International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

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 Volume 7, Issue 1 (January 2020), Pages: 49-59

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 Original Research Paper

 Title: SAAONT: Ontological knowledge-based development to support intelligent decision-making systems for Saudi Arabian agriculture

 Author(s): Eissa Alreshidi *

 Affiliation(s):

 College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-1446-0780

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2020.01.005

 Abstract:

Ontologies have become an essential tool for domain knowledge representation and a core element of many intelligent systems. It considered an appropriate solution to represent complex concepts and relationships within the agricultural domain. Over the last years, there has been an increasing number of undertaken efforts to develop ontology-based agricultural systems. These existing agricultural ontologies may not be sufficient to provide the desired level of information to individual farmers in Arabic regions, i.e. Saudi Arabia. Additional work is therefore needed to focus on building Arabic ontologies to provide the relevant, contextual and scientifically correct information in Arabia. Furthermore, the current practices within the Saudi Arabian agriculture sector are traditional and lack the technological foundations necessary to build and support intelligent, and sustainable technical solutions. Besides the contribution to the body of knowledge, this paper outlines a state-of-art ontological knowledge-based development for the agriculture sector in Saudi Arabia. It proposes an ontology-driven information retrieval system for agriculture in Saudi Arabia (SAAONT). It aims to firstly, structure and standardize agricultural terminology in Arabic and secondly, provide accurate information to decision-makers, to establish a smarter agriculture environment. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Smart, IoT, Agriculture, Intelligent, Arabic, Ontology, Decision-making, Saudi Arabia

 Article History: Received 19 August 2019, Received in revised form 28 October 2019, Accepted 29 October 2019

 Acknowledgment:

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

 Alreshidi E (2020). SAAONT: Ontological knowledge-based development to support intelligent decision-making systems for Saudi Arabian agriculture. International Journal of Advanced and Applied Sciences, 7(1): 49-59

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 Figures

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 References (48) 

