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Related Experiment Video

Updated: Sep 25, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Published on: June 13, 2025

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Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge

Somayeh Asadifar1, Mohsen Kahani1, Saeedeh Shekarpour2

  • 1Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Khorasan Razavi, Iran.

Peerj. Computer Science
|May 2, 2022
PubMed
Summary

This study introduces a novel source prediction method for hybrid question answering (QA) systems. The approach enhances scalability and accuracy by utilizing data details and a mediated schema, outperforming existing benchmarks.

Keywords:
CorpusData integrationHybrid searchKnowledge-basedQuestion answeringSource prediction

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Information Retrieval

Background:

  • Question Answering (QA) systems are categorized into knowledge-based (KB), text-based, and hybrid approaches.
  • Scalability in QA, especially with numerous sources, necessitates effective source prediction.
  • Existing hybrid QA source selection methods often lack robust evaluation and struggle with unstructured data.

Purpose of the Study:

  • To propose a novel source prediction method specifically for hybrid QA systems integrating multiple KB and text sources.
  • To address the limitations of existing source selection techniques that rely on metadata or triple instances.
  • To enhance the scalability and accuracy of hybrid QA through improved source prediction.

Main Methods:

  • Developed a source prediction method for hybrid QA systems using both KB and text sources.
  • Employed data details and the concept of a mediated schema for source prediction, ensuring data integration and scalability.
  • Evaluated the approach using word, triple, and question-level information.

Main Results:

  • The proposed method demonstrates strong performance against existing benchmarks.
  • Significant improvements in response time and accuracy were observed compared to current hybrid and KB source prediction methods.
  • The approach effectively handles unstructured sources in hybrid QA settings.

Conclusions:

  • The novel source prediction method offers a scalable and accurate solution for hybrid QA systems.
  • Utilizing data details and a mediated schema is crucial for effective source prediction in complex QA environments.
  • This research contributes to advancing the field of hybrid question answering and source prediction.