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Enhancing Cross-Lingual Entity Alignment in Knowledge Graphs through Structure Similarity Rearrangement.

Guiyang Liu1,2, Canghong Jin1, Longxiang Shi1

  • 1School of Computer and Computing Science, Hangzhou City University, Hangzhou 310015, China.

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|August 26, 2023
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Summary

This study introduces a new method for cross-lingual entity alignment in knowledge graphs. The SSR model improves matching accuracy by considering local structural features, outperforming existing methods.

Keywords:
cross-lingual entity alignmentknowledge graphstructural similarity rearrangement

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Graph Theory

Background:

  • Cross-lingual entity alignment is vital for knowledge fusion.
  • Current models use embeddings and nearest neighbors but neglect local structure.
  • Poorly represented nodes may not benefit from contextual information.

Purpose of the Study:

  • To propose a novel alignment model, SSR, that incorporates local structural features.
  • To enhance the accuracy of cross-lingual entity alignment in knowledge graphs.
  • To improve upon existing alignment methods by considering node context.

Main Methods:

  • Leveraging graph node embedding algorithms for candidate entity selection.
  • Rearranging candidate entities based on local structural similarity.
  • Evaluating the SSR model on the DBP15k dataset.

Main Results:

  • The SSR model demonstrates improved performance over existing cross-lingual entity alignment approaches.
  • The method effectively utilizes local structural information for better matching accuracy.
  • The approach is compatible with existing alignment models.

Conclusions:

  • The proposed SSR model offers a more accurate and efficient solution for cross-lingual entity alignment.
  • Incorporating local structural similarity is key to improving alignment performance.
  • SSR provides a valuable advancement in knowledge fusion techniques.