  1. AIMS (2019). AGROVOC. Available online at: https://bit.ly/2qVWoH3
  2. AlAgha IM and Abu-Taha A (2015). AR2SPARQL: An Arabic natural language interface for the semantic web. International Journal of Computer Applications, 125(6): 19-27. https://doi.org/10.5120/ijca2015905928   [Google Scholar]
  3. Albarghothi A, Khater F, and Shaalan K (2017). Arabic question answering using ontology. Procedia Computer Science, 117: 183-191. https://doi.org/10.1016/j.procs.2017.10.108   [Google Scholar]
  4. Alfred R, Chin KO, Anthony P, San PW, Im TL, Leong LC, and Soon GK (2014). Ontology-based query expansion for supporting information retrieval in agriculture. In The 8th International Conference on Knowledge Management in Organizations, Springer, Dordrecht, Netherlands: 299-311. https://doi.org/10.1007/978-94-007-7287-8_24   [Google Scholar]
  5. Al-Hamzi AS (1997). Country report on nematodes-Saudi Arabia. In the Conference of the Expert Consultation on Plant Nematode Problems and their Control in the Near East Region Karachi, Pakistan.   [Google Scholar]
  6. Al-Khalifa H and Al-Wabil A (2007). The Arabic language and the semantic web: Challenges and opportunities. In The 1st International Symposium on Computer and Arabic language: 27-35.   [Google Scholar]
  7. Alreshidi E (2019). Smart sustainable agriculture (SSA) solution underpinned by internet of things (IoT) and artificial intelligence (AI). International Journal of Advanced Computer Science and Applications, 10(5): 93-102. https://doi.org/10.14569/IJACSA.2019.0100513   [Google Scholar]
  8. Alreshidi E and Alyami S (2019). Holistic IoT architecture for smart sustainable cities. In the 9th International Conference on Computing for Sustainable Global Development, INDIACom-2019, India.   [Google Scholar]
  9. Al-Subaiee SS, Yoder EP, and Thomson JS (2005). Extension agents’ perceptions of sustainable agriculture in the Riyadh region of Saudi Arabia. Journal of International Agricultural and Extension Education, 12(1): 5-14. https://doi.org/10.5191/jiaee.2005.12101   [Google Scholar]
  10. Aqeel-ur-Rehman SZ and Shaikh ZA (2011). ONTAgri: Scalable service oriented agriculture ontology for precision farming. Center for Research in Ubiquitous Computing (CRUC), National University of Computer and Emerging Sciences, Karachi, Pakistan.   [Google Scholar]
  11. Bailey R and Willoughby R (2013). Edible oil: Food security in the gulf. Chatham House, London, UK.   [Google Scholar]
  12. Beck H, Morgan K, Jung Y, Grunwald S, Kwon HY, and Wu J (2010). Ontology-based simulation in agricultural systems modeling. Agricultural Systems, 103(7): 463-477. https://doi.org/10.1016/j.agsy.2010.04.004   [Google Scholar]
  13. Black W, Elkateb S, Rodriguez H, Alkhalifa M, Vossen P, Pease A, and Fellbaum C (2006). Introducing the Arabic wordnet project. In the 3rd International WordNet Conference, South Jeju Island, Korea: 295-300.   [Google Scholar]
  14. Bontas EP, Mochol M, and Tolksdorf R (2005). Case studies on ontology reuse. In the 5th International Conference on Knowledge Management, 74: 345.   [Google Scholar]
  15. Cao L and He L (2008). Domain ontology-based construction of agriculture literature retrieval system. In the 4th International Conference on Wireless Communications, Networking and Mobile Computing, IEEE, Dalian, China: 1-3. https://doi.org/10.1109/WiCom.2008.2695   [Google Scholar] PMCid:PMC4853003
  16. Falbo AR (2004). Experiences in using a method for building domain ontologies. In The 16th International Conference on Software Engineering and Knowledge Engineering, SEKE, Banff, Canada: 474-477.   [Google Scholar]
  17. Fensel D (2001). Ontologies. Springer, Berlin, Germany. https://doi.org/10.1007/978-3-662-04396-7   [Google Scholar]
  18. Fiaz S, Noor MA, and Aldosri FO (2018). Achieving food security in the Kingdom of Saudi Arabia through innovation: Potential role of agricultural extension. Journal of the Saudi Society of Agricultural Sciences, 17(4): 365-375. https://doi.org/10.1016/j.jssas.2016.09.001   [Google Scholar]
  19. FoodOn (2019). FoodOn: A field to fork ontology. Available online at: https://bit.ly/2Lcqw7C
  20. Goumopoulos C, Kameas AD, and Cassells A (2009). An ontology-driven system architecture for precision agriculture applications. International Journal of Metadata, Semantics and Ontologies, 4(1-2): 72-84. https://doi.org/10.1504/IJMSO.2009.026256   [Google Scholar]
  21. Griffiths EJ, Dooley DM, Buttigieg PL, Hoehndorf R, Brinkman FS, and Hsiao WW (2016). FoodON: A global farm-to-fork food ontology. In the ICBO/BioCreative, International Conference on Biological Ontology and BioCreative, Corvallis, USA: 1-2.   [Google Scholar]
  22. Gruber TR (1995). Toward principles for the design of ontologies used for knowledge sharing? International Journal of Human-Computer Studies, 43(5-6): 907-928. https://doi.org/10.1006/ijhc.1995.1081   [Google Scholar]
  23. Horridge M, Jupp S, Moulton G, Rector A, Stevens R, and Wroe C (2009). A practical guide to building owl ontologies using protégé 4 and co-ode tools. The University of Manchester, Manchester, UK.   [Google Scholar]
  24. Ingram J and Gaskell P (2019). Searching for meaning: Co-constructing ontologies with stakeholders for smarter search engines in agriculture. NJAS-Wageningen Journal of Life Sciences. https://doi.org/10.1016/j.njas.2019.04.006   [Google Scholar]
