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Erschienen in: Cluster Computing 5/2019

21.08.2017

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

verfasst von: P. Shobha Rani, R. M. Suresh, R. Sethukarasi

Erschienen in: Cluster Computing | Sonderheft 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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Metadaten
Titel
Multi-level semantic annotation and unified data integration using semantic web ontology in big data processing
verfasst von
P. Shobha Rani
R. M. Suresh
R. Sethukarasi
Publikationsdatum
21.08.2017
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe Sonderheft 5/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1029-7

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