{"type":"ScholarlyArticle","title":"Entity Alignment with Reliable Path Reasoning and Relation-aware Heterogeneous Graph Transformer","authors":[{"name":"Weishan Cai","slug":"weishan-cai","given":"Weishan","family":"Cai","affiliation":"School of Computer Science , South China Normal University , China; School of Computer and Information Engineering , Hanshan Normal University , China"},{"name":"Wenjun Ma","slug":"wenjun-ma","given":"Wenjun","family":"Ma","affiliation":"School of Computer Science , South China Normal University , China"},{"name":"Jieyu Zhan","slug":"jieyu-zhan","given":"Jieyu","family":"Zhan","affiliation":"School of Computer Science , South China Normal University , China"},{"name":"Yuncheng Jiang","slug":"yuncheng-jiang","given":"Yuncheng","family":"Jiang","affiliation":"School of Computer Science , South China Normal University , China; School of Artificial Intelligence , South China Normal University , China"}],"abstract":"Entity Alignment (EA) has attracted widespread attention in both academia and industry, which aims to seek entities with same meanings from different Knowledge Graphs (KGs). There are substantial multi-step relation paths between entities in KGs, indicating the semantic relations of entities. However, existing methods rarely consider path information because not all natural paths facilitate for EA judgment. In this paper, we propose a more effective entity alignment framework, RPR-RHGT, which integrates relation and path structure information, as well as the heterogeneous information in KGs. Impressively, an initial reliable path reasoning algorithm is developed to generate the paths favorable for EA task from the relation structures of KGs. This is the first algorithm in the literature to successfully use unrestricted path information. In addition, to efficiently capture heterogeneous features in entity neighborhoods, a relation-aware heterogeneous graph transformer is designed to model the relation and path structures of KGs. Extensive experiments on three well-known datasets show RPR-RHGT significantly outperforms 10 state-of-the-art methods, exceeding the best performing baseline up to 8.62% on Hits@1. We also show its better performance than the baselines on different ratios of training set, and harder datasets.","doi":"10.24963/ijcai.2022/268","url":"https://ijcai.org/proceedings/2022/268","pdf":"https://ijcai.org/proceedings/2022/0268.pdf","markdown":"https://ijcai.org/proceedings/2022/268.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/268","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Data Mining","track":"Main Track","number":268,"pages":{"first":1930,"last":1937},"keywords":["Knowledge Representation and Reasoning: Semantic Web","Data Mining: Knowledge Graphs and Knowledge Base Completion"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986598","length_sec":82,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984741","length_sec":294,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bordes et al., 2013] Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko. 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