Enhancing Agricultural Knowledge Management using an efficient & Novel Ontology-Based Approach Leveraging BERT-GPT and Graph Recurrent Q Learning Network
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Abstract
Agriculture, as one of the key drivers for human civilization, demands efficient methods for managing the extensive domain-specific knowledge. While ontologies, structured sets of terms and relationships within a specific domain, have played a pivotal role in structuring agricultural data, existing models lack the precision and adaptability necessary for the dynamic agricultural environment. This paper proposes a novel framework that integrates the strengths of BERT (Bidirectional Encoder Representations from Transformers) GPT (Generative Pre-trained Transformer), and Graph Recurrent Q Learning Network (GRQLN) for developing a dynamic and efficient ontology tool. BERT-GPT, with its powerful natural language processing capabilities, allows for accurate feature extraction from diverse text data, including government and farming sources. Meanwhile, GRQLN leverages graph neural networks and reinforcement learning to convert these features into an ontology graph, optimizing the representation process. The integration of these advanced technologies not only addresses the limitations of existing models but also enhances the precision of ontological query retrieval by 8.5%, accuracy by 8.3%, recall by 10.4%, AUC by 9.5%, and specificity by 9.4%, while reducing delay by 4.5%. The proposed model is a significant contribution to the field, offering a robust tool that empowers stakeholders with actionable insights derived from a vast expanse of agricultural knowledge, ultimately facilitating informed decision-making in the ever-evolving landscape of agricultural operations.
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References
A. Banerjee and V. Choppella, “Knowledge Driven Synthesis Using Resource-Capability Semantics for Control Software Design,” IEEE Access, vol. 11, no. June, pp. 52527–52539, 2023, doi: 10.1109/ACCESS.2023.3277859.
S. I. Wilson, J. S. Goonetillake, A. Ginige, and A. I. Walisadeera, “Towards a Usable Ontology: The Identification of Quality Characteristics for an Ontology-Driven Decision Support System,” IEEE Access, vol. 10, pp. 12889–12912, 2022, doi: 10.1109/ACCESS.2022.3146331.
P. Liu, L. Qian, X. Zhao, and B. Tao, “The Construction of Knowledge Graphs in the Aviation Assembly Domain Based on a Joint Knowledge Extraction Model,” IEEE Access, vol. 11, no. March, pp. 26483–26495, 2023, doi: 10.1109/ACCESS.2023.3254132.
A. Benarab, J. Sun, F. Rafique, and A. Refoufi, “Global Ontology Entities Embeddings,” IEEE Trans. Knowl. Data Eng., vol. 35, no. 11, pp. 1–12, 2023, doi: 10.1109/tkde.2023.3235779.
S. Ibrahim, S. Fathalla, J. Lehmann, and H. Jabeen, “Toward the Multilingual Semantic Web: Multilingual Ontology Matching and Assessment,” IEEE Access, vol. 11, no. January, pp. 8581–8599, 2023, doi: 10.1109/ACCESS.2023.3238871.
Z. Brahmia, F. Grandi, and R. Bouaziz, “τJOWL: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data,” Big Data Min. Anal., vol. 5, no. 4, pp. 271–281, 2022, doi: 10.26599/BDMA.2021.9020019.
F. Abad-Navarro, C. Martínez-Costa, and J. T. Fernández-Breis, “HURON: A quantitative framework for assessing human readability in ontologies,” IEEE Access, no. July, pp. 1–1, 2023, doi: 10.1109/access.2023.3316512.
A. Pinto, Y. Cardinale, I. Dongo, and R. Ticona-Herrera, “An Ontology for Modeling Cultural Heritage Knowledge in Urban Tourism,” IEEE Access, vol. 10, pp. 61820–61842, 2022, doi: 10.1109/ACCESS.2022.3179664.
Z. Brahmia, F. Grandi, and R. Bouaziz, “τSQWRL: A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data,” Big Data Min. Anal., vol. 6, no. 3, pp. 288–300, 2023, doi: 10.26599/BDMA.2022.9020044.
F. Santos and C. E. Mello, “Matching Network of Ontologies: A Random Walk and Frequent Itemsets Approach,” IEEE Access, vol. 10, pp. 44638–44659, 2022, doi: 10.1109/ACCESS.2022.3164067.
