A Knowledge Graph Approach for the Detection of Digital Human Profiles in Big Data
Abstract
Digital transformation is a long process that changes the managing human profiles in both offline and online approaches. This generates the amount of huge data stored in both relational databases and many others like social networks or graph databases. To exploit effectively big data, several measures and algorithms in Picture Fuzzy Graph (PFG) are applied to solve many complex problems in the real-world problems. The paper has presented a novel approach using a knowledge graph to find a human profile including the detection of humans in large data. In the proposed model, digital human profiles are collected from conventional databases combination with social networks in real-time, and a knowledge graph is created to represent complex-relational user attributes of human profile in large datasets. PFG is applied to quantify the degree centrality of nodes. Furthermore, techniques and algorithms on the graph are used to classify the nodes. The experiments in the knowledge graph implemented to illustrate the proposed model. The main contribution in this paper is to identify the right persons among complex-relational groups, locations in real-time based on large datasets on the social networks, relational databases and graph databases.
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References
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[2] Norden E Huang et al., “A data-driven model for predicting the course of COVID-19 epidemic with applications for China, Korea, Italy, Germany, Spain, UK and USA,” medRxiv 2020, DOI: https://doi.org/1-0.11-01/2020.03.28.20046177.
[3] Caijuan Xu et al., “Application of refined management in the prevention and control of coronavirus disease 2019 epidemic in non-isolated areas of a general hospital,” International Journal of Nursing Sciences, 2020, https://doi.org/10.1016/j.ijnss.2020.04.003.
[4] Atina Husnayain et al., “Applications of google search trends for risk communication in infectious disease management: A case study of COVID-19 outbreak in Taiwan,” International Journal of Infectious Diseases, 2020, https://doi.org/10.10-16/j.ijid.2020.03.021.
[5] Lifang Li et al., “Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo,” IEEE Transactions on Computational Social Systems, 2020, Vol. 7, No. 2.
[6] Peter C. Verhoef et al., “Digital transformation: A multidisciplinary reflection and research agenda,” Journal of Business Research, 2019, https://doi.org/10.10-16/j.jbusres.2019.09.022.
[7] Kankanhalli A. et al., “Big data and analytics in healthcare: introduction to the special section,” Information Systems Frontiers, April 2016, Vol. 18, No. 2, pp 233-235.
[8] J. Archenaa et al., “A Survey of Big Data Analytics in Healthcare and Government,” Procedia Computer Science, 2015, Vol. 50, pp 408-413.
[9] Muhammad Intizar Ali et al., “Real-time data analytics and event detection for IoT-enabled communication systems,” Web Semantics: Science, Services and Agents on the World Wide Web, 2017, Vol. 42, pp 19-37.
[10] Muhammad A. et al., “Novel Applications of Intuitionistic Fuzzy Digraphs in Decision Support Systems,” The Scientific World Journal, 2014, http://dx.doi.org/10.11-55/2014/904606.
[11] Cen Zuo et al., “New Concepts of Picture Fuzzy Graphs with Application,” Mathematics, 2019, 7, DOI:10.3390/math7050470.
[12] Olivier Pivert et al., “Expression and efficient evaluation of fuzzy quantified structural queries to fuzzy graph databases,” Fuzzy Sets and Systems, 2019, Vol. 366, pp 3-17.
[13] Binu M. et al., "Connectivity index of a fuzzy graph and its application to human trafficking," Fuzzy Sets and Systems, 2019, Vol. 3601, pp 117-136.
[14] Isnaini Rosyida et al., "Fuzzy chromatic number of union of fuzzy graphs: An algorithm, properties and its application," Fuzzy Sets and Systems, 2020, Vol. 3841, pp 115-131.
[15] Cuong B.C, “Picture fuzzy sets,” Journal Computer Science and Cybernetics, 2014, Vol. 30, No 4, pp 409-420, DOI: https://doi.or-g/10.15625/18139663/30/4/5032.
[16] Cuong B.C. et al., “Picture Fuzzy Sets - a new concept for computational intelligence problems,” Departmental Technical Reports (CS), 2013, Paper 809. http://digitalcommons.ut-ep.edu/cs_techrep/809.
[17] Nuno Guimarães et al., “Twitter as a Source for Time- and Domain-Dependent Sentiment Lexicons,” Social Network Based Big Data Analysis and Applications, Springer International Publishing AG, part of Springer Nature 2018, pp. 1-20.
