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Cluster Computing
Published in: Cluster Computing 5/2019

21-08-2017

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

Authors: P. Shobha Rani, R. M. Suresh, R. Sethukarasi

Published in: Cluster Computing | Special Issue 5/2019

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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%.
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Literature
1.
Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mob. Netw. Appl. 19(2), 171–209 (2014)CrossRef
2.
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)CrossRef
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)CrossRef
5.
Dong, X.L., Srivastava, D.: Big data integration. In: IEEE 29th International Conference on Data Engineering (ICDE), pp. 1245–1248 (2013)
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)CrossRef
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)
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)
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)CrossRef
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)
11.
Boury-Brisset, A.-C.: Managing semantic Big Data for intelligence. In: STIDS, pp. 41–47 (2013)
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)
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)
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)
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)
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)
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)CrossRef
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)
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)CrossRef
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)
21.
d’Aquin, M., Motta, E.: Watson, more than a semantic web search engine. Semant. Web 2(1), 55–63 (2011)
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)CrossRef
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)
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)
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)
26.
Alani, H., Brewster, C., Shadbolt, N.: Ranking ontologies with AKTiveRank. In: International Conference of Semantic Web-ISWC, pp. 1–15 (2006)
27.
Harold, E.R.: Processing Xml with Java. In: ACM Proceedings of the Addison-Wesley Longman Publishing (2002)
28.
Bizer, C., Seaborne, A.: D2rq—treating non-rdf databases as virtual rdf graphs. In: 3rd International Semantic Web Conference, vol. 2004 (2004)
29.
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)
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)
32.
33.
34.
Metadata
Title
Multi-level semantic annotation and unified data integration using semantic web ontology in big data processing
Authors
P. Shobha Rani
R. M. Suresh
R. Sethukarasi
Publication date
21-08-2017
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 5/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1029-7

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