Efficient SPARQL Queries Generator for Question Answering Systems

Yi Hui Chen, Eric Jui Lin Lu*, Ying Yen Lin

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)99850-99860
Number of pages11
JournalIEEE Access
Volume10
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Question answering system (QA)
  • SPARQL query
  • query cost
  • question answering over linked data (QALD)
  • recurrent neural network (RNN)

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