From Web Resources to Agricultural Ontology: a Method for Semi-Automatic Construction

Wei, Yuanyuan; Wang, Rujing; Hu, Yi-min; Wang, Xue

doi:10.1016/s2095-3119(12)60067-7

Cited by 23 publications

(11 citation statements)

References 14 publications

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“…In this study, experts in the domain of Arabic language implemented the conceptual characterization of the Arabic ontology. The concepts and semantic relations of the ontology hierarchy were then built using the Protégé tool [69]. Considering the concept hierarchy structure of the biomedical information depicted in Figure 1, the concept similarities can be computed based on the distances between nodes.…”

Section: Concept Similarity Weightsmentioning

confidence: 99%

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

2019

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Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial part of modern semantic webs because they can convert a web of documents into a web of things. Although ontology learning generally occupies a large space in computer science, Arabic ontology learning, in particular, is underdeveloped due to the Arabic language’s nature as well as the profundity required in this domain. The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. The model proposed in this work contributes to Arabic ontology learning in two ways. First, a text mining algorithm is proposed for extracting concepts and their semantic relations from text documents. The algorithm calculates the concept frequency weights using the term frequency weights. Then, it calculates the weights of concept similarity using the information of the ontology structure, involving (1) the concept’s path distance, (2) the concept’s distribution layer, and (3) the mutual parent concept’s distribution layer. Then, feature mapping is performed by assigning the concepts’ similarities to the concept features. Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. The operator of the G-WOA is a hybrid operator integrating GA’s mutation, crossover, and selection processes with the WOA’s processes (encircling prey, attacking of bubble-net, and searching for prey) to fulfill the balance between both exploitation and exploration, and to find the solutions that exhibit the highest fitness. For evaluating the performance of the ontology learning approach, extensive comparisons are conducted using different Arabic corpora and bio-inspired optimization algorithms. Furthermore, two publicly available non-Arabic corpora are used to compare the efficiency of the proposed approach with those of other languages. The results reveal that the proposed genetic-whale optimization algorithm outperforms the other compared algorithms across all the Arabic corpora in terms of precision, recall, and F-score measures. Moreover, the proposed approach outperforms the state-of-the-art methods of ontology learning from Arabic and non-Arabic texts in terms of these three measures.

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Section: Concept Similarity Weightsmentioning

confidence: 99%

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

2019

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“…[4] A "conceptualization" refers to an abstract model of some phenomena in the world by having identified the relevant concepts of those phenomena; "explicit" means that the type of concepts used, and the constraints on their use are explicitly defined. [16] Ontology is also the representation of Vocabulary or reference to a body of knowledge in some specific domain. It can be used to model a domain formally so that it can be used for knowledge sharing and information integration.…”

Section: A Ontologymentioning

confidence: 99%

“…The main purpose of AOS is to achieve interoperability among different agriculture systems [11]. AOS has been developed to provide the concepts between the terms and the specifications of relationships in agricultural information for the web environment [16] AOS allows agricultural resources of multiple languages to be combined together. It provides a framework for sharing common domain-specific terminology within the agricultural community [11].…”

Section: Nomentioning

confidence: 99%

“…Two of the main applications in Agricultural ontology have been highlighted below: 1) WEI Yuan-yuan et al proposed and developed a semi-automatic construction method for agricultural professional ontology from web resources. For semi-structured web pages, the method automatically extracted and stored structured data through a program, built pattern mapping between relational database and ontology through humancomputer interaction, and automatically generated a preliminary ontology [16]. 2) Agriculture Ontology Service Research Group of Agricultural Information Institute of Chinese Academy of Agriculture Sciences has carried out many ontology projects in China, pertaining to the domain of Agriculture.…”

Section: Chinamentioning

confidence: 99%

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An Exploratory Study on Agriculture Ontology: A Global Perspective

Jebaraj¹,

Sathiaseelan²

2017

IJARCSSE

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Abstract-Ontologies have played a vital role in the field of agriculture. The agriculture domain is a vast and complex system science and the knowledge of it consists of many concepts and relationships. These difficult concepts and relationships have been modelled using Ontologies in various capacities in different countries. This paper is an exploratory study, presenting a review on the various ontology-based applications developed specifically in the domain of agriculture. It also discusses the various Agriculture Ontologies created by specific developing countries. Finally this paper also summarizes the directions for future work.

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“…Information Engineering and Electronic Business, 2020, 2, 28-39 premised on collection of concepts with their semantic relationships and constraints on a chosen area of knowledge (domain). The real power of ontologies lies in its ability to build relationships among classes and instances, and to assign properties to those relationships that enable inferences [10,11,12]. Similarly, one of the popular definitions of ontology [13,14] is that of [15] which states that ontology is an explicit and formal specification of a conceptualization.…”

Section: Introductionmentioning

confidence: 99%

A Review on Ontology Development Methodologies for Developing Ontological Knowledge Representation Systems for various Domains

Aminu¹,

Oyefolahan²,

Abdullahi³

et al. 2020

IJIEEB

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The success of machine represented web known as semantic web largely hinges on ontologies. Ontology is a data modeling technique for structured data repository premised on collection of concepts with their semantic relationships and constraints on domain. There are existing methodologies to aid ontology development process. However, there is no single correct ontology design methodology. Therefore, this paper aims to present a review on existing ontology development approaches for different domains with the goal of identifying individual methodology's weakness and suggests for hybridization in order to strengthen ontology development in terms of its content and constructions correctness. The analysis and comparison of the review were carried out by considering these criteria but not limited to: activities of each method, the initial domain of the methodology, ontology created from scratch or reuse, frequently used ontology management tools based on literature, subject granularity, and usage across different platforms. This review based on the literature showed some approaches that exhibit the required principles of ontology engineering in tandem with software development principles. Nonetheless, the review still noted some gaps among the methodologies that when bridged or hybridized a better correctness of ontology development would be achieved in building intelligent system.

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From Web Resources to Agricultural Ontology: a Method for Semi-Automatic Construction

Cited by 23 publications

References 14 publications

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

An Exploratory Study on Agriculture Ontology: A Global Perspective

A Review on Ontology Development Methodologies for Developing Ontological Knowledge Representation Systems for various Domains

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