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

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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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Authors and Affiliations

  1. R.M.D. Engineering College, Chennai, India

    P. Shobha Rani

  2. Sri Lakshmi Ammaal Engineering College, Chennai, India

    R. M. Suresh

  3. R.M.K. Engineering College, Chennai, India

    R. Sethukarasi

Authors
  1. P. Shobha Rani
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  2. R. M. Suresh
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  3. R. Sethukarasi
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Correspondence to P. Shobha Rani.

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Rani, P.S., Suresh, R.M. & Sethukarasi, R. Multi-level semantic annotation and unified data integration using semantic web ontology in big data processing. Cluster Comput 22 (Suppl 5), 10401–10413 (2019). https://doi.org/10.1007/s10586-017-1029-7

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