Optimizing document management and retrieval with multimodal transformers and knowledge graphs
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View abstract on 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.
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.
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