Abstract
Smart farming is coming with a clear promise to mitigate the myriad of threatens faced by vineyards. In this respect, relying on sensor data, new challenges are rising in order to proactively warn farmers. In this paper, we introduce the SmartVine approach, which extracts knowledge from collected data, converts it into inference rules and integrates them into the reasoning process of the system. In the sake of efficiency, generic bases of association rules are extracted, mapped then to SWRL rules and later used for the enrichment process of the ontology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB 1994, San Francisco, CA, USA, pp. 487–499. Morgan Kaufmann Publishers Inc. (1994)
Bazin, A., Gros, N., Bertaux, A., Nicolle, C.: Condensed representations of association rules in n-ary relations. IEEE Trans. Knowl. Data Eng. (TKDE) (2019, to appear)
Ben Ahmed, E., Gargouri, F.: Enhanced association rules over ontology resources. IJWA 7(1), 10–22 (2015)
d’Amato, C., Staab, S., Tettamanzi, A.G.B., Minh, T.D., Gandon, F.: Ontology enrichment by discovering multi-relational association rules from ontological knowledge bases. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, SAC 2016, pp. 333–338. ACM (2016)
Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquis. 5(2), 199–220 (1993)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. SIGMOD Rec. 29(2), 1–12 (2000)
Idoudi, R., Saheb Ettabaâ, K., Solaiman, B., Hamrouni, K., Mnif, N.: Association rules-based ontology enrichment. IJWA 8, 16–25 (2016)
Mahmood, A., Shi, K., Khatoon, S., Xiao, M.: Data mining techniques for wireless sensor networks: a survey. Int. J. Distrib. Sens. Netw. 9(7), 406316 (2013)
Mouakher, A., Belkaroui, R., Bertaux, A., Labbani, O., Hugol-Gential, C., Nicolle, C.: An ontology-based monitoring system in vineyards of the burgundy region. In: Proceedings of the 28th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, WETICE 2019, Naples, Italy, 12–14 June 2019, pp. 307–312 (2019)
Paiva, L.: Semantic relations extraction from unstructured information for domain ontologies enrichment. Ph.D. thesis, Universidade NOVA de Lisboa (2015)
Paiva, L., Costa, R., Figueiras, P., Lima, C.: Discovering semantic relations from unstructured data for ontology enrichment: asssociation rules based approach. In: Proceedings of the 9th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–6 (2014)
Acknowledgement
This study was conducted as part of the FUI WineCloud (https://winecloud.eurestools.eu/.) project. The authors would like to thank the project partners for their valuable contribution, namely: Orange, R-Tech Solutions, The Cave of Lugny and Photon Lines. The authors are also grateful to all the technical team for their collaboration: Nicolas Gros, Marie Simon and Sébastien Gerin.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mouakher, A., Bertaux, A., Labbani, O., Hugol-Gential, C., Nicolle, C. (2020). Ontology for Smart Viticulture: Integrating Inference Rules Based on Sensor Data. In: Yangui, S., et al. Service-Oriented Computing – ICSOC 2019 Workshops. ICSOC 2019. Lecture Notes in Computer Science(), vol 12019. Springer, Cham. https://doi.org/10.1007/978-3-030-45989-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-45989-5_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45988-8
Online ISBN: 978-3-030-45989-5
eBook Packages: Computer ScienceComputer Science (R0)