Using Agricultural Ontologies

Maliappis, Michael T.

doi:10.1007/978-0-387-77745-0_48

Cited by 4 publications

(4 citation statements)

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“…The contribution of the current research is threefold: first, a pest-control ontology that carries both theoretical and practical implications was developed. On the one hand, it contributes to the existing literature on pest-control ontologies (Beck et al ., 2005; Maliappis, 2009; Li et al ., 2013; Liao et al ., 2015) by adding concepts that have not been covered thus far. On the other hand, the proposed ontology satisfies design criteria (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Beck et al (2005) created a crop-pest ontology to foster accurate retrieval of publications and educational resources. Maliappis (2009) and Liao et al (2015) used an ontology that covers, among other things, the crop-pest domain in order to integrate crop-cultivation knowledge from different independent information systems to support farmers in their daily tasks. However, the schemata of the ontologies used in previous studies remained undescribed.…”

Section: Introductionmentioning

confidence: 99%

“…The increasing use of ontologies is also seen in agriculture (Zhang et al ., 2002; Chang et al ., 2008; Beck et al ., 2010; Roussey et al ., 2010; Song et al ., 2012; Li et al ., 2013; Liao et al ., 2015; Tomic et al ., 2015; Chougule et al ., 2016; Kragt et al ., 2016), where they are used for various purposes such as sharing of agricultural knowledge among farmers around the world (and in different languages) (AGROVOC Thesaurus; Chang et al ., 2008; Maliappis, 2009), creating semantic inter-operability of agricultural systems (Goumopoulos et al ., 2009; Aqeel-ur and Zubair, 2011; Tomic et al ., 2015) and supporting farmer decisions (Gaire et al ., 2013). This trend is not surprising, given that agriculture is a knowledge-centric field that covers many areas of expertise, worldwide practices and technologies, and concepts that are often designated by different names with similar meaning (Palavitsinis and Manouselis, 2014; Liao et al ., 2015) that are fragmented across different systems (Janssen et al ., 2017).…”

Section: Introductionmentioning

confidence: 99%

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Addressing the ‘Tower of Babel’ of pesticide regulations: an ontology for supporting pest-control decisions

et al. 2019

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Farmers, who have to decide which pesticide to use against a particular crop-damaging pest, need to take into account country-specific regulations (e.g. permitted levels of pesticide residues), application instructions and financial considerations. The fact that these data are stored in different locations, sometimes using different terminology or different languages, makes it difficult to gather these data and requires that farmers are familiar with the variety of terms used, which consequently hampers the efficiency and effectiveness of the decision process. To overcome these challenges, a Web application for pest control is proposed to facilitate the integration of information coming from different Internet sources and representing different terminologies by using an ontology. The application is based on a pest-control ontology (formal representations of domain knowledge that can be interpreted by computers) that accounts for various pesticide regulations of different countries to which the crop is exported. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision support systems and other intelligent systems, inter alia, in the context of agriculture. The pest-control ontology developed in the current research includes pest-control concepts that have yet to be covered by existing ontologies. It is demonstrated in the specific case of pepper in Israel. The ontology is expressed using Web Ontology Language (OWL) and thus can be shared on the Web and reused by other ontologies and systems. In addition, a comprehensive method for developing and evaluating agricultural ontologies is presented.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Addressing the ‘Tower of Babel’ of pesticide regulations: an ontology for supporting pest-control decisions

et al. 2019

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“…The increasing use of ontologies is also seen in agriculture (e.g. Beck et al 2010;Chang et al 2008;Kragt et al 2016;Li et al 2013;Liao et al 2015;Roussey et al 2010;Song et al 2012;Tomic et al 2015;Zhang et al 2002), where they are used for various purposes, such as agriculture knowledge sharing across farmers around the world (and in different languages) (AGROVOC Thesaurus; Chang et al 2008;Maliappis 2009), creating semantic interoperability of agricultural systems (Aqeel-ur and Zubair 2011;Goumopoulos et al 2009;Tomic et al 2015), and supporting farmer decisions (Gaire et al 2013). This is not surprising, given that agriculture is a knowledge-centric field that covers many areas of expertise, many world-wide used practices and technologies, and includes numerous concepts that are often designated by different names with similar meaning (Liao et al 2015;Palavitsinis and Manouselis 2014) and fragmented across different systems (Janssen et al 2017).…”

Section: For Examplementioning

confidence: 99%

A Framework for Evaluating Agricultural Ontologies

2021

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An ontology is a formal representation of domain knowledge, which can be interpreted by machines. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision-support systems and other intelligent systems, inter alia, in the context of agriculture. A review of the existing literature on agricultural ontologies, however, reveals that most of the studies, which propose agricultural ontologies, are lacking an explicit evaluation procedure. This is undesired because without well-structured evaluation processes, it is difficult to consider the value of ontologies to research and practice. Moreover, it is difficult to rely on such ontologies and share them on the Semantic Web or between semantic-aware applications. With the growing number of ontology-based agricultural systems and the increasing popularity of the Semantic Web, it becomes essential that such evaluation methods are applied during the ontology development process. Our work contributes to the literature on agricultural ontologies by presenting a framework that guides the selection of suitable evaluation methods, which seems to be missing from most existing studies on agricultural ontologies. The framework supports the matching of appropriate evaluation methods for a given ontology based on the ontology’s purpose.

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