Método para rotular Ligações Semânticas na Web de Dados
Resumo
A Web Semântica, com suas linguagens e padrões, fornece uma estrutura comum que permite que os dados sejam compartilhados e reutilizados. Uma forma de aumentar o conhecimento sobre esses dados é realizando novas interligações entre datasets. No entanto, a maioria das abordagens de interligação apresentam ligações do tipo "Same As" ou "Related To". Este último tipo deixa vago o significado da relação encontrada. Este trabalho apresenta um método para rotular esse tipo de relação entre datasets, por meio da utilização de ontologias e vocabulários controlados. Além do método, apresenta também a aplicação WEB denominada PLAIN que o implementa, e um estudo de caso demonstrando a viabilidade e funcionalidade da abordagem proposta.
Palavras-chave:
Web Semântica, Ontologias, Enriquecimento de datasets
Referências
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Assaf, A., Troncy, R., and Senart, A. (2015). What’s up lod cloud? In ESWC, pages 247–254. Springer.
Athanasiou, S., Giannopoulos, G., Graux, D., Karagiannakis, N., Lehmann, J., Ngomo, A.-C. N., Patroumpas, K., Sherif, M. A., and Skoutas, D. (2019). Big poi data integration with linked data technologies. In EDBT, pages 477–488.
Berners-Lee, T., Hendler, J., and Lassila, O. (2001). The semantic web. Scientific american, 284(5):34–43.
Bizer, C., Volz, J., Kobilarov, G., and Gaedke, M. (2009). Silk-a link discovery framework for the web of data. In 18th International World Wide Web Conference, volume 122.
Collovini, S., Gonc¸alves, P. N., Cavalheiro, G., Santos, J., and Vieira, R. (2020). Relation extraction for competitive intelligence. In PROPOR, pages 249–258. Springer.
de Oliveira, F. A., Costa, R., Goldschmidt, R., and Cavalcanti, M. (2019). Multirelation association rule mining on datasets of the web of data. In SBSI 2019, page 61. ACM.
de Oliveira, F. A., Martins, Y. C., Rocha, D. S., de Siqueira, M. F., da Silva, L. A. E., Costa, R. L., Goldschmidt, R. R., and Cavalcanti, M. C. (2017). Jabotg: Extending the herbarium dataset frontiers. In MTSR 2017, pages 45–53.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint.
Han, X., Gao, T., Yao, Y., Ye, D., Liu, Z., and Sun, M. (2019). Opennre: An open and extensible toolkit for neural relation extraction. In EMNLP-IJCNLP, pages 169–174.
Hebeler, J., Fisher, M., Blace, R., and Perez-Lopez, A. (2011). Semantic web programming. John Wiley & Sons.
Horrocks, I. (2008). Ontologies and the semantic web. Comm of the ACM, 51(12):58–67.
Nentwig, M., Hartung, M., Ngonga Ngomo, A.-C., and Rahm, E. (2017). A survey of current link discovery frameworks. Semantic Web, 8(3):419–436.
Ngomo, A.-C. N. and Auer, S. (2011). Limes—a time-efficient approach for large-scale link discovery on the web of data. In IJCAI-11.
Paris, P.-H. (2018). Assessing the quality of owl: sameas links. In ESWC, pages 304–313. Springer.
Ramezani, R., Saraee, M., Nematbakhsh, M. A., et al. (2014). Mrar: mining multi-relation association rules. Journal of Computing and Security, 1(2):133–158.
Schmachtenberg, M., Bizer, C., and Paulheim, H. (2014). Adoption of the linked data best practices in different topical domains. In ISWC, pages 245–260. Springer.
Sherif, M. A., Ngomo, A.-C. N., and Lehmann, J. (2015). Automating rdf dataset transformation and enrichment. In ESWC, pages 371–387. Springer.
Studer, R., Benjamins, V. R., and Fensel, D. (1998). Knowledge engineering: principles and methods. Data & knowledge engineering, 25(1-2):161–197.
Yu, L. (2014). A developer’s guide to the SW. Springer Science & Business Media.
