Context based Document Indexing and Retrieval using Big Data Analytics - A Review

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K. Swapnika, K. Swanthana


In past few years it is observed that the internet usage is been grown wider all over the world, hence, the data generation and usage is been increased rapidly by the users, the data generated in different forms may or may not be structured. The usage of internet by individuals and organizations have been grown so, there is increasing quantity and diversity of digital data in the form of documents, became available to the end users. The Storage, Maintenance and organization of such huge data in databases is a challenging task. So, there is a great need of efficient and effective retrieval technique which focuses on improving the accuracy of document retrieval. In this paper we are going to discuss about document retrieval using context based indexing approach. Here lexical association between terms is used to separate content carrying terms and other-terms. Content carrying terms are used as they give idea about theme of the document. Indexing weight calculation is done for content carrying terms. Lexical association measure is used to calculate indexing weight of terms. The term having higher indexing weight is considered as important and sentence which contains these terms is also important. When user enters search query, the important terms are matched with the terms with higher weights in order to retrieve documents. The explicit semantic relation or frequent co-occurrence of terms is been considered in this context based indexing.

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How to Cite
, K. S. K. S. “Context Based Document Indexing and Retrieval Using Big Data Analytics - A Review”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 6, no. 6, June 2018, pp. 05-07, doi:10.17762/ijritcc.v6i6.1622.