Topological simplification methods for large-scale knowledge graphs
*Mohanad AlfirasCorresponding authorpresident@gulfuniversity.edu.bhDepartment of Electrical & Electronic EngineeringCollege of EngineeringGulf UniversitySanad, 26489, Kingdom of BahrainView full profile → , Mohammed Hasan Aldulaimimohammed.hassan@uomus.edu.iqDepartment of Computer Techniques EngineeringCollege of EngineeringAl-Mustaqbal UniversityHillah, Babylon, 51001, IraqView full profile → , Mrunmai Mandar Ranademrunmai.ranade@vupune.ac.inDepartment of Engineering SciencesVishwakarma UniversityPune, Maharashtra, 411048, IndiaView full profile → , Gagan Tiwarigagan.tiwari@niu.edu.inDepartment of Computer SciencesNoida International UniversityGreater Noida, Uttar Pradesh, 203201, IndiaView full profile → , M. Vinithavinitham@maher.ac.inDepartment of Computer ScienceMeenakshi College of Arts and ScienceMeenakshi Academy of Higher Education & Research (Deemed to be University)Chennai, Tamil Nadu, 600078, IndiaView full profile → , Eshniyozov Asadbekasadbek_eshniyozov@tues.uzDepartment of EconomicsTermez University of Economics and ServiceTermez, 190111, UzbekistanView full profile → , Saodatkhon Ibragimovasaodatoy.ibragimova@gmail.comDepartment of PhysicsTashkent Institute of Irrigation and Agricultural Mechanization EngineersNational Research UniversityKori Niyoziy 39, Tashkent, 100000, UzbekistanView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-3007
- Pages:
- 3765–3775
Abstract
Topological simplification of large-scale knowledge graphs seeks to simplify the structure of knowledge graphs with minimal semantic loss and query time. The paper gives a detailed methodology on reducing the graph, incorporating mathematical equations, evaluation criteria, and analysis of the taxonomy. Graph theoretic operators, such as node contraction and edge pruning along with node motif preservation under constraint optimization are used to formalize the problem. A single set of quality standards are suggested: information retention, connectivity preservation, computational efficiency and downstream task accuracy. The simplification techniques identified in the paper are classified into three paradigms which include structural, heuristic and learning-driven. Graph neural networks and embedding-guided pruning are used in machine learning methods to reduce adaptively using centrality methods, clustering methods, and sparsification methods, unlike heuristic methods that do not use graph neural networks. Experimental results indicate that there are trade-offs between compression ratio and semantics fidelity.
Keywords
Subject Classifications
References
[1] A Lobo-Santos and J. Borrego-Díaz, “Enhancing mathematical knowledge graphs with large language models,” Modelling, vol. 6, no. 3, Art. no. 53 (2025), doi: 10.3390/modelling6030053.
[2] D. Zhong, L. Wen, L. Bi, and Y. Liu, “An efficient and automatic simplification method for arbitrary complex networks in mine ventilation,” Mathematics, vol. 12, no. 18, Art. no. 2815 (2024), doi: 10.3390/math12182815.
[3] B. Wang, G. Wu, Q. Zhao, Y. Li, Y. Gao, and J. She, “A topology-preserving simplification method for 3D building models,” ISPRS International Journal of Geo-Information, vol. 10, no. 6, Art. no. 422 (2021), doi: 10.3390/ijgi10060422.
[4] Y. Wang, Y. Liu, K. Ding, S. Wei, X. Zhang, and Y. Zhao, “Dynamic optimization method of knowledge graph entity relations for smart maintenance of cantilever roadheaders,” Mathematics, vol. 11, no. 23, Art. no. 4833 (2023), doi: 10.3390/math11234833.
[5] N. Ghanbari, M. A. Dehghanizadeh, and S. Alikhani, “Some new results on total irregularity of graphs,” Journal of Information and Optimization Sciences, vol. 47, no. 3, pp. 1051–1062 (2026), doi: 10.47974/JIOS-1845.
[6] M. Hofer, D. Obraczka, A. Saeedi, H. Köpcke, and E. Rahm, “Construction of knowledge graphs: Current state and challenges,” Information, vol. 15, no. 8, Art. no. 509 (2024), doi: 10.3390/info15080509.
[7] L. Yang, H. Guo, Y. Dai, and W. Chen, “A method for complex question-answering over knowledge graph,” Applied Sciences, vol. 13, no. 8, Art. no. 5055 (2023), doi: 10.3390/app13085055.
[8] R. A. Mustafa, M. S. Ahmed, and A. M. Ali, “Wiener index for a square of some special graphs,” Journal of Information and Optimization Sciences, vol. 47, no. 3, pp. 1175–1185 (2026), doi: 10.47974/JIOS-2156.
[9] F. Rolli, C. Parretti, R. Barbieri, A. Polidoro, and B. Bindi, “RDF knowledge graphs designed with axiomatic methodology to enhance Industry 4.0,” Machines, vol. 13, no. 1, Art. no. 58 (2025), doi: 10.3390/machines13010058.
[10] M. Yani and A. A. Krisnadhi, “Challenges, techniques, and trends of simple knowledge graph question answering: A survey,” Information, vol. 12, no. 7, Art. no. 271 (2021), doi: 10.3390/info12070271.
[11] X. Guo, H. Qian, F. Wu, and J. Liu, “A method for constructing geographical knowledge graph from multisource data,” Sustainability, vol. 13, no. 19, Art. no. 10602 (2021), doi: 10.3390/su131910602.
[12] P. Prabu, M. Alsulami, D. Alsadie, A. K. J. Saudagar, M. Alkhathami, and R. C. Poonia, “Utilizing behavioral deep learning models to monitor and alert physicians regarding trauma cases,” Journal of Information and Optimization Sciences, vol. 44, no. 5, pp. 983–995 (2023), doi: 10.47974/JIOS-1461.
[13] M. Zamini, H. Reza, and M. Rabiei, “A review of knowledge graph completion,” Information, vol. 13, no. 8, Art. no. 396 (2022), doi: 10.3390/info13080396.
[14] Y. Song, W. Li, G. Dai, and X. Shang, “Advancements in complex knowledge graph question answering: A survey,” Electronics, vol. 12, no. 21, Art. no. 4395 (2023), doi: 10.3390/electronics12214395.
[15] A Lee, “Geometry and topology correction of 3D building models with fragmented and disconnected components,” ISPRS International Journal of Geo-Information, vol. 14, no. 5, Art. no. 198 (2025), doi: 10.3390/ijgi14050198.
