Transforming Natural Language Query to SPARQL for Semantic Information Retrieval

Sharmela Shaik; Prathyusha Kanakam; S Mahaboob Hussain; D. Suryanarayana

doi:https://doi.org/10.14445/22315381/IJETT-V41P263

Research Article | Open Access | Download PDF

Volume 41 | Number 1 | Year 2016 | Article Id. IJETT-V41P263 | DOI : https://doi.org/10.14445/22315381/IJETT-V41P263

Transforming Natural Language Query to SPARQL for Semantic Information Retrieval

Sharmela Shaik, Prathyusha Kanakam, S Mahaboob Hussain, D. Suryanarayana

Citation :

Sharmela Shaik, Prathyusha Kanakam, S Mahaboob Hussain, D. Suryanarayana, "Transforming Natural Language Query to SPARQL for Semantic Information Retrieval," International Journal of Engineering Trends and Technology (IJETT), vol. 41, no. 1, pp. 347-350, 2016. Crossref, https://doi.org/10.14445/22315381/IJETT-V41P263

Abstract

To retrieve the information in a semantic manner requires a special query language to apply on the huge web and database. In general, the entire user queries will be in the form of natural language to search using the traditional search engine applications and no guarantee that user will satisfy with the outcome results. According to the users, querying the databases in natural language is a very easy method for the desired data but it might be difficult to understand the NL query by a machine. Therefore, this paper clearly explains the procedure and importance of reforming the natural language (NL) query into SPARQL query to apply to the database to retrieve the accurate semantic results. SPARQL is an RDF query language which is a semantic query language used to retrieve data and give precise results. Natural language query is an English sentence interpreted by the computer and appropriate action taken. Thus, in this paper architecture introduced to translate an NL query into SPARQL to retrieve semantic results and compared them with the traditional search engines.

Keywords

Information retrieval, NLP, natural language, RDF, SPARQL, semantic Web, semantic search, URIs, tagging, POS.

References

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