Method for Labeling Semantic Web Data Links
Abstract
The Semantic Web, with its languages and standards, provides a common framework that allows data to be shared and reused. One way to increase knowledge about this data is by making new interconnection between datasets. However, most of the interconnection approaches have connections like "Same As" or "Related To". The latter type leaves vague the meaning of the relationship found. This paper presents a method to label this type of relation between datasets, through the use of ontologies and controlled vocabularies. Besides the method, it also presents the WEB application called PLAIN that implements it, and a case study demonstrating the feasibility and functionality of the proposed approach.
Keywords:
Semantic Web, Ontologies, Dataset enrichment
References
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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.
Published
2020-09-28
How to Cite
DA SILVEIRA, Rafael Neves; CAVALCANTI, Maria Cláudia.
Method for Labeling Semantic Web Data Links. In: BRAZILIAN SYMPOSIUM ON DATABASES (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.
