Entity class-restricted twin attention-based aggregation for geographic knowledge graph inference
Abstract Geographic knowledge graphs (GeoKGs) describe and simulate geographic phenomena by storing geographic entities with their semantic relations, providing a foundation for many real-world tasks such as urban function identification. However, GeoKGs are often incomplete due to manual constructi...
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-025-02060-y |
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| author | Jinlan Kong Xiaohui He Haichuan Fang Roujia Ji Peng Yang Shuang Li Haofei Li Haonan Sun Mengjia Qiao Panle Li Xijie Cheng Qinglei Zhou Jiandong Shang |
| author_facet | Jinlan Kong Xiaohui He Haichuan Fang Roujia Ji Peng Yang Shuang Li Haofei Li Haonan Sun Mengjia Qiao Panle Li Xijie Cheng Qinglei Zhou Jiandong Shang |
| author_sort | Jinlan Kong |
| collection | DOAJ |
| description | Abstract Geographic knowledge graphs (GeoKGs) describe and simulate geographic phenomena by storing geographic entities with their semantic relations, providing a foundation for many real-world tasks such as urban function identification. However, GeoKGs are often incomplete due to manual construction, leading to inaccurate and/or suboptimal decision-making. This situation requires GeoKG inference (GeoKGI), which involves learning new knowledge by projecting entities and relations into embedding spaces to infer missing facts and complete GeoKGs. Nevertheless, current inference methods mainly focus on modeling universal knowledge graphs (KGs), which cannot be directly transferred into GeoKGs as, unlike KGs, their entities contain specific class information. Inference methods on universal KGs lack a clear manner of modeling such entity class information. Moreover, they often ignore the representation learning of relations, resulting in inadequate modeling of GeoKGs. To address this issue, we propose a novel entity class-restricted twin attention-based aggregation (ECTAA) framework for GeoKGI. Briefly, we first restricted entities by encapsulating the class information within their vector representations to aid the model in better understanding and distinguishing between different entities and their attributes. We then designed a twin aggregation module with attention mechanisms to adaptively incorporate context information into both entity and relation representations. This module simultaneously considers the impact of context on entities and relations, further promoting the capture of GeoKG structural information. Experimental results on five GeoKG datasets validated the effectiveness of ECTAA compared to several state-of-the-art universal KG inference methods in predicting geographic facts. |
| format | Article |
| id | doaj-art-12da82eff77a44bc8d7c6a5413fcebcd |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-12da82eff77a44bc8d7c6a5413fcebcd2025-08-24T12:02:21ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-08-01111011910.1007/s40747-025-02060-yEntity class-restricted twin attention-based aggregation for geographic knowledge graph inferenceJinlan Kong0Xiaohui He1Haichuan Fang2Roujia Ji3Peng Yang4Shuang Li5Haofei Li6Haonan Sun7Mengjia Qiao8Panle Li9Xijie Cheng10Qinglei Zhou11Jiandong Shang12School of Computer Science and Artificial Intelligence, Zhengzhou UniversitySchool of Geoscience and Technology, Zhengzhou UniversitySchool of Computer Science and Artificial Intelligence, Zhengzhou UniversitySchool of Geoscience and Technology, Zhengzhou UniversitySchool of Geoscience and Technology, Zhengzhou UniversitySchool of Geoscience and Technology, Zhengzhou UniversitySchool of Computer Science and Artificial Intelligence, Zhengzhou UniversitySchool of Computer Science and Artificial Intelligence, Zhengzhou UniversitySchool of Geoscience and Technology, Zhengzhou UniversitySchool of Geoscience and Technology, Zhengzhou UniversitySchool of Geoscience and Technology, Zhengzhou UniversitySchool of Computer Science and Artificial Intelligence, Zhengzhou UniversityNational Supercomputing Center in Zhengzhou, Zhengzhou UniversityAbstract Geographic knowledge graphs (GeoKGs) describe and simulate geographic phenomena by storing geographic entities with their semantic relations, providing a foundation for many real-world tasks such as urban function identification. However, GeoKGs are often incomplete due to manual construction, leading to inaccurate and/or suboptimal decision-making. This situation requires GeoKG inference (GeoKGI), which involves learning new knowledge by projecting entities and relations into embedding spaces to infer missing facts and complete GeoKGs. Nevertheless, current inference methods mainly focus on modeling universal knowledge graphs (KGs), which cannot be directly transferred into GeoKGs as, unlike KGs, their entities contain specific class information. Inference methods on universal KGs lack a clear manner of modeling such entity class information. Moreover, they often ignore the representation learning of relations, resulting in inadequate modeling of GeoKGs. To address this issue, we propose a novel entity class-restricted twin attention-based aggregation (ECTAA) framework for GeoKGI. Briefly, we first restricted entities by encapsulating the class information within their vector representations to aid the model in better understanding and distinguishing between different entities and their attributes. We then designed a twin aggregation module with attention mechanisms to adaptively incorporate context information into both entity and relation representations. This module simultaneously considers the impact of context on entities and relations, further promoting the capture of GeoKG structural information. Experimental results on five GeoKG datasets validated the effectiveness of ECTAA compared to several state-of-the-art universal KG inference methods in predicting geographic facts.https://doi.org/10.1007/s40747-025-02060-yGeographic knowledge graphGeographic knowledge graph inferenceEntity classTwin aggregationAttention mechanism |
| spellingShingle | Jinlan Kong Xiaohui He Haichuan Fang Roujia Ji Peng Yang Shuang Li Haofei Li Haonan Sun Mengjia Qiao Panle Li Xijie Cheng Qinglei Zhou Jiandong Shang Entity class-restricted twin attention-based aggregation for geographic knowledge graph inference Complex & Intelligent Systems Geographic knowledge graph Geographic knowledge graph inference Entity class Twin aggregation Attention mechanism |
| title | Entity class-restricted twin attention-based aggregation for geographic knowledge graph inference |
| title_full | Entity class-restricted twin attention-based aggregation for geographic knowledge graph inference |
| title_fullStr | Entity class-restricted twin attention-based aggregation for geographic knowledge graph inference |
| title_full_unstemmed | Entity class-restricted twin attention-based aggregation for geographic knowledge graph inference |
| title_short | Entity class-restricted twin attention-based aggregation for geographic knowledge graph inference |
| title_sort | entity class restricted twin attention based aggregation for geographic knowledge graph inference |
| topic | Geographic knowledge graph Geographic knowledge graph inference Entity class Twin aggregation Attention mechanism |
| url | https://doi.org/10.1007/s40747-025-02060-y |
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