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Related Experiment Video

Updated: Jun 15, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Optimizing document management and retrieval with multimodal transformers and knowledge graphs.

Yali Chen1, Bin Hu2, Yajuan Liu3

  • 1Archives of Nantong University, Nantong, China.

Plos One
|June 11, 2025
PubMed
Summary
This summary is machine-generated.

The MDKG-RL model enhances multimodal archival retrieval by integrating knowledge graphs and deep reinforcement learning. It significantly improves accuracy and efficiency, outperforming baseline models for better information management.

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Area of Science:

  • Computer Science
  • Information Science

Background:

  • Multimodal archival data is growing rapidly, posing challenges for traditional retrieval methods.
  • Existing methods struggle with heterogeneous data, leading to low accuracy and efficiency.

Purpose of the Study:

  • To develop an advanced model for efficient and accurate multimodal archival data retrieval.
  • To address the limitations of traditional retrieval techniques in handling complex data.

Main Methods:

  • Proposed the MDKG-RL model, integrating knowledge graph reasoning, deep reinforcement learning, and multimodal Transformers.
  • Utilized ICDAR 2023 and AIDA Corpus datasets for experimental validation.

Main Results:

  • Achieved a Mean Reciprocal Rank (MRR) of 0.85 and Normalized Discounted Cumulative Gain (NDCG) of 0.88.
  • Demonstrated significant improvements over baseline models: 13.3% MRR increase, 12.8% NDCG increase, and 38.2% response time reduction.
  • Entity linking accuracy reached 92.4%.

Conclusions:

  • The MDKG-RL model offers an effective solution for multimodal archival retrieval, enhancing performance.
  • Future work includes expanding data, optimizing strategies, and exploring new application scenarios.