A Data Design Pattern for Building and Exploring Semantic Views of Enterprise Knowledge Graphs
Resumo
An Enterprise Knowledge Graph (EKG) is a robust foundation for knowledge management, data integration, and advanced analytics across organizations. It achieves this by offering a semantic view that semantically integrates various data sources within an organization’s data lake. This paper introduces a novel data design pattern (DDP) aimed at constructing and managing the semantic view of an EKG. The proposed DDP logically organizes data into three hierarchical levels, facilitating the maintenance and the versatile exploration of the semantic view in various contexts. Furthermore, this paper details an interactive graphical interface developed to supports context-sensitive navigation of the semantic view, enhancing user interaction and resource utilization.
Palavras-chave:
Knowledge Graphs, Semantic Views, Design Pattern
Referências
Avila, C. and Vidal, V. (2023). Lirb: Um navegador leve baseado em texto para knowledge graphs rdf. In Anais Estendidos do XXXVIII Simpósio Brasileiro de Bancos de Dados, pages 102–107, Porto Alegre, RS, Brasil. SBC.
de Souza, E. M. F., Rossanez, A., dos Reis, J. C., and da Silva Torres, R. (2022). Visualização interativa da evolução de grafos de conhecimento. In Anais do XXXVII Simpósio Brasileiro de Bancos de Dados, pages 343–354. SBC.
Ehrlinger, L. and Wöß, W. (2016). Towards a definition of knowledge graphs. SEMANTiCS (Posters, Demos, SuCCESS), 48(1-4):2.
Galkin, M., Auer, S., and Scerri, S. (2016). Enterprise knowledge graphs: a backbone of linked enterprise data. In 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pages 497–502. IEEE.
Galkin, M., Auer, S., Vidal, M.-E., and Scerri, S. (2017). Enterprise knowledge graphs: A semantic approach for knowledge management in the next generation of enterprise information systems. In International Conference on Enterprise Information Systems, volume 2, pages 88–98. SCITEPRESS.
Grainger, T., AlJadda, K., Korayem, M., and Smith, A. (2016). The semantic knowledge graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain. In 2016 ieee international conference on data science and advanced analytics (dsaa), pages 420–429. IEEE.
Haase, P., Herzig, D. M., Kozlov, A., Nikolov, A., and Trame, J. (2019). metaphactory: A platform for knowledge graph management. Semantic Web, 10(6):1109–1125.
Ruan, T., Xue, L., Wang, H., Hu, F., Zhao, L., and Ding, J. (2016). Building and exploring an enterprise knowledge graph for investment analysis. In The Semantic Web–ISWC 2016: 15th International Semantic Web Conference, Kobe, Japan, October 17–21, 2016, Proceedings, Part II 15, pages 418–436. Springer.
Sellami, S. and Zarour, N. E. (2022). Keyword-based faceted search interface for knowledge graph construction and exploration. International Journal of Web Information Systems, 18(5/6):453–486.
Song, D., Schilder, F., Hertz, S., Saltini, G., Smiley, C., Nivarthi, P., Hazai, O., Landau, D., Zaharkin, M., Zielund, T., et al. (2017). Building and querying an enterprise knowledge graph. IEEE Transactions on Services Computing, 12(3):356–369.
Vidal, V. M., Casanova, M. A., Arruda, N., Roberval, M., Leme, L. P., Lopes, G. R., and Renso, C. (2015). Specification and incremental maintenance of linked data mashup views. In International Conference on Advanced Information Systems Engineering, pages 214–229. Springer.
de Souza, E. M. F., Rossanez, A., dos Reis, J. C., and da Silva Torres, R. (2022). Visualização interativa da evolução de grafos de conhecimento. In Anais do XXXVII Simpósio Brasileiro de Bancos de Dados, pages 343–354. SBC.
Ehrlinger, L. and Wöß, W. (2016). Towards a definition of knowledge graphs. SEMANTiCS (Posters, Demos, SuCCESS), 48(1-4):2.
Galkin, M., Auer, S., and Scerri, S. (2016). Enterprise knowledge graphs: a backbone of linked enterprise data. In 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pages 497–502. IEEE.
Galkin, M., Auer, S., Vidal, M.-E., and Scerri, S. (2017). Enterprise knowledge graphs: A semantic approach for knowledge management in the next generation of enterprise information systems. In International Conference on Enterprise Information Systems, volume 2, pages 88–98. SCITEPRESS.
Grainger, T., AlJadda, K., Korayem, M., and Smith, A. (2016). The semantic knowledge graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain. In 2016 ieee international conference on data science and advanced analytics (dsaa), pages 420–429. IEEE.
Haase, P., Herzig, D. M., Kozlov, A., Nikolov, A., and Trame, J. (2019). metaphactory: A platform for knowledge graph management. Semantic Web, 10(6):1109–1125.
Ruan, T., Xue, L., Wang, H., Hu, F., Zhao, L., and Ding, J. (2016). Building and exploring an enterprise knowledge graph for investment analysis. In The Semantic Web–ISWC 2016: 15th International Semantic Web Conference, Kobe, Japan, October 17–21, 2016, Proceedings, Part II 15, pages 418–436. Springer.
Sellami, S. and Zarour, N. E. (2022). Keyword-based faceted search interface for knowledge graph construction and exploration. International Journal of Web Information Systems, 18(5/6):453–486.
Song, D., Schilder, F., Hertz, S., Saltini, G., Smiley, C., Nivarthi, P., Hazai, O., Landau, D., Zaharkin, M., Zielund, T., et al. (2017). Building and querying an enterprise knowledge graph. IEEE Transactions on Services Computing, 12(3):356–369.
Vidal, V. M., Casanova, M. A., Arruda, N., Roberval, M., Leme, L. P., Lopes, G. R., and Renso, C. (2015). Specification and incremental maintenance of linked data mashup views. In International Conference on Advanced Information Systems Engineering, pages 214–229. Springer.
Publicado
14/10/2024
Como Citar
VIDAL, Vânia M. P.; FREITAS, Renato; ARRUDA, Narciso; CASANOVA, Marco A.; RENSO, Chiara.
A Data Design Pattern for Building and Exploring Semantic Views of Enterprise Knowledge Graphs. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 1-13.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.241024.