FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration

Dooley, Damion; Griffiths, Emma; Gosal, Gurinder S; Buttigieg, Pier Luigi; Hoehndorf, Robert; Lange, Matthew; Schriml, Lynn M.; Brinkman, Fiona S. L.; Hsiao, William W. L.

doi:10.1038/s41538-018-0032-6

Cited by 239 publications

(178 citation statements)

References 31 publications

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“…As we move quickly toward the Internet of Things (IoT) paradigm, advancing food ontology would provide effective communications of food, ingredients, and health outcomes from a semantic view (Boulos, Yassine, Shirmohammadi, Namahoot, & Brückner, 2015). A few examples of food ontologies designed for various purposes are Food-Wiki, AGROVOC, Open Food Facts, Food Product Ontology, and Foodon (Boulos et al, 2015;Dooley et al, 2018). For instance, FoodWiki is a system designed for customers to quickly examine the free text written on packaged food products for inferring their side effects (Çelik, 2015;Ertuğrul, 2016).…”

Section: Food Knowledge Discoverymentioning

confidence: 99%

Utilization of text mining as a big data analysis tool for food science and nutrition

Tao

Yang

Feng

2020

Comp Rev Food Sci Food Safe

166

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Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument‐generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word‐level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer‐opinion mining, new‐product development, food knowledge discovery, food supply‐chain management, and online food services. The goal is to provide insights for intelligent decision‐making to improve food production, food safety, and human nutrition.

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Section: Food Knowledge Discoverymentioning

confidence: 99%

Utilization of text mining as a big data analysis tool for food science and nutrition

Tao

Yang

Feng

2020

Comp Rev Food Sci Food Safe

166

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“…BFO Basic Formal Ontology [19] CHEBI Chemical Entities of Biological Interest [5] DO Disease Ontology [10] GO Gene Ontology [20] OBI Ontology for Biomedical Investigations [21] PATO Phenotypic Quality Ontology [22] PO Plant Ontology [23] XAO Xenopus Anatomy and Development Ontology [24] ZFA Zebrafish Anatomy and Development Ontology [25] intentionally unsatisfiable (and thus not considered an error). In addition, the Ontology of Vaccine 149 Adverse Events (OVAE) [26], Food Ontology (FOODON) [27], Plant Trait Ontology (TO) [28], 150 Gazetteer (GAZ) [29], Porifera (PORO) [30], Plant Experimental Conditions Ontology (PECO) [28], 151 Oral Health and Disease Ontology (OHD) [31], and Statistics Ontology (STATO) [32] became 152 inconsistent.…”

Section: /16mentioning

confidence: 99%

“…explanation can be used to diagnose the cause of the class becoming unsatisfiable. 26 The Open Biomedical Ontologies (OBO) Foundry is a collection of ontologies that use a shared 27 set of design principles, and encourages re-use of terms amongst them [3]. The ontologies are built 28 using the framework provided by common upper-level ontology, the Basic Formal Ontology 29 (BFO) [4], and include many large and widely used domain ontologies describing areas such as 30 chemical entities [5], phenotypes [6], and model organisms [7].…”

Section: Introductionmentioning

confidence: 99%

Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies

Slater

Gkoutos

Hoehndorf

2020

Preprint

Self Cite

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Background Ontologies are widely used throughout the biomedical domain. These ontologies formally represent the classes and relations assumed to exist within a domain. As scientific domains are deeply interlinked, so too are their representations. While individual ontologies can be tested for consistency and coherency using automated reasoning methods, systematically combining ontologies of multiple domains together may reveal previously hidden contradictions.Results We developed a method that tests for hidden unsatisfiabilities in an ontology that arise when combined with other ontologies. For this purpose, we combine sets of ontologies and use automated reasoning to determine whether unsatisfiable classes are present. We test the mutual consistency of the OBO Foundry and the OBO ontologies and find that the combined OBO Foundry gives rise to at least 636 unsatisfiable classes, while the OBO ontologies give rise to more than 300,000 unsatisfiable classes.We design and implement a novel algorithm that can determine justifications for contradictions across extremely large and complicated ontologies, and use these justifications to semi-automatically repair ontologies by identifying the minimal set of axioms that, when removed, result in a consistent and coherent set of ontologies. We applied our algorithm to each combination of OBO ontologies that resulted in unsatisfiable classes.Conclusions We identified a large set of hidden unsatisfiability across a broad range of biomedical ontologies, and we find that this large set of unsatisfiable classes is the result of a relatively small amount of axiomatic disagreements. Our results show that hidden unsatisfiability is a serious problem in ontology interoperability; however, our results also provide a way towards more consistent ontologies by addressing the issues we identified. 2Ontologies use classes to characterise the kinds of things that exist within a domain as well as 3 1/16 axioms that provide constraints for these classes and conditions that must be satisfied within the 4 domain. Most ontologies in biology are domain-specific and focus on a single domain. Creating 5 ontologies that reference and extend other biomedical ontologies is common practice, as it promotes 6 a unified understanding of the biomedical domain by defining terms and groups of terms in the 7 context of their relationships with classes from related domains, and in the common context of 8 higher level domains. Reusing the formalised knowledge from other domain ontologies also enables 9 the reuse of expertise from ontology developers in other domains. 10The majority of biomedical ontologies are now being developed in the Web Ontology Language 11 (OWL) [1], a formal model-theoretic language based on description logics [2]. OWL ontologies enable 12 the use of automated reasoners, which in turn enable the deductive inference of knowledge implied 13 by the explicit assertions made in the ontologies. Furthermore, these inferences can be examined to 14 determine whether an ontology's classes are sati...

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“…We used the annotator provided by AgroPortal [31] for the annotation step. This annotator uses more than 100 ontologies covering domains like agricultural research, animal science, ecology, nutrition or farming, among others, including ontologies such as Global Agricultural Concept Scheme (gacs) [32], Thesaurus for Animal Physiology and Livestock systems (TriPhase) [33], Environment Ontology (envo) [34], FoodOn [35] or AgroRDF [36]. These ontologies were obtained through the AgroPortal REST API and, when possible, converted to RDF/XML format with the ROBOT tool [37] in order to insert them into the knowledge graph.…”

Section: Description Of the Experimentsmentioning

confidence: 99%

Semantic Publication of Agricultural Scientific Literature Using Property Graphs

Abad-Navarro

Bernabé-Díaz

Castro³

et al. 2020

Applied Sciences

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During the last decades, there have been significant changes in science that have provoked a big increase in the number of articles published every year. This increment implies a new difficulty for scientists, who have to do an extra effort for selecting literature relevant for their activity. In this work, we present a pipeline for the generation of scientific literature knowledge graphs in the agriculture domain. The pipeline combines Semantic Web and natural language processing technologies, which make data understandable by computer agents, empowering the development of final user applications for literature searches. This workflow consists of (1) RDF generation, including metadata and contents; (2) semantic annotation of the content; and (3) property graph population by adding domain knowledge from ontologies, in addition to the previously generated RDF data describing the articles. This pipeline was applied to a set of 127 agriculture articles, generating a knowledge graph implemented in Neo4j, publicly available on Docker. The potential of our model is illustrated through a series of queries and use cases, which not only include queries about authors or references but also deal with article similarity or clustering based on semantic annotation, which is facilitated by the inclusion of domain ontologies in the graph.

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FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration

Cited by 239 publications

References 31 publications

Utilization of text mining as a big data analysis tool for food science and nutrition

Utilization of text mining as a big data analysis tool for food science and nutrition

Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies

Semantic Publication of Agricultural Scientific Literature Using Property Graphs

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