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A Natural Language Interface Concordant with a Knowledge Base.

Yong-Jin Han1, Seong-Bae Park1, Se-Young Park1

  • 1School of Computer Science and Engineering, Kyungpook National University, 80 Daehakro, Buk-gu, Daegu 41566, Republic of Korea.

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This study introduces a novel method for natural language interfaces (NLIs) to bridge the gap between user questions and knowledge base answers. The approach translates natural language into formal queries, ensuring accurate retrieval and rejection of unanswerable questions.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • A significant challenge in Natural Language Interfaces (NLIs) is the discordance between expressions understood by the system and those answerable by a knowledge base.
  • This discrepancy hinders effective information retrieval and user interaction with data-driven systems.

Purpose of the Study:

  • To propose and evaluate a method for translating natural language questions into formal queries executable by a graph-based knowledge base.
  • To address the discordance problem by establishing a robust mapping between natural language expressions and formal queries.

Main Methods:

  • Developed a method to translate natural language questions into formal queries derived from a graph-based knowledge base.
  • Pre-generated all formal queries and their corresponding natural language expressions, establishing a one-to-one mapping.
  • Implemented a matching mechanism where questions are translated by identifying the most appropriate pre-collected natural language expression.

Main Results:

  • The proposed method effectively translates natural language questions into formal queries for knowledge base retrieval.
  • Questions with insufficient matching confidence are reliably rejected, preventing incorrect answers.
  • Experimental results demonstrate the method's efficacy in handling answerable questions and rejecting unanswerable ones.

Conclusions:

  • The developed method successfully resolves the discordance between natural language expressions and knowledge base query capabilities.
  • This approach enhances the accuracy and reliability of Natural Language Interfaces by ensuring only answerable questions are processed.