A. Jaradeh and M. B. Kurdy, “ArEmotive Bridging the Gap: Automatic Ontology Augmentation Using Zero-Shot Classification for Fine-Grained Sentiment Analysis of Arabic Text,” IEEE Access, vol. 11, no. July, pp. 81318–81330, 2023, doi: 10.1109/ACCESS.2023.3300737.
Y. Wen, X. Zhu, and L. Zhang, “CQACD: A Concept Question-Answering System for Intelligent Tutoring Using a Domain Ontology with Rich Semantics,” IEEE Access, vol. 10, no. June, pp. 67247–67261, 2022, doi: 10.1109/ACCESS.2022.3185400.
Z. Ren, J. Shi, and M. Imran, “Data Evolution Governance for Ontology-Based Digital Twin Product Lifecycle Management,” IEEE Trans. Ind. Informatics, vol. 19, no. 2, pp. 1791–1802, 2023, doi: 10.1109/TII.2022.3187715.
X. Ni, B. Geng, H. Zheng, J. Shi, G. Hu, and J. Gao, “Accurate Estimation of Single-Cell Differentiation Potency Based on Network Topology and Gene Ontology Information,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 19, no. 6, pp. 3255–3262, 2022, doi: 10.1109/TCBB.2021.3112951.
O. Gerasimova, N. Severin, and I. Makarov, “Comparative Analysis of Logic Reasoning and Graph Neural Networks for Ontology-Mediated Query Answering With a Covering Axiom,” IEEE Access, vol. 11, no. July, pp. 88074–88086, 2023, doi: 10.1109/ACCESS.2023.3305272.
L. Buoncompagni, S. Y. Kareem, and F. Mastrogiovanni, “Human Activity Recognition Models in Ontology Networks,” IEEE Trans. Cybern., vol. 52, no. 6, pp. 5587–5606, 2022, doi: 10.1109/TCYB.2021.3073539.
M. Paul and A. Anand, “A New Family of Similarity Measures for Scoring Confidence of Protein Interactions Using Gene Ontology,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 19, no. 1, pp. 19–30, 2022, doi: 10.1109/TCBB.2021.3083150.
J. Lu, J. Ma, X. Zheng, G. Wang, H. Li, and D. Kiritsis, “Design Ontology Supporting Model-Based Systems Engineering Formalisms,” IEEE Syst. J., vol. 16, no. 4, pp. 5465–5476, 2022, doi: 10.1109/JSYST.2021.3106195.
A. Pedro, S. Baik, J. Jo, D. Lee, R. Hussain, and C. Park, “A Linked Data and Ontology-based Framework for Enhanced Sharing of Safety Training Materials in the Construction Industry,” IEEE Access, vol. 11, no. September, 2023, doi: 10.1109/ACCESS.2023.3319090.
M. A. Osman, S. A. Mohd Noah, and S. Saad, “Ontology-Based Knowledge Management Tools for Knowledge Sharing in Organization-A Review,” IEEE Access, vol. 10, pp. 43267–43283, 2022, doi: 10.1109/ACCESS.2022.3163758.
Z. Xie, L. Ren, Q. Zhan, and Y. Liu, “A Constructivist Ontology Relation Learning Method,” IEEE Trans. Cybern., vol. 52, no. 7, pp. 6434–6441, 2022, doi: 10.1109/TCYB.2021.3138452.
L. Besancon, C. F. Da Silva, P. Ghodous, and J. P. Gelas, “A Blockchain Ontology for DApps Development,” IEEE Access, vol. 10, pp. 49905–49933, 2022, doi: 10.1109/ACCESS.2022.3173313.
A. Carniel, J. D. M. Bezerra, and C. M. Hirata, “An Ontology-Based Approach to Aid STPA Analysis,” IEEE Access, vol. 11, no. January, pp. 12676–12696, 2023, doi: 10.1109/ACCESS.2023.3242642.
Y. Feng, L. Qi, and W. Tian, “PhenoBERT: A Combined Deep Learning Method for Automated Recognition of Human Phenotype Ontology,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 20, no. 2, pp. 1269–1277, 2023, doi: 10.1109/TCBB.2022.3170301.
Y. Liu, Z. Yu, J. Wang, B. Guo, J. Su, and J. Liao, “CrowdManager: An Ontology-Based Interaction and Management Middleware for Heterogeneous Mobile Crowd Sensing,” IEEE Trans. Mob. Comput., vol. 22, no. 11, pp. 6358–6376, 2022, doi: 10.1109/TMC.2022.3199787.