SWFQA Semantic Web Based Framework for Question Answering

SWFQA Semantic Web Based Framework for Question Answering

Irphan Ali (Uttarakhand Technical University, Dehradun, India), Divakar Yadav (Madan Mohan Malviya University of Technology, Gorakhpur, India) and Ashok Kumar Sharma (BSA Institute of Technology and Management, Faridabad, India)
Copyright: © 2019 |Pages: 19
DOI: 10.4018/IJIRR.2019010106

Abstract

A question answering system aims to provide the correct and quick answer to users' query from a knowledge base. Due to the growth of digital information on the web, information retrieval system is the need of the day. Most recent question answering systems consult knowledge bases to answer a question, after parsing and transforming natural language queries to knowledge base-executable forms. In this article, the authors propose a semantic web-based approach for question answering system that uses natural language processing for analysis and understanding the user query. It employs a “Total Answer Relevance Score” to find the relevance of each answer returned by the system. The results obtained thereof are quite promising. The real-time performance of the system has been evaluated on the answers, extracted from the knowledge base.
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BASEBALL (Green, 1961) and LUNAR (Woods, 1973) is among the first known question answering systems (QAS). It could answer queries regarding dates, locations, and American baseball games. LUNAR was one of the primary scientific QAS. It helped the geographical examination of the stones brought by the Apollo mission and accurately addressed 90% of the queries posed by users.

The presence of semantic information in the web pages permits machines to process such information and helps users to search, share and merge the information more conveniently (Shen, 2018). Natural language-based question answering systems for semantic web generates relations between words in document repository as well as finds the accurate answers (Saint-Dizier & Moens, 2011; Melo, 2018; Subalalitha, 2017). Moreover, this system uses the knowledge of reasoning to interpret the returned document, to get the correct answer corresponding to user’s query without his/her help.

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