{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/entity-alignment-for-knowledge-graphs-with","title":"Entity Alignment for Knowledge Graphs with Multi-order Convolutional Networks","arxiv_id":null,"date":"2020-11-17","proceeding":null,"authors":["Tam Thanh Nguyen","Thanh Trung Huynh","Hongzhi Yin","Vinh Van Tong","Darnbi Sakong","Bolong Zheng","Quoc Viet Hung Nguyen"],"abstract":"Knowledge graphs (KGs) have become popular structures for unifying real-world entities by modelling the relationships between them and their attributes. Entity alignment -- the task of identifying corresponding entities across different KGs -- has attracted a great deal of attention in both academia and industry. However, existing alignment techniques often require large amounts of labelled data, are unable to encode multi-modal data simultaneously, and enforce only few consistency constraints. In this paper, we propose an end-to-end, unsupervised entity alignment framework for cross-lingual KGs that fuses different types of information in order to fully exploit the richness of KG data. The model captures the relation-based correlation between entities by using a multi-order graph convolutional neural (GCN) model that is designed to satisfy the consistency constraints, while incorporating the attribute-based correlation via a translation machine. We adopt a late-fusion mechanism to combine all the information together, which allows these approaches to complement each other and thus enhances the final alignment result, and makes the model more robust to consistency violations. Empirical results show that our model is more accurate and orders of magnitude faster than existing baselines. We also demonstrate its sensitivity to hyper-parameters, effort saving in terms of labelling, and the robustness against adversarial conditions.","url_abs":"https://ieeexplore.ieee.org/document/9262038","url_pdf":"https://www.researchgate.net/profile/Tam-Nguyen-83/publication/345848386_Multi-order_Graph_Convolutional_Networks_for_Knowledge_Graph_Alignment/links/5fafb3c645851518fda2f4bc/Multi-order-Graph-Convolutional-Networks-for-Knowledge-Graph-Alignment.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"entity-alignment-for-knowledge-graphs-with","repo_url":"https://github.com/vinhsuhi/EMGCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"entity-alignment","task_name":"Entity Alignment"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/entity-alignment-on-dbp15k-zh-en","task":"Entity Alignment","dataset":"DBP15k zh-en","model":"EMGCN","rank_in_archive_order":7,"of":38,"metrics":{"Hits@1":"0.863"},"uses_additional_data":true},{"leaderboard":"/sota/entity-alignment-on-dbp15k-fr-en","task":"Entity Alignment","dataset":"dbp15k fr-en","model":"EMGCN","rank_in_archive_order":8,"of":14,"metrics":{"Hits@1":"0.940"},"uses_additional_data":true},{"leaderboard":"/sota/entity-alignment-on-dbp15k-ja-en","task":"Entity Alignment","dataset":"dbp15k ja-en","model":"EMGCN","rank_in_archive_order":8,"of":14,"metrics":{"Hits@1":"0.866"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}