A Knowledge Graph Approach for the Detection of Digital Human Profiles in Big Data

  • Cu Kim Long School of Information Communication Technology - Hanoi University of Science and Technology (S0ICT-HUST)
  • Ha Quoc Trung Information Communication Center - MOST, Hanoi, Vietnam
  • Tran Ngoc Thang School of Applied Mathematics and Informatics - HUST, Hanoi, Vietnam
  • Nguyen Tien Dong School of Information Communication Technology - HUST, Hanoi, Vietnam
  • Pham Van Hai School of Information Communication Technology - HUST, Hanoi, Vietnam


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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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: 19 jan. 2022. doi: https://doi.org/10.31130/ict-ud.2021.118.