  25. Joo S, Koide S, Takeda H, Horyu D, Takezaki A, and Yoshida T (2016). Agriculture activity ontology: An ontology for core vocabulary of agriculture activity. In the International Semantic Web Conference, Posters and Demos, 33: 1-4.   [Google Scholar]
  26. Khamparia A, Pandey B, and Pardesi V (2014). Performance analysis on agriculture ontology using SPARQL query system. In the International Conference on Data Mining and Intelligent Computing, IEEE, New Delhi, India: 1-5. https://doi.org/10.1109/ICDMIC.2014.6954258   [Google Scholar]
  27. Kim HM, Fox MS, and Gruninger M (1995). An ontology of quality for enterprise modelling. In the 4th IEEE Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, IEEE, Berkeley Springs, USA: 105-116. https://doi.org/10.1109/ENABL.1995.484554   [Google Scholar]
  28. Lauser B, Sini M, Liang A, Keizer J, and Katz S (2006). From AGROVOC to the agricultural ontology service/concept server: An OWL model for creating ontologies in the agricultural domain. In the Dublin Core Conference Proceedings, Dublin Core DCMI, Dublin, Ireland.   [Google Scholar]
  29. Maliappis MT (2009). Applying an agricultural ontology to web-based applications. International Journal of Metadata, Semantics and Ontologies, 4(1-2): 133-140. https://doi.org/10.1504/IJMSO.2009.026261   [Google Scholar]
  30. Meenachi NM and Baba MS (2012). A survey on usage of ontology in different domains. International Journal of Applied Information Systems, 9: 46-55. https://doi.org/10.5120/ijais12-450666   [Google Scholar]
  31. Musen MA (2015). The protégé project: A look back and a look forward. AI Matters, 1(4): 4-12. https://doi.org/10.1145/2757001.2757003   [Google Scholar] PMid:27239556 PMCid:PMC4883684
  32. Ngo QH, Le-Khac NA, and Kechadi T (2018). Ontology based approach for precision agriculture. In the International Conference on Multi-Disciplinary Trends in Artificial Intelligence, Springer, Hanoi, Vietnam: 175-186. https://doi.org/10.1007/978-3-030-03014-8_15   [Google Scholar]
  33. Noy NF and McGuinness DL (2001). Ontology development 101: A guide to creating your first ontology. Knowledge Systems Laboratory Tech report KSL-01-05, Stanford Medical Informatics, Stanford University, California, USA.   [Google Scholar]
  34. Papajorgji P (2012). New technologies for constructing complex agricultural and environmental systems. IGI Global, Pennsylvania, USA. https://doi.org/10.4018/978-1-4666-0333-2   [Google Scholar]
  35. Prachi D, Varsha M, Madhu G, Ankita P, Sudarshan GK, and Sanket SP (2016). Overview of agriculture domain ontologies. International Journal of Recent Advances in Engineering and Technology, 4(7): 5-9.   [Google Scholar]
  36. PROTÉGÉ (2019). DL-query tab. Available online at: https://stanford.io/2OFbwkN
  37. Qin X, Zhang H, and Zheng H (2019). Research on intelligent retrieval system for agricultural information resources based on ontology. Journal of Physics: Conference Series, 1168(2): 022041. https://doi.org/10.1088/1742-6596/1168/2/022041   [Google Scholar]
  38. Roussey C, Soulignac V, Champomier JC, Abt V, and Chanet JP (2010). Ontologies in agriculture. In the International Conference on Agricultural Engineering, Cemagref, Clermont-Ferrand, France.   [Google Scholar]
  39. Sicilia MA and Lytras MD (2008). Metadata and semantics. Springer Science and Business Media, Berlin, Germany. https://doi.org/10.1007/978-0-387-77745-0   [Google Scholar]
  40. Sini M, Lauser B, Salokhe G, Keizer J, and Katz S (2008). The AGROVOC concept server: Rationale, goals and usage. Library Review, 57(3): 200-212. https://doi.org/10.1108/00242530810865745   [Google Scholar]
  41. Tedman RA and Tedman DK (2007). Introduction to the evolution of teaching and learning paradigms. In: Jain LC, Tedman RA, and Tedman DK (Eds.), Evolution of teaching and learning paradigms in intelligent environment: 1-6. Springer, Berlin, Germany. https://doi.org/10.1007/978-3-540-71974-8_1   [Google Scholar]
  42. Ukpe E (2013). Agriculture ontology for sustainable development in Nigeria. International Organization of Scientific Research Journal of Computer Engineering, 14(5): 57-59. https://doi.org/10.9790/0661-1455759   [Google Scholar]
  43. Uschold M and King M (1995). Towards a methodology for building ontologies. Artificial Intelligence Applications Institute, University of Edinburgh, Edinburgh, Scotland.   [Google Scholar]
  44. WAICENT (2019). WAICENT portal. Available online at: https://bit.ly/2rIhqsG
  45. Walisadeera AI, Ginige A, and Wikramanayake GN (2015). User centered ontology for Sri Lankan farmers. Ecological Informatics, 26: 140-150. https://doi.org/10.1016/j.ecoinf.2014.07.008   [Google Scholar]
  46. WEBVOWL (2019). WebVOWL: Web-based visualization of ontologies. Available online at: https://bit.ly/2LbVCfW
  47. Xie N, Wang W, and Yang Y (2007). Ontology-based agricultural knowledge acquisition and application. In the International Conference on Computer and Computing Technologies in Agriculture, Springer, Boston, USA: 349-357. https://doi.org/10.1007/978-0-387-77251-6_38   [Google Scholar]
  48. Yang Y, Du J, and Liang M (2011). Study on food safety semantic retrieval system based on domain ontology. In the IEEE International Conference on Cloud Computing and Intelligence Systems, IEEE, Beijing, China: 40-44. https://doi.org/10.1109/CCIS.2011.6045028   [Google Scholar]