerance.org/sites/files/BMIR2007-1245.pdf
16. http://www.ncbi.nlm.nih.gov/pubmed/23404054
17. http://www.sersc.org/journals/IJDTA/vol4_no2/2.pdf
18. http://sophocles.gws.uky.edu/~ben/Interface.html.
19. H. G. Molina et al., The TSIMMIS Approach to Mediation: Data Models and Languages. Journal ofIntelligent Information Systems 8 (1997), 117-132.
20. C. A. Goble et al., Transparent access to multiple bioinformatics information sources. IBM SYSTEMSJOURNAL 40 (2001), 532-551.
21. Y. Arens, C. A. Knoblock and C. Hsu, Query Processing in the SIMS Information Mediator. AdvancedPlanning Technology, Austin Tate, AAAI Press, Menlo Park, CA (1996).
22. A. D. Preece et al., The KRAFT Architecture for Knowledge Fusion and Transformation. Proceedings of the 19th SGES Int. Conf. on Knowledge-based Systems and Applied Artificial Intelligence (1999)
23. T. Etzold and P. Argos, SRS – an indexing and retrieval tool for flat file data libraries. COMPUTERAPPLICATIONS IN THE BIOSCIENCES 9 (1993), 49-57.
24. G. D. Schuler et al., Entrez: Molecular biology database and retrieval system. COMP. METHODS FORMACROMOLECULAR SEQUENCE ANALYSIS 266 (1996), 141-162.
25. P. Kersey L. Bower, L. Morris et al., Integr8 and Genome Reviews: integrated views of completegenomes and proteomes. NUCLEIC ACIDS RESEARCH 33 (2005), D297-D302.
26. C. Lovis et al., DebugIT for patient safety - improving the treatment with antibiotics through multimedia data mining of heterogeneous clinical data. Stud Health Tech Inform 136 (2008), 641-6