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Disambiguating the Twitter Stream Entities and Enhancing the Search Operation Using DBpedia Ontology: Named Entity Disambiguation for Twitter Streams

Disambiguating the Twitter Stream Entities and Enhancing the Search Operation Using DBpedia Ontology: Named Entity Disambiguation for Twitter Streams

N. Senthil Kumar, Dinakaran Muruganantham
Copyright: © 2016 |Volume: 11 |Issue: 2 |Pages: 12
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781466689572|DOI: 10.4018/IJITWE.2016040104
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MLA

Kumar, N. Senthil, and Dinakaran Muruganantham. "Disambiguating the Twitter Stream Entities and Enhancing the Search Operation Using DBpedia Ontology: Named Entity Disambiguation for Twitter Streams." IJITWE vol.11, no.2 2016: pp.51-62. http://doi.org/10.4018/IJITWE.2016040104

APA

Kumar, N. S. & Muruganantham, D. (2016). Disambiguating the Twitter Stream Entities and Enhancing the Search Operation Using DBpedia Ontology: Named Entity Disambiguation for Twitter Streams. International Journal of Information Technology and Web Engineering (IJITWE), 11(2), 51-62. http://doi.org/10.4018/IJITWE.2016040104

Chicago

Kumar, N. Senthil, and Dinakaran Muruganantham. "Disambiguating the Twitter Stream Entities and Enhancing the Search Operation Using DBpedia Ontology: Named Entity Disambiguation for Twitter Streams," International Journal of Information Technology and Web Engineering (IJITWE) 11, no.2: 51-62. http://doi.org/10.4018/IJITWE.2016040104

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Abstract

The web and social web is holding the huge amount of unstructured data and makes the searching processing more cumbersome. The principal task here is to migrate the unstructured data into the structured data through the appropriate utilization of named entity detections. The goal of the paper is to automatically build and store the deep knowledge base of important facts and construct the comprehensive details about the facts such as its related named entities, its semantic classes of the entities and its mutual relationship with its temporal context can be thoroughly analyzed and probed. In this paper, the authors have given and proposed the model to identify all the major interpretations of the named entities and effectively link them to the appropriate mentions of the knowledge base (DBpedia). They finally evaluate the approaches that uniquely identify the DBpedia URIs of the selected entities and eliminate the other candidate mentions of the entities based on the authority rankings of those candidate mentions.

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