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

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.