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Related Experiment Video

Updated: Aug 16, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Complex Knowledge Base Question Answering for Intelligent Bridge Management Based on Multi-Task Learning and

Xiaoxia Yang1, Jianxi Yang1, Ren Li1

  • 1School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China.

Entropy (Basel, Switzerland)
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for intelligent bridge management, enhancing knowledge services by utilizing accumulated domain data. The proposed complex knowledge base question answering (C-KBQA) model improves data analysis and decision-making in bridge maintenance.

Keywords:
bridge managementcomplex questioncross-task constraintsknowledge base question answeringmulti-task learning

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Area of Science:

  • Civil Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Bridge management generates vast domain information (attributes, defects, conditions) that is often underutilized.
  • Current knowledge services in bridge management are insufficient due to poor information utilization.
  • Existing pipeline models can suffer from error propagation, limiting their effectiveness.

Purpose of the Study:

  • To propose a complex knowledge base question answering (C-KBQA) framework for intelligent bridge management.
  • To leverage multi-task learning (MTL) and cross-task constraints (CTC) to improve knowledge service.
  • To address the underutilization of domain information in bridge management.

Main Methods:

  • Developed an MTL framework with C-KBQA as the main task and part-of-speech (POS) tagging, topic entity extraction (TEE), and question classification (QC) as auxiliary tasks.
  • Implemented CTC using POS embeddings, entity embeddings, and question-type embeddings to provide cross-task semantic constraints.
  • Utilized template matching for generating query statements and interacting with the knowledge base.

Main Results:

  • The proposed model demonstrated superior performance in TEE and QC compared to mainstream models on bridge management datasets.
  • The C-KBQA framework achieved outstanding performance in answering complex questions about bridge management.
  • The MTL approach effectively mitigated error propagation issues inherent in pipeline models.

Conclusions:

  • The developed C-KBQA framework significantly enhances knowledge service in intelligent bridge management.
  • MTL and CTC are effective strategies for improving the accuracy and efficiency of bridge management systems.
  • The findings suggest a promising direction for utilizing big data in infrastructure management.