A Matching Approach to Confer Semantics over Tabular Data Based on Knowledge Graphs

Author:

Baazouzi Wiem,Kachroudi Marouen,Faiz Sami

Publisher

Springer Nature Switzerland

Reference14 articles.

1. Abdelmageed, N., Schindler, S.: JenTab: matching tabular data to knowledge graphs. In: Proceedings of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2020) co-located with the 19th International Semantic Web Conference (ISWC 2020), Virtual conference (originally planned to be in Athens, Greece), 5 November 2020. CEUR Workshop Proceedings, vol. 2775, pp. 40–49 (2020)

2. Chabot, Y., Labbé, T., Liu, J., Troncy, R.: DAGOBAH: an end-to-end context-free tabular data semantic annotation system. In: Proceedings of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Co-located with the 18th International Semantic Web Conference, SemTab@ISWC 2019, Auckland, New Zealand, 30 October 2019. CEUR Workshop Proceedings, vol. 2553, pp. 41–48 (2019)

3. Chen, J., Jiménez-Ruiz, E., Horrocks, I., Sutton, C.: Learning semantic annotations for tabular data. arXiv preprint arXiv:1906.00781 (2019)

4. Chen, S., et al.: Linkingpark: an integrated approach for semantic table interpretation. In: Proceedings of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2020) co-located with the 19th International Semantic Web Conference (ISWC 2020), Virtual conference (originally planned to be in Athens, Greece), 5 November 2020. CEUR Workshop Proceedings, vol. 2775, pp. 65–74 (2020)

5. Cremaschi, M., De Paoli, F., Rula, A., Spahiu, B.: A fully automated approach to a complete semantic table interpretation. Futur. Gener. Comput. Syst. 112, 478–500 (2020)

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