Laufer, C. (2015). Guia de web semântica. Gov. do Estado de SP e Gov. do Reino Unido.
Vandenbussche, P.-Y., Atemezing, G. A., Poveda-Villalón, M., and Vatant, B. (2017). Linked open vocabularies (lov): a gateway to reusable semantic vocabularies on the web. Semantic Web, 8(3):437–452.
Assaf, A., Troncy, R., and Senart, A. (2015). What’s up lod cloud? In ESWC, pages 247–254. Springer.
Athanasiou, S., Giannopoulos, G., Graux, D., Karagiannakis, N., Lehmann, J., Ngomo, A.-C. N., Patroumpas, K., Sherif, M. A., and Skoutas, D. (2019). Big poi data integration with linked data technologies. In EDBT, pages 477–488.
Berners-Lee, T., Hendler, J., and Lassila, O. (2001). The semantic web. Scientific american, 284(5):34–43.
Bizer, C., Volz, J., Kobilarov, G., and Gaedke, M. (2009). Silk-a link discovery framework for the web of data. In 18th International World Wide Web Conference, volume 122.
Collovini, S., Gonc¸alves, P. N., Cavalheiro, G., Santos, J., and Vieira, R. (2020). Relation extraction for competitive intelligence. In PROPOR, pages 249–258. Springer.
de Oliveira, F. A., Costa, R., Goldschmidt, R., and Cavalcanti, M. (2019). Multirelation association rule mining on datasets of the web of data. In SBSI 2019, page 61. ACM.
de Oliveira, F. A., Martins, Y. C., Rocha, D. S., de Siqueira, M. F., da Silva, L. A. E., Costa, R. L., Goldschmidt, R. R., and Cavalcanti, M. C. (2017). Jabotg: Extending the herbarium dataset frontiers. In MTSR 2017, pages 45–53.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint.
Han, X., Gao, T., Yao, Y., Ye, D., Liu, Z., and Sun, M. (2019). Opennre: An open and extensible toolkit for neural relation extraction. In EMNLP-IJCNLP, pages 169–174.
Hebeler, J., Fisher, M., Blace, R., and Perez-Lopez, A. (2011). Semantic web programming. John Wiley & Sons.
Horrocks, I. (2008). Ontologies and the semantic web. Comm of the ACM, 51(12):58–67.
Nentwig, M., Hartung, M., Ngonga Ngomo, A.-C., and Rahm, E. (2017). A survey of current link discovery frameworks. Semantic Web, 8(3):419–436.
Ngomo, A.-C. N. and Auer, S. (2011). Limes—a time-efficient approach for large-scale link discovery on the web of data. In IJCAI-11.
Paris, P.-H. (2018). Assessing the quality of owl: sameas links. In ESWC, pages 304–313. Springer.
Ramezani, R., Saraee, M., Nematbakhsh, M. A., et al. (2014). Mrar: mining multi-relation association rules. Journal of Computing and Security, 1(2):133–158.
Schmachtenberg, M., Bizer, C., and Paulheim, H. (2014). Adoption of the linked data best practices in different topical domains. In ISWC, pages 245–260. Springer.
Sherif, M. A., Ngomo, A.-C. N., and Lehmann, J. (2015). Automating rdf dataset transformation and enrichment. In ESWC, pages 371–387. Springer.
Studer, R., Benjamins, V. R., and Fensel, D. (1998). Knowledge engineering: principles and methods. Data & knowledge engineering, 25(1-2):161–197.
Yu, L. (2014). A developer’s guide to the SW. Springer Science & Business Media.
Laufer, C. (2015). Guia de web semântica. Gov. do Estado de SP e Gov. do Reino Unido.
Vandenbussche, P.-Y., Atemezing, G. A., Poveda-Villalón, M., and Vatant, B. (2017). Linked open vocabularies (lov): a gateway to reusable semantic vocabularies on the web. Semantic Web, 8(3):437–452.
Publicado
28/09/2020
Como Citar
DA SILVEIRA, Rafael Neves; CAVALCANTI, Maria Cláudia.
Método para rotular Ligações Semânticas na Web de Dados. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 35. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 49-60.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2020.13624.
