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Uncertainty modeling for inductive knowledge graph embedding.

Chao Liu1, Sam Kwong2, Xizhao Wang3

  • 1College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.

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Summary
This summary is machine-generated.

This study introduces EDSU, a novel inductive knowledge graph embedding model that addresses distribution shifts in entity features. EDSU effectively reconstructs mean and variance to improve representation learning for evolving knowledge graphs.

Keywords:
Distribution shiftEmbedding spaceGraph representation learningInductive link predictionReconstruction

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Machine Learning

Background:

  • Knowledge Graphs (KGs) undergo continuous refinement, leading to emergent entities and evolving existing ones.
  • This evolution causes distribution shifts in entity features within the embedding space, impacting graph representation learning.
  • Existing inductive KG embedding methods often overlook the detrimental effects of these distribution shifts.

Purpose of the Study:

  • To develop a novel inductive knowledge graph embedding model, EDSU, capable of handling distribution shifts in entity feature distributions.
  • To alleviate the deviation of data information caused by distribution shifts by integrating intra-entity and inter-entity characteristics.
  • To provide a framework for understanding distribution shift handling as a form of distributional data augmentation.

Main Methods:

  • Developed the EDSU model utilizing mean and variance reconstruction techniques.
  • Assumed entity embeddings follow a multivariate Gaussian distribution.
  • Combined distribution characteristics of entity embedding components with neighborhood structure information to mitigate data deviation.

Main Results:

  • The EDSU model demonstrated superior performance compared to state-of-the-art baseline models.
  • Experiments confirmed the effectiveness of EDSU in inductive link prediction tasks.
  • The approach successfully alleviated data information deviation between intra-entity and inter-entity features.

Conclusions:

  • EDSU effectively addresses the challenge of distribution shifts in inductive knowledge graph embedding.
  • The mean and variance reconstruction method provides a robust approach to handling evolving entity features.
  • The findings suggest a new direction for improving representation learning in dynamic Knowledge Graphs.