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SocioPedia: Visualizing Social Knowledge over Time

Published:07 June 2023Publication History

ABSTRACT

In this paper, we introduce SocioPedia, which is a real-time automatic system for efficiently visualizing and analyzing the variations, characteristics, and evolutions of social knowledge following the change of time. SocioPedia has been developed to provide a full knowledge graph life cycle and combined the temporal information into each processed knowledge. To benefit different classes of users, SocioPedia provides a user-friendly and intuitive environment with different visualization types including static knowledge visualization, timeline knowledge visualization, timeline characteristic visualization, and dynamic timeline visualization.

References

  1. Youcef Abdelsadek, Kamel Chelghoum, Francine Herrmann, Imed Kacem, and Benoît Otjacques. 2018. Community extraction and visualization in social networks applied to Twitter. Information Sciences 424 (2018), 204--223.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Zhe Chen, Yuehan Wang, Bin Zhao, Jing Cheng, Xin Zhao, and Zongtao Duan. 2020. Knowledge graph completion: A review. Ieee Access 8 (2020), 192435--192456.Google ScholarGoogle ScholarCross RefCross Ref
  3. Andrea Cimmino and Raúl García-Castro. 2022. Helio: a framework for implementing the life cycle of knowledge graphs. Semantic Web (2022).Google ScholarGoogle Scholar
  4. Silviu Cucerzan and Avirup Sil. 2013. The MSR Systems for Entity Linking and Temporal Slot Filling at TAC 2013.. In TAC.Google ScholarGoogle Scholar
  5. Dieter Fensel, U Simsek, Kevin Angele, Elwin Huaman, Elias Kärle, Oleksandra Panasiuk, Ioan Toma, Jürgen Umbrich, and Alexander Wahler. 2020. Knowledge graphs. Springer.Google ScholarGoogle Scholar
  6. G David Forney. 1973. The viterbi algorithm. Proc. IEEE 61, 3 (1973), 268--278.Google ScholarGoogle ScholarCross RefCross Ref
  7. Guillermo Garrido, Anselmo Penas, and Bernardo Cabaleiro. 2013. UNED Slot Filling and Temporal Slot Filling systems at TAC KBP 2013: System description.. In TAC.Google ScholarGoogle Scholar
  8. Jose Manuel Gomez-Perez, Jeff Z Pan, Guido Vetere, and Honghan Wu. 2017. Enterprise knowledge graph: An introduction. In Exploiting linked data and knowledge graphs in large organisations. Springer, 1--14.Google ScholarGoogle Scholar
  9. Simon Gottschalk and Elena Demidova. 2018. EventKG: a multilingual event-centric temporal knowledge graph. In European Semantic Web Conference. Springer, 272--287.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Damien Graux, Fabrizio Orlandi, Tanmay Kaushik, David Kavanagh, Hailing Jiang, Brian Bredican, Matthew Grouse, and Dáithí Geary. 2021. Timelining Knowledge Graphs in the Browser. In VOILA! 2021-6th International Workshop on the Visualization and Interaction for Ontologies and Linked Data.Google ScholarGoogle Scholar
  11. Jon Kleinberg. 2003. Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery 7, 4 (2003), 373--397.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Yu Liu, Wen Hua, and Xiaofang Zhou. 2021. Temporal knowledge extraction from large-scale text corpus. World Wide Web 24, 1 (2021), 135--156.Google ScholarGoogle ScholarCross RefCross Ref
  13. Hardik Patel, Pavlos Paraskevopoulos, and Matthias Renz. 2018. GeoTeGra: a system for the creation of knowledge graph based on social network data with geographical and temporal information. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 617--620.Google ScholarGoogle ScholarCross RefCross Ref
  14. Partha Pratim Talukdar, Derry Wijaya, and Tom Mitchell. 2012. Coupled temporal scoping of relational facts. In Proceedings of the fifth ACM international conference on Web search and data mining. 73--82.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Xueying Wang, Haiqiao Zhang, Qi Li, Yiyu Shi, and Meng Jiang. 2019. A novel unsupervised approach for precise temporal slot filling from incomplete and noisy temporal contexts. In The World Wide Web Conference. 3328--3334.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Yafang Wang, Bin Yang, Lizhen Qu, Marc Spaniol, and Gerhard Weikum. 2011. Harvesting facts from textual web sources by constrained label propagation. In Proceedings of the 20th ACM international conference on Information and knowledge management. 837--846.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Yafang Wang, Mingjie Zhu, Lizhen Qu, Marc Spaniol, and Gerhard Weikum. 2010. Timely yago: harvesting, querying, and visualizing temporal knowledge from wikipedia. In Proceedings of the 13th international conference on extending database technology. 697--700.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Ling Wu, Qiong Peng, Michael Lemke, Tao Hu, and Xi Gong. 2022. Spatial social network research: a bibliometric analysis. Computational Urban Science 2, 1 (2022), 1--13.Google ScholarGoogle ScholarCross RefCross Ref

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    • Published in

      cover image ACM Conferences
      SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
      March 2023
      1932 pages
      ISBN:9781450395175
      DOI:10.1145/3555776

      Copyright © 2023 ACM

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      Publication History

      • Published: 7 June 2023

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