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Approximate Query on Temporal Knowledge Graphs via Two-Level Embeddings.

Jiaxuan Liu1, Xinyi Duan2, Luyi Bai2

  • 1Sydney Smart Technology College, Northeastern University, Qinhuangdao 066004, China.

Entropy (Basel, Switzerland)
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for approximate querying in dynamic temporal knowledge graphs (TKGs). The Two-Level Approximate Query (TLAQ) method enhances graph embeddings for more accurate results.

Keywords:
approximate querytemporal knowledge graphtwo-level embeddings

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • Approximate querying is crucial for knowledge graphs (KGs) in real-world applications.
  • Existing methods primarily address static KGs, neglecting their dynamic nature.
  • Temporal knowledge graphs (TKGs) incorporate time-evolving information, posing unique challenges.

Purpose of the Study:

  • To develop an effective method for approximate querying in temporal knowledge graphs (TKGs).
  • To address the limitations of existing methods in handling dynamic and time-aware data.
  • To improve the accuracy and relevance of query results from evolving KGs.

Main Methods:

  • Proposed a Two-Level Approximate Query (TLAQ) method for TKGs.
  • Enhanced graph convolutional network (GCN) eigenmatrix for improved vertex and graph embeddings.
  • Introduced relational reliability and attributive confidence at the vertex level.
  • Unified timestamp encoding at the graph level to strengthen the embedding model.

Main Results:

  • The TLAQ method demonstrated effectiveness in handling approximate queries on TKGs.
  • The proposed approach showed improved performance compared to existing methods in experimental evaluations.
  • The two-level embedding strategy successfully captured temporal dynamics and relationships.

Conclusions:

  • The TLAQ method offers a robust solution for approximate querying in dynamic TKGs.
  • Enhancing graph embeddings with temporal information is key to improving query accuracy.
  • This work contributes to more efficient and effective information retrieval from evolving knowledge bases.