Rule-based Approach for Automatic Ontology Population of Agriculture Domain

Saat, Nur Izyan Yasmin; Noah, Shahrul Azman Mohd

doi:10.3923/itj.2016.46.51

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…For the automated ontology population in the agricultural sector, a rule‐based methodology was developed. The documents were processed using the NLP technique at the earlier stages of the approach and displayed an accuracy of about 74% and 85% in precision and recall values [24].…”

Section: Related Workmentioning

confidence: 99%

An effective automated ontology construction based on the agriculture domain

Deepa

Vigneshwari

2022

ETRI Journal

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The agricultural sector is completely different from other sectors since it completely relies on various natural and climatic factors. Climate changes have many effects, including lack of annual rainfall and pests, heat waves, changes in sea level, and global ozone/atmospheric CO 2 fluctuation, on land and agriculture in similar ways. Climate change also affects the environment.Based on these factors, farmers chose their crops to increase productivity in their fields. Many existing agricultural ontologies are either domain-specific or have been created with minimal vocabulary and no proper evaluation framework has been implemented. A new agricultural ontology focused on subdomains is designed to assist farmers using Jaccard relative extractor (JRE) and Naïve Bayes algorithm. The JRE is used to find the similarity between two sentences and words in the agricultural documents and the relationship between two terms is identified via the Naïve Bayes algorithm. In the proposed

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Section: Related Workmentioning

confidence: 99%

An effective automated ontology construction based on the agriculture domain

Deepa

Vigneshwari

2022

ETRI Journal

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“…The named entities are related to appropriate concepts as instances in the semantic Knowledgebase. Then, mapping the relations between those named entities to the suitable property, data type or object, in the ontology as relation instances in the semantic Knowledgebase (Kumar & Zayaraz, 2015;Saat & Noah, 2016).…”

Section: Populating the Extracted Informationmentioning

confidence: 99%

A Semantic Knowledge-Based Framework for Information Extraction and Exploration

Aljamel

Osman

Thakker

2021

International Journal of Decision Support System Technology

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The availability of online documents that describe domain-specific information provides an opportunity in employing a knowledge-based approach in extracting information from web data. This research proposes a novel comprehensive semantic knowledge-based framework that helps to transform unstructured data to be easily exploited by data scientists. The resultant sematic knowledgebase is reasoned to infer new facts and classify events that might be of importance to end users. The target use case for the framework implementation was the financial domain, which represents an important class of dynamic applications that require the modelling of non-binary relations. Such complex relations are becoming increasingly common in the era of linked open data. This research in modelling and reasoning upon such relations is a further contribution of the proposed semantic framework, where non-binary relations are semantically modelled by adapting the semantic reasoning axioms to fit the intermediate resources in the N-ary relations requirements.

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Innovative agricultural ontology construction using NLP methodologies and graph neural network

Sanju Saravanan,

Bhagavathiappan

2024

Engineering Science and Technology, an International Journal

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Rule-based Approach for Automatic Ontology Population of Agriculture Domain

Cited by 6 publications

References 3 publications

An effective automated ontology construction based on the agriculture domain

An effective automated ontology construction based on the agriculture domain

A Semantic Knowledge-Based Framework for Information Extraction and Exploration

Innovative agricultural ontology construction using NLP methodologies and graph neural network

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