[1] Thayyib PV et al (2023) State-of-the-art of artificial intelligence and big data analytics reviews in five different domains: a bibliometric summary. Sustainability 15(5):4026

[2] Janssen M, Van Der Voort H, Wahyudi A (2017) Factors influencing big data decision-making quality. J Bus Res 70:338–345

[3] Tang M, Liao H (2021) From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey, Omega 100:102141

[4] Li C, Chen Y, Shang Y (2022) A review of industrial big data for decision making in intelligent manufacturing. Eng Sci Technol Int J 29:101021

[5] Deepa N et al (2022) A survey on blockchain for big data: Approaches, opportunities, and future directions. Futur Gener Comput Syst 131:209–226

[6] Palanisamy V, Thirunavukarasu R (2019) Implications of big data analytics in developing healthcare frameworks - A review. J King Saud Univ Comp Inf Sci 31(4):415–425

[7] Pal G, Atkinson K, Li G (2020) Managing heterogeneous data on a big data platform: a multi-criteria decision-making model for data-intensive science. 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), IEEE

[8] Azeem, MF (Ed) (2012) Fuzzy inference system: theory and applications. BoD–Books on Demand

[9] Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23.3:665–685

[10] Man JY, Chen Z, Dick S (2007) Towards inductive learning of complex fuzzy inference systems. In: Proc Annu Meeting North America Fuzzy Inf Process Soc, pp 415–420

[11] Selvachandran G (2019) New design of Mamdani complex fuzzy inference system for multi-attribute decision-making problems. IEEE Trans Fuzzy Syst, early access, Dec. 20

[12] Ji S et al (2021) A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Trans Neural Netw Learn Syst 33(2):494–514

[13] Lan LTH et al (2020) A new complex fuzzy inference system with fuzzy knowledge graph and extensions in decision making. IEEE Access 8:164899–164921

[14] Long CK et al (2022) A novel fuzzy knowledge graph pairs approach in decision making. Multimed Tools Appl 81(18):26505–26534

[15] Pham HV et al (2023) A Fuzzy Knowledge Graph Pairs-Based Application for Classification in Decision Making: Case Study of Preeclampsia Signs, Information 14.2

[16] Chuan PM et al (2022) Chronic kidney disease diagnosis using Fuzzy Knowledge Graph Pairs-based inference in the extreme case, RICE

[17] Long CK et al (2023) A novel Q-learning-based FKG-Pairs approach for extreme cases in decision making. Eng Appl Artif Intell 120

[18] Zheng T, Wang L (2021) Large graph sampling algorithm for frequent subgraph mining. IEEE Access 9:88970–88980

[19] Li R-H et al (2015) On random walk based graph sampling. 2015 IEEE 31st international conference on data engineering, IEEE

[20] Xu X, Lee C-H (2014) A general framework of hybrid graph sampling for complex network analysis. IEEE INFOCOM 2014-IEEE Conference on Computer Communications, IEEE

[21] Yousuf MI, Kim S (2020) Guided sampling for large graphs. Data Min Knowl Disc 34(4):905–948

[22] Ahmed NK, Neville J, Kompella R (2013) Network sampling: From static to streaming graphs. ACM Trans Knowl Discov Data (TKDD) 8(2):1–56

[23] Papagelis M, Das G, Koudas N (2011) Sampling online social networks. IEEE Trans Knowl Data Eng 25(3):662–676

[24] Shaik T et al (2023) A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom. Inf Fusion

[25] Muhammad G et al (2021) A comprehensive survey on multimodal medical signals fusion for smart healthcare systems. Inf Fusion 76:355–375

[26] Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 1:116–132

[27] Yang L-H et al (2023) Belief rule-based expert system with multilayer tree structure for complex problems modeling. Expert Syst Appl 217

[28] Geramian A, Abraham A, Nozari MA (2019) Fuzzy logic-based FMEA robust design: a quantitative approach for robustness against groupthink in group/team decision-making. Int J Prod Res 57(5):1331–1344

[29] Cao Y et al (2021) A new approximate belief rule base expert system for complex system modeling. Decision Support Syst 150

[30] Han F et al (2023) Multimodal fuzzy granular representation and classification. Appl Intell 53(23):29433–29447

[31] Huong TT et al (2023) A novel transfer learning model on complex fuzzy inference system. J Intell Fuzzy Syst 44(3):3733–3750

[32] Hu P, Lau WC (2013) A survey and taxonomy of graph sampling. arXiv preprint, arXiv:1308.5865

[33] Leskovec J, Faloutsos C (2006) Sampling from large graphs. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

[34] Stumpf MPH, Wiuf C, May RM (2005) Subnets of scale-free networks are not scale-free: sampling properties of networks. Proc Natl Acad Sci 102(12):4221–4224

[35] Krishnamurthy V et al (2003) Sampling Internet topologies: How small can we go? International Conference on Internet Computing

[36] Ahmed N, Neville J, Kompella RR (2011) Network sampling via edge-based node selection with graph induction

[37] Doerr C, Blenn N (2013) Metric convergence in social network sampling. Proceedings of the 5th ACM workshop on HotPlanet

[38] Goodman LA (1960) Snowball Sampling: The Annals of Mathematical Statistics

[39] Leskovec J, Kleinberg J, Faloutsos C (2005) Graphs over time: densification laws, shrinking diameters and possible explanations. Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

[40] Rezvanian A, Rahmati M, Meybodi MR (2014) Sampling from complex networks using distributed learning automata. Physica A 396:224–234

[41] Stutzbach D et al (2006) On unbiased sampling for unstructured peer-to-peer networks. In: Proceedings of the 6th ACM SIGCOMM conference on Internet measurement, pp 27–40

[42] Gao Q et al (2014) An improved sampling method of complex network. Int J Modern Phys C

[43] Jarnac, L, Chabot Y, Couceiro M (2024) Uncertainty Management in the Construction of Knowledge Graphs: a Survey. arXiv preprint arXiv:2405.16929

[44] Yang P et al (2024) LMKG: A large-scale and multi-source medical knowledge graph for intelligent medicine applications. Knowl-Based Syst 284:111323

[45] Salih AB, Alotaibi S (2024) A systematic literature review of knowledge graph construction and application in education. [J], Heliyon 10.3:e25383–e25383

[46] Ning, Y et al (2024) UUKG: unified urban knowledge graph dataset for urban spatiotemporal prediction. Adv Neural Inf Process Syst 36

[47] Kosasih EE et al (2024) Towards knowledge graph reasoning for supply chain risk management using graph neural networks. Int J Prod Res 62(15):5596–5612

[48] Venugopal V, Olivetti E (2024) MatKG: An autonomously generated knowledge graph in Material Science. Scientific Data 11(1):217

[49] Chen Z et al (2024) Knowledge graphs meet multi-modal learning: A comprehensive survey. arXiv preprint arXiv:2402.05391

[50] Pan S et al (2024) Unifying large language models and knowledge graphs: A roadmap. IEEE Trans Knowl Data Eng

[51] Center for Machine Learning and Intelligent Systems UCI machine learning repository. https://archive.ics.uci.edu/dataset/45/heart+disease