[18] Prateek D. et al., “Hiding in Plain Sight: The Anatomy of Malicious Pages on Facebook”, Social Network-Based Big Data Analysis and Applications, Springer International Publishing AG, part of Springer Nature 2018, pp. 21-54.
[19] Peter Brauna et al., “Knowledge Discovery from Social Graph Data,” Procedia Computer Science, 2016, Vol. 96, pp. 682-691, https://doi.org/10.10-16/j.procs.2016.08.250.
[20] Phong P.H. et al., “Some compositions of picture fuzzy relations,” In Proceedings of the 7th National Conference on Fundamental and Applied Information Technology Research (FAIR’7), 2014, pp. 19-20.
[21] Cuong B.C., Van Hai P., “Some fuzzy logic operators for picture fuzzy sets,” In Proceedings of the 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE), 2015, pp. 132–137.
[22] Wei G., “Some similarity measures for picture fuzzy sets and their applications,” Iranian Journal Fuzzy Systems, 2018, Vol. 15, pp. 77–89, DOI: 10.22111/IJFS.2018.3579.
[23] Wang R. et al., “Methods for MADM with picture fuzzy muirhead mean operators and their application for evaluating the financial investment risk,” Symmetry 2019, Vol. 11, No. 6, pp. 2-21. DOI:10.3390/sym11010006.
[24] Wei G.W., “Picture fuzzy aggregation operators and their application to multiple attribute decision making,” Journal Intelligent Fuzzy Systems, 2017, Vol. 33, pp. 713-724.
[25] Hai V.P. et al., “A Novel Approach using Context Matching Algorithm and Knowledge Inference for User Identification in Social Networks,” ICMLSC 2020, January 17-19, 2020, pp. 149-153, 0 https://doi.org/10.1145/3380-688.3380708.
[26] Truong X.D. and Hai V.P., “A Proposal of Deep Learning Model for Classifying User Interests on Social Networks” ICMLSC 2020, January 17-19, 2020, pp. 10-14, https://doi.org/10.1145/3380688.3380707.
[27] Hai V.P., Long K.C., “A New Approach of Picture Fuzzy Sets integrated with TOPSIS model for Group Decision Making in Evaluation under Uncertainty,” The 11th International Conference on Internet (ICONI 2019), December 15-18, 2019, accepted.
[28] Hai V.P. et al., “Context Matching with Reasoning and Decision Support using Hedge Algebra with Kansei Evaluation,” SoICT '14: Proceedings of the Fifth Symposium on Information and Communication Technology, December 2014, pp.202-210, https://doi.org/1-0.1145/26-76585.2676598.
[29] LH Son et al., “Picture Inference System: A New Fuzzy Inference System on Picture Fuzzy Set,” Applied Intelligence, 2017, Vol. 46, No. 3, pp. 652-669.
[30] Le Hoang Son, “Generalized Picture Distance Measure and Applications to Picture Fuzzy Clustering,” Applied Soft Computing, 2016, Vol. 46, pp. 284-295.
[31] LH Son, Measuring Analogousness in Picture Fuzzy Sets: From Picture Distance Measures to Picture Association Measures, Fuzzy Optimization and Decision Making, 2017. Vol. 16 (3), pp. 359–378.
[32] A.MohamedIsmayil et al., “Domination in Picture Fuzzy Graphs,” American International Journal of Research in Science, Technology, Engineering & Mathematics, Special Issue of 5th ICOMAC-2019, February 20-21, 2019, pp. 205-210.
[33] M.Laurent et al., A Lex-BFS-based recognition algorithm for Robinsonian matrices, Discrete Applied Mathematics, 2017, Vol. 222, pp. 151-165.
Published
2021-06-29
How to Cite
LONG, Cu Kim et al.
A Knowledge Graph Approach for the Detection of Digital Human Profiles in Big Data.
Journal of Science and Technology: Issue on Information and Communications Technology, [S.l.], v. 19, n. 6.2, p. 6-15, june 2021.
ISSN 1859-1531.
Available at: <http://ict.jst.udn.vn/index.php/jst/article/view/118>. Date accessed: 20 apr. 2024.
doi: https://doi.org/10.31130/ict-ud.2021.118.
Section
Articles
