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Cluster Computing

, Volume 22, Supplement 5, pp 10401–10413 | Cite as

Multi-level semantic annotation and unified data integration using semantic web ontology in big data processing

  • P. Shobha RaniEmail author
  • R. M. Suresh
  • R. Sethukarasi
Article

Abstract

The potential applications of big data need semantic annotation and unified integration of heterogeneous data. This paper proposes MOUNT a multi-level annotation and integration framework that significantly process the heterogeneous dataset by exploiting the semantic knowledge to improve the query processing in the large scale infrastructure. The multi-level annotation proposes the coarse-grained and fine-grained annotation models. The coarse-grained annotation employs Yago and SEeds SEarch to categorize the domain information on the big data and fine-grained annotation enables semantic enrichment. Moreover, the MOUNT approach integrates the structured and unstructured data to form the global resource description framework ontology. Moreover, it facilitates the query processing by translating the natural language user query into structured triples. The experimental results prove that the MOUNT approach yields a better performance in terms of result accuracy by 94%.

Keywords

Big data Annotation Yago Structured Unstructured Query 

References

  1. 1.
    Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mob. Netw. Appl. 19(2), 171–209 (2014)CrossRefGoogle Scholar
  2. 2.
    Emani, C.K., Cullot, N., Nicolle, C.: Understandable big data: a survey. Comput. Sci. Rev. 17, 70–81 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Zhou, Z.H., Chawla, N.V., Jin, Y., Williams, G.J.: Big data opportunities and challenges: discussions from data analytics perspectives. IEEE Trans. Comput. Intell. Mag. 9(4), 62–74 (2014)CrossRefGoogle Scholar
  4. 4.
    Liao, Y., Lezoche, M., Panetto, H., Boudjlida, N., Loures, E.R.: Semantic annotation for knowledge explicitation in a product lifecycle management context: a survey. Comput. Ind. 71, 24–34 (2015)CrossRefGoogle Scholar
  5. 5.
    Dong, X.L., Srivastava, D.: Big data integration. In: IEEE 29th International Conference on Data Engineering (ICDE), pp. 1245–1248 (2013)Google Scholar
  6. 6.
    Chen, C.P., Zhang, C.Y.: Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf. Sci. 275, 314–347 (2014)CrossRefGoogle Scholar
  7. 7.
    Dou, D., Wang, H., Liu, H.: Semantic data mining: a survey of ontology-based approaches. In: IEEE International Conference on Semantic Computing (ICSC), pp. 244–251 (2015)Google Scholar
  8. 8.
    El-Sappagh, S.H., Hendawi, A.M., El Bastawissy, A.H.: A proposed model for data warehouse ETL processes. J. King Saud Univ. Comput. Inf. Sci. 23(2), 91–104 (2011)Google Scholar
  9. 9.
    Buche, P., Dibie-Barthelemy, J., Ibanescu, L., Soler, L.: Fuzzy web data tables integration guided by an ontological and terminological resource. IEEE Trans. Knowl. Data Eng. 25(4), 805–819 (2013)CrossRefGoogle Scholar
  10. 10.
    Salmen, D., Malyuta, T., Hansen, A., Cronen, S., Smith, B.: Integration of intelligence data through semantic enhancement. In: Semantic Technology in Intelligence, Defense and Security (STIDS) (2011)Google Scholar
  11. 11.
    Boury-Brisset, A.-C.: Managing semantic Big Data for intelligence. In: STIDS, pp. 41–47 (2013)Google Scholar
  12. 12.
    Robak, S., Franczyk, B., Robak, M.: Applying big data and linked data concepts in supply chains management. In: IEEE Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1215–1221 (2013)Google Scholar
  13. 13.
    Sint, R., Schaffert, S., Stroka, S., Ferstl, R.: Combining unstructured, fully structured and semi-structured information in semantic wikis. In: Fourth Workshop on Semantic Wikis—The Semantic Wiki Web 6th European Semantic Web Conference Hersonissos, p. 73 (2009)Google Scholar
  14. 14.
