Three‐View Dual‐Space Contrastive Perception Matching Method for Entity Alignment.
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| Title: | Three‐View Dual‐Space Contrastive Perception Matching Method for Entity Alignment. |
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| Authors: | Tan, Qiwen1 (AUTHOR) 2372048@stu.neu.edu.cn, Nie, Tiezheng1 (AUTHOR) nietiezheng@mail.neu.edu.cn, Murray, Richard1 (AUTHOR) rmurray@wiley.com |
| Source: | International Journal of Intelligent Systems. 6/2/2026, Vol. 2026, p1-11. 11p. |
| Subjects: | Hyperbolic spaces, Contrastive learning, Graph neural networks, Graph theory |
| Abstract: | In recent years, entity matching methods based on graph neural networks have significantly improved the ability of entity structure representation through multilayer neighborhood aggregation. However, such methods still suffer from oversmoothing and noise diffusion in local structures, as well as insufficient global topological consistency and limited geometric expression. Existing methods usually rely on local aggregation to obtain structural representations, making it difficult to explicitly model the global topological patterns and hierarchical structures between entities. Especially in heterogeneous or cross‐lingual graphs, traditional Euclidean embedding spaces cannot fully represent complex semantic hierarchies and multiscale geometric relationships. To this end, this paper proposes a Triview Dual‐space Contrastive Perception Matching (TriDCPM) method, which unifies the modeling of local correlations, global topologies, and cross‐graph consistency under a multiview representation and geometric collaborative learning framework. Specifically, TriDCPM constructs a triview framework consisting of local, global, and cross‐graph views and simultaneously learns multiscale entity representations in both Euclidean and hyperbolic geometric spaces. A global structure enhancement module based on singular value decomposition (SVD) is adopted to extract key topological patterns, and a gated residual unit (GRU) is introduced to alleviate noise propagation and oversmoothing. In the local encoding stage, multilayer attention aggregation and a degree‐aware relation fusion mechanism are employed to further enhance heterogeneous neighborhood and relational semantic features. Finally, a dual‐level contrastive consistency learning mechanism is adopted to jointly optimize feature consistency between the local–global levels and the Euclidean–hyperbolic spaces, achieving collaborative perception and discriminative unification of multiview representations. Experimental results on four public benchmark datasets demonstrate that the proposed method significantly outperforms existing structure‐driven entity alignment approaches in terms of Hit@1, MRR, and other metrics, with particularly outstanding performance in relation‐heterogeneous and cross‐lingual scenarios. Further ablation experiments and visualization analysis verify the effectiveness and stability of the proposed method in global structure modeling, noise suppression, and multispace contrastive optimization. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194233768 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Three‐View Dual‐Space Contrastive Perception Matching Method for Entity Alignment. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tan%2C+Qiwen%22">Tan, Qiwen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 2372048@stu.neu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Nie%2C+Tiezheng%22">Nie, Tiezheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> nietiezheng@mail.neu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Murray%2C+Richard%22">Murray, Richard</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rmurray@wiley.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Intelligent+Systems%22">International Journal of Intelligent Systems</searchLink>. 6/2/2026, Vol. 2026, p1-11. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hyperbolic+spaces%22">Hyperbolic spaces</searchLink><br /><searchLink fieldCode="DE" term="%22Contrastive+learning%22">Contrastive learning</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+theory%22">Graph theory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In recent years, entity matching methods based on graph neural networks have significantly improved the ability of entity structure representation through multilayer neighborhood aggregation. However, such methods still suffer from oversmoothing and noise diffusion in local structures, as well as insufficient global topological consistency and limited geometric expression. Existing methods usually rely on local aggregation to obtain structural representations, making it difficult to explicitly model the global topological patterns and hierarchical structures between entities. Especially in heterogeneous or cross‐lingual graphs, traditional Euclidean embedding spaces cannot fully represent complex semantic hierarchies and multiscale geometric relationships. To this end, this paper proposes a Triview Dual‐space Contrastive Perception Matching (TriDCPM) method, which unifies the modeling of local correlations, global topologies, and cross‐graph consistency under a multiview representation and geometric collaborative learning framework. Specifically, TriDCPM constructs a triview framework consisting of local, global, and cross‐graph views and simultaneously learns multiscale entity representations in both Euclidean and hyperbolic geometric spaces. A global structure enhancement module based on singular value decomposition (SVD) is adopted to extract key topological patterns, and a gated residual unit (GRU) is introduced to alleviate noise propagation and oversmoothing. In the local encoding stage, multilayer attention aggregation and a degree‐aware relation fusion mechanism are employed to further enhance heterogeneous neighborhood and relational semantic features. Finally, a dual‐level contrastive consistency learning mechanism is adopted to jointly optimize feature consistency between the local–global levels and the Euclidean–hyperbolic spaces, achieving collaborative perception and discriminative unification of multiview representations. Experimental results on four public benchmark datasets demonstrate that the proposed method significantly outperforms existing structure‐driven entity alignment approaches in terms of Hit@1, MRR, and other metrics, with particularly outstanding performance in relation‐heterogeneous and cross‐lingual scenarios. Further ablation experiments and visualization analysis verify the effectiveness and stability of the proposed method in global structure modeling, noise suppression, and multispace contrastive optimization. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/int/5865814 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: Hyperbolic spaces Type: general – SubjectFull: Contrastive learning Type: general – SubjectFull: Graph neural networks Type: general – SubjectFull: Graph theory Type: general Titles: – TitleFull: Three‐View Dual‐Space Contrastive Perception Matching Method for Entity Alignment. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tan, Qiwen – PersonEntity: Name: NameFull: Nie, Tiezheng – PersonEntity: Name: NameFull: Murray, Richard IsPartOfRelationships: – BibEntity: Dates: – D: 02 M: 06 Text: 6/2/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08848173 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: International Journal of Intelligent Systems Type: main |
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