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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Temporal multi-modal knowledge graph generation for link prediction.

Yuandi Li1, Hui Ji1, Fei Yu2

  • 1Jiangsu University, Zhenjiang, 212013, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Temporal Multi-Modal Knowledge Graph Generation (TMMKGG) to automatically build complex dynamic knowledge graphs. A new Temporal Multi-Modal Link Prediction (TMMLP) method is also proposed, outperforming existing techniques.

Keywords:
Knowledge graph generationLink predictionMultimodal knowledge graphTemporal knowledge graphs

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Knowledge Representation

Background:

  • Temporal Multi-Modal Knowledge Graphs (TMMKGs) integrate Temporal Knowledge Graphs (TKGs) and Multi-Modal Knowledge Graphs (MMKGs).
  • TMMKGs are crucial for modeling dynamic real-world phenomena from heterogeneous, time-series data sources.
  • Applications include e-commerce, scene recording, and intelligent transportation systems.

Purpose of the Study:

  • To propose an automated Temporal Multi-Modal Knowledge Graph Generation (TMMKGG) method to reduce construction costs.
  • To introduce a dynamic Visual-Audio-Language Multimodal (VALM) dataset for structured knowledge extraction.
  • To develop a Temporal Multi-Modal Link Prediction (TMMLP) method addressing unique TMMKG characteristics.

Main Methods:

  • Developed TMMKGG for automatic TMMKG construction, focusing on temporal dynamics and cross-modal integration.
  • Created the VALM dataset for temporal multimodal perception data.
  • Proposed TMMLP based on observed entity-edge disparities in TMMKGs.

Main Results:

  • TMMKGG effectively generates TMMKGs, validated against state-of-the-art dynamic graph generation methods on the VALM dataset.
  • The VALM dataset supports structured knowledge extraction from temporal multimodal data.
  • TMMLP demonstrated superior performance in link prediction tasks compared to existing methods.

Conclusions:

  • TMMKGG offers an efficient approach to constructing TMMKGs, reducing manual effort.
  • The VALM dataset facilitates research in temporal multimodal knowledge representation.
  • TMMLP effectively addresses the unique challenges of link prediction in TMMKGs.