Efficient SPARQL Queries Generator for Question Answering Systems
Much like traditional database querying, the question answering process in a Question Answering (QA) system involves converting a user’s question input into query grammar, querying the knowledge base through the query grammar, and finally returning the query result (i.e., the answer) to t...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9893129/ |
| _version_ | 1811208581816516608 |
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| author | Yi-Hui Chen Eric Jui-Lin Lu Ying-Yen Lin |
| author_facet | Yi-Hui Chen Eric Jui-Lin Lu Ying-Yen Lin |
| author_sort | Yi-Hui Chen |
| collection | DOAJ |
| description | Much like traditional database querying, the question answering process in a Question Answering (QA) system involves converting a user’s question input into query grammar, querying the knowledge base through the query grammar, and finally returning the query result (i.e., the answer) to the user. The accuracy of query grammar generation is therefore important in determining whether a Question Answering system can produce a correct answer. Generally speaking, incorrect query grammar will never find the right answer. SPARQL is the most frequently used query language in question answering systems. In the past, SPARQL was generated based on graph structures, such as dependency trees, syntax trees and so on. However, the query cost of generating SPARQL is high, which creates long processing times to answer questions. To reduce the query cost, this work proposes a low-cost SPARQL generator named Light-QAWizard, which integrates multi-label classification into a recurrent neural network (RNN), builds a template classifier, and generates corresponding query grammars based on the results of template classifier. Light-QAWizard reduces query frequency to DBpedia by aggregating multiple outputs into a single output using multi-label classification. In the experimental results, Light-QAWizard’s performance on Precision, Recall and F-measure metrics were evaluated on the QALD-7, QALD8 and QALD-9 datasets. Not only did Light-QAWizard outperform all other models, but it also had a lower query cost that was nearly half that of QAWizard. |
| first_indexed | 2024-04-12T04:24:58Z |
| format | Article |
| id | doaj.art-cf66426cacbc4505925b919358a34346 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T04:24:58Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cf66426cacbc4505925b919358a343462022-12-22T03:48:08ZengIEEEIEEE Access2169-35362022-01-0110998509986010.1109/ACCESS.2022.32067949893129Efficient SPARQL Queries Generator for Question Answering SystemsYi-Hui Chen0https://orcid.org/0000-0002-9932-0594Eric Jui-Lin Lu1https://orcid.org/0000-0001-7953-5486Ying-Yen Lin2Department of Information Management, Chang Gung University, Taoyuan, TaiwanDepartment of Management Information Systems, National Chung Hsing University, Taichung, TaiwanDepartment of Management Information Systems, National Chung Hsing University, Taichung, TaiwanMuch like traditional database querying, the question answering process in a Question Answering (QA) system involves converting a user’s question input into query grammar, querying the knowledge base through the query grammar, and finally returning the query result (i.e., the answer) to the user. The accuracy of query grammar generation is therefore important in determining whether a Question Answering system can produce a correct answer. Generally speaking, incorrect query grammar will never find the right answer. SPARQL is the most frequently used query language in question answering systems. In the past, SPARQL was generated based on graph structures, such as dependency trees, syntax trees and so on. However, the query cost of generating SPARQL is high, which creates long processing times to answer questions. To reduce the query cost, this work proposes a low-cost SPARQL generator named Light-QAWizard, which integrates multi-label classification into a recurrent neural network (RNN), builds a template classifier, and generates corresponding query grammars based on the results of template classifier. Light-QAWizard reduces query frequency to DBpedia by aggregating multiple outputs into a single output using multi-label classification. In the experimental results, Light-QAWizard’s performance on Precision, Recall and F-measure metrics were evaluated on the QALD-7, QALD8 and QALD-9 datasets. Not only did Light-QAWizard outperform all other models, but it also had a lower query cost that was nearly half that of QAWizard.https://ieeexplore.ieee.org/document/9893129/Question answering system (QA)SPARQL queryquery costrecurrent neural network (RNN)question answering over linked data (QALD) |
| spellingShingle | Yi-Hui Chen Eric Jui-Lin Lu Ying-Yen Lin Efficient SPARQL Queries Generator for Question Answering Systems IEEE Access Question answering system (QA) SPARQL query query cost recurrent neural network (RNN) question answering over linked data (QALD) |
| title | Efficient SPARQL Queries Generator for Question Answering Systems |
| title_full | Efficient SPARQL Queries Generator for Question Answering Systems |
| title_fullStr | Efficient SPARQL Queries Generator for Question Answering Systems |
| title_full_unstemmed | Efficient SPARQL Queries Generator for Question Answering Systems |
| title_short | Efficient SPARQL Queries Generator for Question Answering Systems |
| title_sort | efficient sparql queries generator for question answering systems |
| topic | Question answering system (QA) SPARQL query query cost recurrent neural network (RNN) question answering over linked data (QALD) |
| url | https://ieeexplore.ieee.org/document/9893129/ |
| work_keys_str_mv | AT yihuichen efficientsparqlqueriesgeneratorforquestionansweringsystems AT ericjuilinlu efficientsparqlqueriesgeneratorforquestionansweringsystems AT yingyenlin efficientsparqlqueriesgeneratorforquestionansweringsystems |