An effective automated ontology construction based on the agriculture domain

Deepa, Rajendran; Vigneshwari, S.

doi:10.4218/etrij.2020-0439

Cited by 11 publications

(3 citation statements)

References 30 publications

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“…Renny et al ( 2021) used text mining and machine learning to construct an ontology for the tomato pest and disease domain [14]. Deepa R et al (2022) used natural speech processing techniques to extract agricultural terms and combined textual similarity with plain Bayes (NBM) to propose a method for automatically constructing agricultural ontologies [15]. In specific verticals, narrative lists are difficult to provide finer-grained knowledge categorization, and ontology construction relies more on domain experts by manual construction.…”

Section: Ontologymentioning

confidence: 99%

Automated Knowledge Extraction in the Field of Wheat Sharp Eyespot Control

Liu,

Cui

2024

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Wheat sharp eyespot is a soil-borne fungal disease commonly found in wheat areas in China, which can occur throughout the entire reproductive period of wheat and has a great impact on the yield and quality of wheat in China. By constructing a domain ontology for wheat sharp eyespot control and modeling the domain knowledge, we aim to integrate and share the knowledge in the field of wheat sharp eyespot control, which can provide important support and guidance for agricultural decision-making and disease control. In this study, the literature in the field of wheat sharp eyespot control was used as a data source, the KeyBERT keyword extraction algorithm was used to mine the core concepts of the ontology, and the hierarchical relationships among the ontology concepts were extracted through clustering. Based on the constructed ontology of wheat sharp eyespot control, the schema of knowledge extraction was formed, and the knowledge extraction model was trained using the ERNIE 3.0 knowledge enhancement pretraining model. This study proposes a model and algorithm to realize knowledge extraction based on domain ontology, describes the construction method and process framework of wheat sharp eyespot control domain ontology, and details the training and reasoning effect of the knowledge extraction model. The knowledge extraction model constructed in this study for wheat sharp eyespot control contains a more complete conceptual system of wheat sharp eyespot. The F1 value of the model reaches 91.26%, which is a 17.86% improvement compared with the baseline model, and it can satisfy the knowledge extraction needs in the field of wheat sharp eyespot control. This study can provide a reference for domain knowledge extraction and provide strong support for knowledge discovery and downstream applications such as intelligent Q&A and intelligent recommendation in the field of wheat sharp eyespot control.

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Section: Ontologymentioning

confidence: 99%

Automated Knowledge Extraction in the Field of Wheat Sharp Eyespot Control

Liu,

Cui

2024

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“…This study discovered relevant ontological knowledge from text data which are among the commonly used data sources for constructing the ontology (Faria et al, 2014;Arguello Casteleiro et al, 2017;Mahmoud et al, 2018;Ayadi et al, 2019b;Al-Aswadi et al, 2019;Deepa & Vigneshwari, 2022).…”

Section: Data Acquisitionmentioning

confidence: 99%

An Ontology for Defining and Characterizing Demonstration Environments

Nie¹,

Vita²,

Masood³

2022

SSRN Journal

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“…The classification and discovery of knowledge get much easier when the conceptualization improves the data elements through property characteristics. Natural language processing [21], machine learning, information retrieval, data mining [22], and knowledge representation [23] techniques have all contributed to the evolution of ontology development. The technique also allows for the creation of numerous interpretable patterns that can be used to make future predictions.…”

Section: Introductionmentioning

confidence: 99%

Design of Multiple Ontology Based Agro Knowledge Mining Model

Abdullah,

Murali,

et al. 2023

IJRITCC

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Farming is regarded as a major industry in India, accounting for 17% of the country's GDP growth. Agriculture employs 60% of the population hence it is considered an important sector in India. The important factors for agriculture are pest management, disease prevention, irrigation management, soil mineral composition, crop management, location, and the season in which the crop is grown. Hence all this information along with the techniques are well known only by the experienced farmers. Hence it is important to create an agro knowledge management system. As a result, this work makes an attempt to develop a multiple ontology-based agro knowledge management system. The designed system consists of agriculture information related to attributes of soil mineral, moisture, season, location, crop type, and temperature. It consists of multiple ontologies such as soil ontology, crop ontology, location ontology, and crop season ontology to provide agronomy knowledge. Soil ontology is premeditated to classify the soil type in a hierarchical order while crop ontology classifies the crop type, location ontology classifies locations suitable for different crop types and finally, crop season ontology classifies the season that is suitable for different crops. A rule base is built to develop the knowledge base and to validate the truthfulness of the knowledge base. Visualization of a knowledge base is carried out for better understanding and decision-making.

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An effective automated ontology construction based on the agriculture domain

Cited by 11 publications

References 30 publications

Automated Knowledge Extraction in the Field of Wheat Sharp Eyespot Control

Automated Knowledge Extraction in the Field of Wheat Sharp Eyespot Control

An Ontology for Defining and Characterizing Demonstration Environments

Design of Multiple Ontology Based Agro Knowledge Mining Model

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