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Linguistic rule-based translation of natural language question into SPARQL query for effective semantic question answering

Semantic question answering (SQA) demands different processing compared to the common information retrieval method because the semantic knowledge base is stored in the triples form. However, manipulating the knowledge requires understanding of its structure and proficiency in semantic query language...

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Main Authors: Mohd Sharef, Nurfadhlina, Mohd Noah, Shahrul Azman, Azmi Murad, Masrah Azrifah
Format: Article
Language:English
Published: Little Lion Scientific R&D 2015
Online Access:http://psasir.upm.edu.my/id/eprint/64662/1/Linguistic%20rule-based%20translation%20of%20natural%20language%20question%20into%20SPARQL%20query%20for%20effective%20semantic%20question%20answering.pdf
http://psasir.upm.edu.my/id/eprint/64662/
http://www.jatit.org/volumes/eighty3.php
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spelling my.upm.eprints.646622018-08-13T03:45:41Z http://psasir.upm.edu.my/id/eprint/64662/ Linguistic rule-based translation of natural language question into SPARQL query for effective semantic question answering Mohd Sharef, Nurfadhlina Mohd Noah, Shahrul Azman Azmi Murad, Masrah Azrifah Semantic question answering (SQA) demands different processing compared to the common information retrieval method because the semantic knowledge base is stored in the triples form. However, manipulating the knowledge requires understanding of its structure and proficiency in semantic query language such as SPARQL. Natural language interface (NLI) alleviates this by allowing user to input question in their human language. Then it produces an answer by translating the input into an equivalent SPARQL before it is executed to retrieve the answer. However, none of the existing research has presented a holistic computational model for the translation of NL question into an equivalent SPARQL for the semantic KB querying. Existing studies have focused mainly on the semantic disambiguation through consolidation where user interaction is initiated so that relevant concept can be chosen by the user to be inserted into the SPARQL. Besides, the linguistic understanding of the input has limited coverage where only one triple is constructed which loses many original expressions. There is a necessity to increase the linguistic understanding to involve multi-variables and multi-triples in the translated SPARQL so that accurate answer will be returned. Therefore, in this paper the linguistic challenge in NLI is addressed, specifically on the question complexity depth, processes that need to be performed to answer the question and gaps in existing study. A linguistic-rule-based translation model for natural language question is introduced that utilizes a set of observational variables to extract the information in the question; (i) checking if the focus is equals to subject, (ii) number of subjects, (iii) number of property, (iv) number of object, (v) checking if object is instance, (vi) checking if the question contains superlative expression, (vii) superlative orientation and (viii) checking if the question contains aggregates expression. The model is also aimed to reduce dependability on clarification dialogues. The results show that the approach has managed to eliminate clarification dialogues and increase linguistic coverage of NLI. Little Lion Scientific R&D 2015 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/64662/1/Linguistic%20rule-based%20translation%20of%20natural%20language%20question%20into%20SPARQL%20query%20for%20effective%20semantic%20question%20answering.pdf Mohd Sharef, Nurfadhlina and Mohd Noah, Shahrul Azman and Azmi Murad, Masrah Azrifah (2015) Linguistic rule-based translation of natural language question into SPARQL query for effective semantic question answering. Journal of Theoretical and Applied Information Technology, 80 (3). pp. 557-575. ISSN 1992-8645; ESSN: 1817-3195 http://www.jatit.org/volumes/eighty3.php
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Semantic question answering (SQA) demands different processing compared to the common information retrieval method because the semantic knowledge base is stored in the triples form. However, manipulating the knowledge requires understanding of its structure and proficiency in semantic query language such as SPARQL. Natural language interface (NLI) alleviates this by allowing user to input question in their human language. Then it produces an answer by translating the input into an equivalent SPARQL before it is executed to retrieve the answer. However, none of the existing research has presented a holistic computational model for the translation of NL question into an equivalent SPARQL for the semantic KB querying. Existing studies have focused mainly on the semantic disambiguation through consolidation where user interaction is initiated so that relevant concept can be chosen by the user to be inserted into the SPARQL. Besides, the linguistic understanding of the input has limited coverage where only one triple is constructed which loses many original expressions. There is a necessity to increase the linguistic understanding to involve multi-variables and multi-triples in the translated SPARQL so that accurate answer will be returned. Therefore, in this paper the linguistic challenge in NLI is addressed, specifically on the question complexity depth, processes that need to be performed to answer the question and gaps in existing study. A linguistic-rule-based translation model for natural language question is introduced that utilizes a set of observational variables to extract the information in the question; (i) checking if the focus is equals to subject, (ii) number of subjects, (iii) number of property, (iv) number of object, (v) checking if object is instance, (vi) checking if the question contains superlative expression, (vii) superlative orientation and (viii) checking if the question contains aggregates expression. The model is also aimed to reduce dependability on clarification dialogues. The results show that the approach has managed to eliminate clarification dialogues and increase linguistic coverage of NLI.
format Article
author Mohd Sharef, Nurfadhlina
Mohd Noah, Shahrul Azman
Azmi Murad, Masrah Azrifah
spellingShingle Mohd Sharef, Nurfadhlina
Mohd Noah, Shahrul Azman
Azmi Murad, Masrah Azrifah
Linguistic rule-based translation of natural language question into SPARQL query for effective semantic question answering
author_facet Mohd Sharef, Nurfadhlina
Mohd Noah, Shahrul Azman
Azmi Murad, Masrah Azrifah
author_sort Mohd Sharef, Nurfadhlina
title Linguistic rule-based translation of natural language question into SPARQL query for effective semantic question answering
title_short Linguistic rule-based translation of natural language question into SPARQL query for effective semantic question answering
title_full Linguistic rule-based translation of natural language question into SPARQL query for effective semantic question answering
title_fullStr Linguistic rule-based translation of natural language question into SPARQL query for effective semantic question answering
title_full_unstemmed Linguistic rule-based translation of natural language question into SPARQL query for effective semantic question answering
title_sort linguistic rule-based translation of natural language question into sparql query for effective semantic question answering
publisher Little Lion Scientific R&D
publishDate 2015
url http://psasir.upm.edu.my/id/eprint/64662/1/Linguistic%20rule-based%20translation%20of%20natural%20language%20question%20into%20SPARQL%20query%20for%20effective%20semantic%20question%20answering.pdf
http://psasir.upm.edu.my/id/eprint/64662/
http://www.jatit.org/volumes/eighty3.php
_version_ 1643838088334540800
score 13.211869

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