Agri semantics : developments to improve data interoperability to support farm information management and decision support systems in agriculture
- Author
- Saba Noor (UGent) , Jade Bokma (UGent) , Bart Pardon (UGent) , Gerdien van Schaik and Miels Hostens
- Organization
- Project
- Abstract
- Farm animal health management systems (FAHMSs) face significant challenges in data acquisition, integration, and analysis. In this context, the semantics of agriculture data, which takes advantage of semantic web technologies, is an important tool for improving data management and enabling informed decision-making. However, existing systems lack standardization, integrity, interoperability, reusability, and advanced analytical reasoning. The authors propose an ontology-driven, knowledge-based framework for FAHMSs to address these challenges. Their framework focuses on a cattle application scenario and provides a standardized framework, a species-specific Livestock Health Ontology (LHO), Resource Descriptive Framework (RDF) data generation, and semantic interoperability. This research aims to improve disease surveillance and early detection, leading to better animal health outcomes. The chapter comprehensively analyzes the background knowledge, presents the methodology as a case study, and concludes with future research directions and challenges.
- Keywords
- Livestock Health Ontology, Data Integration, Agri Semantics, Semantic interoperability, Barometer, Cattle, Pig, Poultry
Downloads
-
BookChapter.pdf
- full text (Published version)
- |
- open access
- |
- |
- 3.32 MB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01HZ08G35DJNW77HZP2X1P6H6B
- MLA
- Noor, Saba, et al. “Agri Semantics : Developments to Improve Data Interoperability to Support Farm Information Management and Decision Support Systems in Agriculture.” Smart Farms : Improving Data-Driven Decision Making in Agriculture, edited by Claus Grøn Sørensen, Burleigh Dodds Science, 2024, pp. 75–96, doi:10.19103/as.2023.0132.05.
- APA
- Noor, S., Bokma, J., Pardon, B., van Schaik, G., & Hostens, M. (2024). Agri semantics : developments to improve data interoperability to support farm information management and decision support systems in agriculture. In C. G. Sørensen (Ed.), Smart farms : improving data-driven decision making in agriculture (pp. 75–96). https://doi.org/10.19103/as.2023.0132.05
- Chicago author-date
- Noor, Saba, Jade Bokma, Bart Pardon, Gerdien van Schaik, and Miels Hostens. 2024. “Agri Semantics : Developments to Improve Data Interoperability to Support Farm Information Management and Decision Support Systems in Agriculture.” In Smart Farms : Improving Data-Driven Decision Making in Agriculture, edited by Claus Grøn Sørensen, 75–96. Cambridge: Burleigh Dodds Science. https://doi.org/10.19103/as.2023.0132.05.
- Chicago author-date (all authors)
- Noor, Saba, Jade Bokma, Bart Pardon, Gerdien van Schaik, and Miels Hostens. 2024. “Agri Semantics : Developments to Improve Data Interoperability to Support Farm Information Management and Decision Support Systems in Agriculture.” In Smart Farms : Improving Data-Driven Decision Making in Agriculture, ed by. Claus Grøn Sørensen, 75–96. Cambridge: Burleigh Dodds Science. doi:10.19103/as.2023.0132.05.
- Vancouver
- 1.Noor S, Bokma J, Pardon B, van Schaik G, Hostens M. Agri semantics : developments to improve data interoperability to support farm information management and decision support systems in agriculture. In: Sørensen CG, editor. Smart farms : improving data-driven decision making in agriculture. Cambridge: Burleigh Dodds Science; 2024. p. 75–96.
- IEEE
- [1]S. Noor, J. Bokma, B. Pardon, G. van Schaik, and M. Hostens, “Agri semantics : developments to improve data interoperability to support farm information management and decision support systems in agriculture,” in Smart farms : improving data-driven decision making in agriculture, C. G. Sørensen, Ed. Cambridge: Burleigh Dodds Science, 2024, pp. 75–96.
@incollection{01HZ08G35DJNW77HZP2X1P6H6B,
abstract = {{Farm animal health management systems (FAHMSs) face significant challenges in data acquisition, integration, and analysis. In this context, the semantics of agriculture data, which takes advantage of semantic web technologies, is an important tool for improving data management and enabling informed decision-making. However, existing systems lack standardization, integrity, interoperability, reusability, and advanced analytical reasoning. The authors propose an ontology-driven, knowledge-based framework for FAHMSs to address these challenges. Their framework focuses on a cattle application scenario and provides a standardized framework, a species-specific Livestock Health Ontology (LHO), Resource Descriptive Framework (RDF) data generation, and semantic interoperability. This research aims to improve disease surveillance and early detection, leading to better animal health outcomes. The chapter comprehensively analyzes the background knowledge, presents the methodology as a case study, and concludes with future research directions and challenges.}},
author = {{Noor, Saba and Bokma, Jade and Pardon, Bart and van Schaik, Gerdien and Hostens, Miels}},
booktitle = {{Smart farms : improving data-driven decision making in agriculture}},
editor = {{Sørensen, Claus Grøn}},
isbn = {{9781801463829}},
issn = {{2059-6936}},
keywords = {{Livestock Health Ontology,Data Integration,Agri Semantics,Semantic interoperability,Barometer,Cattle,Pig,Poultry}},
language = {{eng}},
pages = {{75--96}},
publisher = {{Burleigh Dodds Science}},
series = {{Burleigh Dodds series in agricultural science}},
title = {{Agri semantics : developments to improve data interoperability to support farm information management and decision support systems in agriculture}},
url = {{http://doi.org/10.19103/as.2023.0132.05}},
year = {{2024}},
}
- Altmetric
- View in Altmetric