    Bhide, M.A., Gupta, A., Gupta, R., Roy, P., Mohania, M.K., Ichhaporia, Z.: Liptus: associating structured and unstructured information in a banking environment. In: CM Proceedings of the SIGMOD International Conference on Management of Data, pp. 915–924 (2007)Google Scholar
  15. 15.
    Park, B.K., Song, I.Y.: Toward total business intelligence incorporating structured and unstructured data. In: ACM Proceedings of the 2nd International Workshop on Business Intelligence and the Web, pp. 12–19 (2011)Google Scholar
  16. 16.
    Unger, C., Cimiano, P.: Pythia: compositional meaning construction for ontology-based question answering on the semantic web. In: Springer International Conference on Application of Natural Language to Information Systems, pp. 153–160 (2011)Google Scholar
  17. 17.
    Shekarpour, S., Marx, E., Ngomo, A.C., Auer, S.: Sina: semantic interpretation of user queries for question answering on interlinked data. Sci. Serv. Agents World Wide Web 30, 39–51 (2015)CrossRefGoogle Scholar
  18. 18.
    Yao, Y., Yi, J., Liu, Y., Zhao, X., Sun, C.: Query processing based on associated semantic context inference. In: IEEE 2nd International Conference on Information Science and Control Engineering (ICISCE), pp. 395–399 (2015)Google Scholar
  19. 19.
    Liu, C., Wang, H., Yu, Y., Xu, L.: Towards efficient SPARQL query processing on RDF data. Tsinghua Sci. Technol. 15(6), 613–622 (2010)CrossRefGoogle Scholar
  20. 20.
    Ding, L., Pan, R., Finin, T., Joshi, A., Peng, Y., Kolari, P.: Finding and ranking knowledge on the semantic web. In: International Semantic Web Conference, pp. 156–170 (2005)Google Scholar
  21. 21.
    d’Aquin, M., Motta, E.: Watson, more than a semantic web search engine. Semant. Web 2(1), 55–63 (2011)Google Scholar
  22. 22.
    Qu, Y., Cheng, G.: Falcons concept search: a practical search engine for web ontologies. IEEE Trans. Syst. Man Cybern. A 41(4), 810–816 (2011)CrossRefGoogle Scholar
  23. 23.
    Sabou, M., d’Aquin, M., Motta, E.: Exploring the semantic web as background knowledge for ontology matching. J. Data Semant. 11, 156–190 (2008)Google Scholar
  24. 24.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: ACM Proceedings of the 16th International Conference on World Wide Web, pp. 697–706 (2007)Google Scholar
  25. 25.
    O’Madadhain, J., Fisher, D., Smyth, P., White, S., Boey, Y.B.: Analysis and visualization of network data using JUNG. J. Stat. Softw. 10(2), 1–35 (2005)Google Scholar
  26. 26.
    Alani, H., Brewster, C., Shadbolt, N.: Ranking ontologies with AKTiveRank. In: International Conference of Semantic Web-ISWC, pp. 1–15 (2006)Google Scholar
  27. 27.
    Harold, E.R.: Processing Xml with Java. In: ACM Proceedings of the Addison-Wesley Longman Publishing (2002)Google Scholar
  28. 28.
    Bizer, C., Seaborne, A.: D2rq—treating non-rdf databases as virtual rdf graphs. In: 3rd International Semantic Web Conference, vol. 2004 (2004)Google Scholar
  29. 29.
  30. 30.
    Winkler, W.E.: The State of Record Linkage and Current Research Problems. Technical report. Statistical Research Division, U.S. Bureau of the Census, Washington, DC (1999)Google Scholar
  31. 31.
    Rusu, D., Dali, L., Fortuna, B., Grobelnik, M., Mladenic, D.: Triplet extraction from sentences. In: Proceedings of the 10th International Multiconference on Information Society-IS, pp. 8–12 (2007)Google Scholar
  32. 32.
  33. 33.
    Snyder, W.E.: NC state university image analysis laboratory database. http://www.ece.ncsu.edu/imaging/Archives/ImageDataBase/ (2002)
  34. 34.

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • P. Shobha Rani
    • 1
    Email author
  • R. M. Suresh
    • 2
  • R. Sethukarasi
    • 3
  1. 1.R.M.D. Engineering CollegeChennaiIndia
  2. 2.Sri Lakshmi Ammaal Engineering CollegeChennaiIndia
  3. 3.R.M.K. Engineering CollegeChennaiIndia

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