DualRep: Knowledge Graph Completion by Utilizing Dual Representation of Relational Paths and Tail Node Density Insights
Knowledge graphs (KGs) possess a vital role in enhancing the semantic comprehension of extensive datasets across many fields. It facilitate activities like recommendation systems, semantic searching, and intelligent data mining. However, lacking information can sometimes limit the usefulness of know...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10756593/ |
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| author | Haji Gul Feras Al-Obeidat Adnan Amin Muhammad Wasim Fernando Moreira |
| author_facet | Haji Gul Feras Al-Obeidat Adnan Amin Muhammad Wasim Fernando Moreira |
| author_sort | Haji Gul |
| collection | DOAJ |
| description | Knowledge graphs (KGs) possess a vital role in enhancing the semantic comprehension of extensive datasets across many fields. It facilitate activities like recommendation systems, semantic searching, and intelligent data mining. However, lacking information can sometimes limit the usefulness of knowledge graphs (KGs), as the lack of relationships between entities could severely limit their practical application. Most existing approaches for KG completion primarily concentrate on embedding-based methods or just use relational paths, neglecting the valuable structural information offered by node density. This research presents an approach that effectively combines relational paths and the density features of tail nodes to enhance the accuracy of predicting relationships that are missing in knowledge graphs. Our method combines the sequential relational context represented by paths with the structural prominence indicated by node density, allowing for a dual view on possible entity connections. We validate the effectiveness of our technique by conducting comprehensive tests on many benchmark datasets, revealing substantial enhancements compared to conventional approaches. The Dual-Rep model, which incorporates relational paths and node density features, has continuously shown improved performance across several metrics, such as Mean Reciprocal Rank (MRR), Hit at 1 (Hit@1), and Hit at 3 (Hit@3). The DualRep model achieved a mean reciprocal rank (MRR) of 90.80. Additionally, it achieved a hit rate of 87.39 at rank 1 (Hit@1) and a hit rate of 91.18. |
| format | Article |
| id | doaj-art-c29c595d222d4d4ba37f6a3a9be85392 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c29c595d222d4d4ba37f6a3a9be853922025-08-20T02:33:48ZengIEEEIEEE Access2169-35362024-01-011217956617957810.1109/ACCESS.2024.350173510756593DualRep: Knowledge Graph Completion by Utilizing Dual Representation of Relational Paths and Tail Node Density InsightsHaji Gul0https://orcid.org/0000-0002-2227-6564Feras Al-Obeidat1https://orcid.org/0000-0001-6941-6555Adnan Amin2Muhammad Wasim3https://orcid.org/0000-0003-4154-105XFernando Moreira4https://orcid.org/0000-0002-0816-1445School of Digital Science, Universiti Brunei Darussalam, Bandar Seri Begawan, BruneiCollege of Technological Innovation, Zayed University, Abu Dhabi, United Arab EmiratesSchool of Computer Science and Information Technology, Institute of Management Sciences Peshawar, Peshawar, PakistanDepartment of Computer Science, City University of Science and Information Technology, Peshawar, PakistanREMIT, IJP, Universidade Portucalense, Porto, PortugalKnowledge graphs (KGs) possess a vital role in enhancing the semantic comprehension of extensive datasets across many fields. It facilitate activities like recommendation systems, semantic searching, and intelligent data mining. However, lacking information can sometimes limit the usefulness of knowledge graphs (KGs), as the lack of relationships between entities could severely limit their practical application. Most existing approaches for KG completion primarily concentrate on embedding-based methods or just use relational paths, neglecting the valuable structural information offered by node density. This research presents an approach that effectively combines relational paths and the density features of tail nodes to enhance the accuracy of predicting relationships that are missing in knowledge graphs. Our method combines the sequential relational context represented by paths with the structural prominence indicated by node density, allowing for a dual view on possible entity connections. We validate the effectiveness of our technique by conducting comprehensive tests on many benchmark datasets, revealing substantial enhancements compared to conventional approaches. The Dual-Rep model, which incorporates relational paths and node density features, has continuously shown improved performance across several metrics, such as Mean Reciprocal Rank (MRR), Hit at 1 (Hit@1), and Hit at 3 (Hit@3). The DualRep model achieved a mean reciprocal rank (MRR) of 90.80. Additionally, it achieved a hit rate of 87.39 at rank 1 (Hit@1) and a hit rate of 91.18.https://ieeexplore.ieee.org/document/10756593/Knowledge graph completionrelational pathsnode density analysisgraph structural featuresentity relationship predictiongraph neural networks |
| spellingShingle | Haji Gul Feras Al-Obeidat Adnan Amin Muhammad Wasim Fernando Moreira DualRep: Knowledge Graph Completion by Utilizing Dual Representation of Relational Paths and Tail Node Density Insights IEEE Access Knowledge graph completion relational paths node density analysis graph structural features entity relationship prediction graph neural networks |
| title | DualRep: Knowledge Graph Completion by Utilizing Dual Representation of Relational Paths and Tail Node Density Insights |
| title_full | DualRep: Knowledge Graph Completion by Utilizing Dual Representation of Relational Paths and Tail Node Density Insights |
| title_fullStr | DualRep: Knowledge Graph Completion by Utilizing Dual Representation of Relational Paths and Tail Node Density Insights |
| title_full_unstemmed | DualRep: Knowledge Graph Completion by Utilizing Dual Representation of Relational Paths and Tail Node Density Insights |
| title_short | DualRep: Knowledge Graph Completion by Utilizing Dual Representation of Relational Paths and Tail Node Density Insights |
| title_sort | dualrep knowledge graph completion by utilizing dual representation of relational paths and tail node density insights |
| topic | Knowledge graph completion relational paths node density analysis graph structural features entity relationship prediction graph neural networks |
| url | https://ieeexplore.ieee.org/document/10756593/ |
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