# An attribute-enhanced relationship-aware neighborhood matching model with dual attention

## TL;DR

This paper introduces a new model for matching entities across knowledge graphs by improving attribute and relationship modeling with a dual-attention mechanism.

## Contribution

The novel ARNM-DAE2A model uses a dual-attention mechanism to enhance entity alignment by combining structural and attribute information more effectively.

## Key findings

- The proposed model outperforms existing methods on the DBP15K and SRPRS datasets.
- The dual-attention mechanism improves the balance and comprehensiveness of entity representations.
- The relationship-aware neighborhood matching module reduces noise in information aggregation.

## Abstract

The entity alignment task aims to match semantically corresponding entities in different knowledge graphs, which is important for knowledge fusion. Traditional graph-based methods often lose information due to insufficient use of attributes and imperfect relationship modeling, which makes it difficult to capture the deep semantic relationship between entities fully. To improve the effect of entity alignment, we propose a new model named ARNM-DAE2A, which strengthens the information aggregation capability of GCN by introducing a dual-attention mechanism to ensure a more balanced and comprehensive structural representation. The model contains the entity structure embedding module, the attribute structure embedding module, the joint alignment module and the relationship-aware neighborhood matching module. The entity structure embedding module optimizes the structure learning capability of GCN by introducing the pairwise attention mechanism. The attribute structural embedding module utilizes GCN to acquire entity attribute information. The joint alignment module weights and fuses the relationship structure information and attribute information as a comprehensive representation of entities. The relationship-aware neighborhood matching module then corrects the noise in the GCN aggregated information by comparing the neighborhood relationships of entity pairs. Experiments conducted on DBP15K and SRPRS datasets illustrate that the proposed ARNM-DAE2A outperforms baselines.