Interactive Visualization of Knowledge Graphs Evolution
Abstract
Knowledge graphs are essential artifacts employed to represent structured data in several application domains. Temporal knowledge graphs express a set of knowledge graphs that change over time. A relevant need for such structures is how one makes use of them to analyze knowledge and make decisions. This investigation studies the conception and development of a software tool, for interactively visualizing the evolution of knowledge graphs. We implemented the TKGEvolViewer tool, which enables graphically exploring temporal knowledge graphs, by means of measurements coded within their structures. Our results enable users to conduct visual analyses over temporal knowledge graphs, filtering available information through a graphic modal.
Keywords:
Knowledge Graphs, Temporal Knowledge Graphs, Data Visualization
References
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Beck, F., Burch, M., Diehl, S., and Weiskopf, D. (2017). A Taxonomy and Survey of Dynamic Graph Visualization. Computer Graphics Forum, 36(1):133-159.
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Pernischová, R., Dell’Aglio, D., Horridge, M., Baumgartner, M., and Bernstein, A. (2019). Toward Predicting Impact of Changes in Evolving Knowledge Graphs. In SEMWEB.
Pomp, A., Kraus, V., Poth, L., and Meisen, T. (2020). Semantic Concept Recommendation for Continuously Evolving Knowledge Graphs. In Filipe, J., Smialek, M., Brodsky, A., and Hammoudi, S., editors, Enterprise Information Systems, pages 361-385, Cham. Springer International Publishing.
Rospocher, M., van Erp, M., Vossen, P., Fokkens, A., Aldabe, I., Rigau, G., Soroa, A., Ploeger, T., and Bogaard, T. (2016). Building Event-Centric Knowledge Graphs from News. Journal of Web Semantics, 37-38:132-151.
Rossanez, A. and Dos Reis, J. C. (2019). Generating Knowledge Graphs from Scientific Literature of Degenerative Diseases. In Proceedings of the 4th International Workshop on Semantics-Powered Data Mining and Analytics, SEPDA 2019, pages 12-23.
Rossanez, A., Dos Reis, J. C., Torres, R. d. S., and De Ribaupierre, H. (2020). KGen: A Knowledge Graph Generator from Biomedical Scientific Literature. BMC Medical Informatics and Decision Making, 20(S4).
Rossanez, A., Dos Reis, J. C., and Torres, R. d. S. (2020). Representing Scientific Literature Evolution via Temporal Knowledge Graphs. In Proceedings of the 6th Workshop on Managing the Evolution and Preservation of the Data Web, MEPDaW 2020, pages 33-42.
Singh, K., Lytra, I., Radhakrishna, A. S., Shekarpour, S., Vidal, M.-E., and Lehmann, J. (2020). No one is Perfect: Analysing the Performance of Question Answering Components over the dbpedia Knowledge Graph. Journal of Web Semantics, 65:100594.
Tosi, M. D. L. and dos Reis, J. C. (2022). Understanding the Evolution of a Scientific Field by Clustering and Visualizing Knowledge Graphs. Journal of Information Science, 48(1):71-89.
Rodrigues U, Moura D. C., Cunha F. A., and Torres, R. (2019). Graph Visual Rhythms in Temporal Network Analyses. Graphical Models, 103:101021.
Beck, F., Burch, M., Diehl, S., and Weiskopf, D. (2017). A Taxonomy and Survey of Dynamic Graph Visualization. Computer Graphics Forum, 36(1):133-159.
Chen, F., Doan, A., Yang, J., and Ramakrishnan, R. (2008). Efficient Information Extraction over Evolving Text Data. In 2008 IEEE 24th International Conference on Data Engineering, pages 943-952.
Hogan, A., Blomqvist, E., Cochez, M., D’amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A.-C. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., and Zimmermann, A. (2021). Knowledge Graphs. ACM Comput. Surv., 54(4).
Kim, H. (2017). Towards a Sales Assistant Using a Product Knowledge Graph. Journal of Web Semantics, 46-47:14-19.
Liu, J., Zhang, Q., Fu, L., Wang, X., and Lu, S. (2019). Evolving Knowledge Graphs. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pages 2260-2268.
Pernischová, R., Dell’Aglio, D., Horridge, M., Baumgartner, M., and Bernstein, A. (2019). Toward Predicting Impact of Changes in Evolving Knowledge Graphs. In SEMWEB.
Pomp, A., Kraus, V., Poth, L., and Meisen, T. (2020). Semantic Concept Recommendation for Continuously Evolving Knowledge Graphs. In Filipe, J., Smialek, M., Brodsky, A., and Hammoudi, S., editors, Enterprise Information Systems, pages 361-385, Cham. Springer International Publishing.
Rospocher, M., van Erp, M., Vossen, P., Fokkens, A., Aldabe, I., Rigau, G., Soroa, A., Ploeger, T., and Bogaard, T. (2016). Building Event-Centric Knowledge Graphs from News. Journal of Web Semantics, 37-38:132-151.
Rossanez, A. and Dos Reis, J. C. (2019). Generating Knowledge Graphs from Scientific Literature of Degenerative Diseases. In Proceedings of the 4th International Workshop on Semantics-Powered Data Mining and Analytics, SEPDA 2019, pages 12-23.
Rossanez, A., Dos Reis, J. C., Torres, R. d. S., and De Ribaupierre, H. (2020). KGen: A Knowledge Graph Generator from Biomedical Scientific Literature. BMC Medical Informatics and Decision Making, 20(S4).
Rossanez, A., Dos Reis, J. C., and Torres, R. d. S. (2020). Representing Scientific Literature Evolution via Temporal Knowledge Graphs. In Proceedings of the 6th Workshop on Managing the Evolution and Preservation of the Data Web, MEPDaW 2020, pages 33-42.
Singh, K., Lytra, I., Radhakrishna, A. S., Shekarpour, S., Vidal, M.-E., and Lehmann, J. (2020). No one is Perfect: Analysing the Performance of Question Answering Components over the dbpedia Knowledge Graph. Journal of Web Semantics, 65:100594.
Tosi, M. D. L. and dos Reis, J. C. (2022). Understanding the Evolution of a Scientific Field by Clustering and Visualizing Knowledge Graphs. Journal of Information Science, 48(1):71-89.
Rodrigues U, Moura D. C., Cunha F. A., and Torres, R. (2019). Graph Visual Rhythms in Temporal Network Analyses. Graphical Models, 103:101021.
Published
2022-09-19
How to Cite
DE SOUZA, Eduardo Moreira Freitas; ROSSANEZ, Anderson; DOS REIS, Julio Cesar; TORRES, Ricardo da Silva.
Interactive Visualization of Knowledge Graphs Evolution. In: BRAZILIAN SYMPOSIUM ON DATABASES (SBBD), 37. , 2022, Búzios.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 343-354.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2022.224301.
