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Related Concept Videos

Graphs of Equations in Two Variables01:30

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Related Experiment Video

Updated: Jan 11, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Offline and online coupled tensor factorization with knowledge graph.

SeungJoo Lee1, Yong-Chan Park2, U Kang2

  • 1IPAI, Seoul National University, Seoul, Republic of Korea.

Plos One
|November 12, 2025
PubMed
Summary
This summary is machine-generated.

We introduce Knowledge Graph-based Coupled Tensor Factorization (KG-CTF) and its online version (OKG-CTF) to analyze irregular tensors. These methods effectively integrate static knowledge graph information with dynamic temporal data, improving accuracy and speed.

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Area of Science:

  • Data Science
  • Machine Learning
  • Tensor Decomposition

Background:

  • PARAFAC2 decomposition analyzes irregular tensors but often neglects static features like knowledge graphs.
  • Existing methods focus on dynamic temporal features, overlooking time-invariant information crucial for comprehensive analysis.
  • Irregular tensors arise frequently in real-world data due to temporal variations.

Purpose of the Study:

  • To propose novel tensor factorization methods that capture both dynamic and static features in irregular tensors.
  • To integrate knowledge graph information as static features into tensor decomposition.
  • To develop both offline (KG-CTF) and online streaming (OKG-CTF) solutions for coupled tensor factorization.

Main Methods:

  • Developed KG-CTF and OKG-CTF, coupling irregular temporal tensors with knowledge graph tensors via a shared axis.
  • Employed relational regularization to maintain structural dependencies within knowledge graph factor matrices.
  • Utilized momentum-based update strategies to accelerate the convergence of factor matrices.

Main Results:

  • KG-CTF demonstrated up to 1.64× reduction in error rates compared to existing PARAFAC2 methods in offline settings.
  • OKG-CTF achieved up to 5.7× faster running times than existing streaming approaches for new tensor arrivals.
  • Both methods successfully integrated static knowledge graph features with dynamic temporal data.

Conclusions:

  • KG-CTF and OKG-CTF offer effective solutions for analyzing irregular tensors by incorporating knowledge graphs.
  • These coupled tensor factorization methods enhance accuracy and efficiency in both offline and online streaming scenarios.
  • The integration of static and dynamic features provides a more complete understanding of complex temporal data.