# A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks
## 6 Summary  
In this work, we focus on the domain problem of entity alignment. We propose a novel approach (DomainEA) that can be effectively implemented on domain-oriented knowledge graphs. Existing methods are aimed at enhancing cross-lingual links, but when applied to the domain-oriented task of entity alignment, they have difficulty effectively solving the aforementioned problems in the introduction. In response to the issue that domain KGs have few relationships between entities, our approach integrates attribute triples instead of relationship triples to achieve the task of entity alignment. Specifically, we first employed a filtering module consisting of attribute values to exclude noisy entities, and selected a candidate set for each entity to reconstruct the sub-KGs. Then, we employed a SA and an AA to integrate the implicit features of the entities, and to map the heterogeneous KGs onto a unified space. Finally, the alignment module realized the alignment task by calculating the similarity among the entity embeddings. Compared to the five baselines, our approach consistently performed better on real datasets, and comparisons from the ablation experiments also demonstrate the usefulness of the filtering module and SA.  
DomainEA achieves some advancements in the task of domain-oriented entity alignment. However, it also has some limitations to its applications. We will pay more attention to investigating two aspects of our approach in future work:  
Firstly, DomainEA only performed well on two Chinese-recipe datasets, which limits its universality. So we will evaluate DomainEA on multilingual datasets, e.g., English and French, and on domain-oriented datasets, e.g., biomedical science and social media.  
Secondly, we utilized attribute values to capture the features of entities, but entities also have other useful features, such as entity descriptions and entity names. Therefore, we will enhance DomainEA by combining these characteristics in future work.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Hits@1 (\%)** & **Hits@10 (\%)** & **Hits@50 (\%)** & **MRR (\%)** \\ \hline KG\_1K & 57.24 & 95.37 & 97.48 & 74.35 \\ KG\_2K & 56.89 & 95.48 & 97.66 & 73.81 \\ KG\_3K & 60.48 & 95.98 & 98.16 & 75.22 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Entity alignment results with various numbers of hidden layers of GCNs.  
**Author Contributions:** Conceptualization, J.Z.; methodology, J.Z. and Y.X.; software, Y.G., H.L. and Y.L.; validation, P.L.; data curation, C.L., Y.L., H.L. and Y.Z.; writing--original draft preparation, Y.X.; writing--review and editing, Y.L., J.Z. and S.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 62102372, 62072414, and 62006212; The Program for Young Key Teachers of Henan Province under Grant No. 2021 GJS095; The Project of Science and Technology in Henan Province under Grant Nos: 222102210317, 232102210078, 232102240072, 232102210023, and 232102211051; The Project of Collaborative Innovation in Zhengzhou under Grant Nos. 2021 ZDPY0208; The Doctor Scientific Research Fund of Zhengzhou University of Light Industry under Grant No. 2021 BSJ029.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Notably, the dataset has been released on GitHub [[https://github.com/Zhonglinjun/DomainEA](https://github.com/Zhonglinjun/DomainEA)]([https://github.com/Zhonglinjun/DomainEA](https://github.com/Zhonglinjun/DomainEA)) (accessed on 31 December 2022) to facilitate access for scholars who may wish to employ it for research purposes.  
**Acknowledgments:** We sincerely thank the editors and reviewers for their valuable comments in improving this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.