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Leveraging neighborhood distance awareness for entity alignment in temporal knowledge graphs.

Lin Zhu1, Guishun Li1, Luyi Bai1

  • 1Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University (Qinhuangdao), Qinhuangdao 066004, China.

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Summary

This study introduces Tem-DA, a novel method for temporal knowledge graph entity alignment. Tem-DA effectively integrates direct and indirect neighborhood information, improving entity alignment accuracy in temporal knowledge graphs.

Keywords:
Entity alignmentGating mechanismGraph attention networkTemporal knowledge graph

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Knowledge Representation

Background:

  • Entity alignment (EA) is crucial for integrating knowledge graphs.
  • Temporal Knowledge Graphs (TKGs) add a time dimension but face data sparsity.
  • Existing EA methods often overlook neighborhood information in TKGs.

Purpose of the Study:

  • To develop a temporal knowledge graph entity alignment method that leverages neighborhood information.
  • To address data sparsity and redundancy issues in TKGs through improved entity alignment.
  • To enhance the accuracy and adaptability of entity alignment in temporal contexts.

Main Methods:

  • Proposed Tem-DA (Temporal-aware Neighborhood Distance-aware Entity Alignment).
  • Modeled direct and indirect neighbors separately using a distance detection module.
  • Implemented a gating mechanism for adaptive feature fusion and cross-entropy loss with regularization.
  • Captured and encoded temporal information, including estimating temporal characteristics for sparse entities.

Main Results:

  • Tem-DA demonstrated superior performance compared to baseline methods on two monolingual TKG datasets.
  • The method effectively utilized both direct and indirect neighborhood information.
  • Adaptive fusion and temporal information encoding contributed to improved alignment accuracy.

Conclusions:

  • Tem-DA offers a more flexible and adaptive approach to entity alignment in temporal knowledge graphs.
  • The method successfully addresses limitations of existing EA techniques by incorporating neighborhood awareness and temporal dynamics.
  • Tem-DA shows significant potential for enhancing knowledge graph integration in time-aware scenarios.