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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Querying knowledge graphs in natural language.

Shiqi Liang1, Kurt Stockinger2, Tarcisio Mendes de Farias3,4

  • 1ETH Swiss Federal Institute of Technology, Rämistrasse 101, 8092 Zurich, Switzerland.

Journal of Big Data
|January 25, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Question-Answering (QA) system that translates natural language questions into SPARQL queries. The system significantly improves accuracy, outperforming state-of-the-art methods on benchmark datasets.

Keywords:
Knowledge graphsNatural language processingQuery processingSPARQL

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Knowledge Representation

Background:

  • Knowledge graphs offer powerful data querying but require specialized expertise (e.g., SPARQL, RDF).
  • Existing Question-Answering (QA) systems simplify access but have limitations in accuracy.
  • End-users often lack the technical knowledge for direct interaction with complex data models.

Purpose of the Study:

  • To develop an advanced QA system for translating natural language questions into SPARQL queries.
  • To enhance the accuracy and accessibility of knowledge graph querying for end-users.
  • To leverage machine learning and neural networks for natural language to query translation.

Main Methods:

  • A novel QA system breaking down natural language to SPARQL translation into 5 sub-tasks.
  • Utilizing ensemble machine learning methods for improved translation accuracy.
  • Employing Tree-LSTM-based neural network models for automated learning and translation.

Main Results:

  • The proposed QA system demonstrated superior performance on benchmark datasets.
  • Achieved a 15% improvement over state-of-the-art on the QALD-7 dataset.
  • Achieved a 48% improvement over state-of-the-art on the LC-QuAD dataset.

Conclusions:

  • The developed QA system effectively translates natural language questions into SPARQL queries.
  • The system offers a significant improvement in accuracy compared to existing methods.
  • The source code is publicly available to facilitate further research and development.