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PO^2$$ - A Process and Observation Ontology in Food Science. Application to Dairy Gels
Abstract
This paper focuses on the knowledge representation task for an interdisciplinary project called Delicious concerning the production and transformation processes in food science. The originality of this project is to combine data from different disciplines like food composition, food structure, sensorial perception and nutrition. Available data sets are described using different vocabularies and are stored in different formats. Therefore there is a need to define an ontology, called \(PO^2\) (Process and Observation Ontology), as a common and standardized vocabulary for this project. The scenario 6 of the NeON methodology was used for building \(PO^2\) and the core component is implemented in OWL. By making use of \(PO^2\), data from the project were structured and an use case is presented here. \(PO^2\) aims to play a key role as the representation layer of the querying and simulation systems of Delicious project.
... The objective is to collect and formalize scientific knowledge from experts or bibliography to construct an informative system. An example is the construction of a Process and Observation Ontology (PO²) applied to dairy gels (Ibanescu et al., 2016). The aim of this ontology was to provide a consensual structure representative of the production and transformation of dairy gels and to solve the lack of communication between domain experts because data were gathered for different purposes by different experts with their own experimental itineraries, vocabularies, and methods. ...
... Ontology has gained much importance not only in the field of artificial intelligence, but also in the fields of natural language processing (Medjkoune et al., 2016) or knowledge representation and acquisition (Ibanescu et al., 2016). ...
... A highly simplified example of an ontology for wines is graphically presented in Figure Ontologies define a common vocabulary for researchers who need to share information in a domain, for instance dairy gels (Ibanescu et al., 2016). Thus, they are valuable in sharing a common understanding of the structure of information among people or software agents. ...
Among the sensory dimensions involved in food flavor, the odor component is critical because it often determines the identity and the typicality of the food. Chemical flavor analysis provides a list of the odorants contained in a food product but is not sufficient to predict the odor resulting from their mixture. Indeed, odor perception relies on the processing by the olfactory system of many odorants embedded in complex mixtures and several perceptual interactions can occur. Thus, the prediction of the perceptual outcome of a complex odor mixture remains challenging and two main approaches emerge from the literature review. On the one hand, predictive approaches based on the molecular structure of odorants have been proposed but have been limited to single odorants only. On the other hand, methodologies relying on recombination strategies after the chemical analyses of flavor have been successfully applied to identify those odorants that are key to the food odor. However, the choices of odorants to be recombined are mostly based on empirical approaches. Thus, two questions arise: How can we predict the odor quality of a mixture on the basis of the molecular structure of its odorants? How can we improve food flavor analysis in order to predict the odor of a food containing several tens of odorants? These two questions are at the basis of the thesis and of this manuscript which is divided in two main axes.The first axis describes the development of a model based on the concept of angle distances computed from the molecular structure of odorants in order to predict the odor similarity between mixtures. The results highlight the importance of taking into account the odor intensity dimension to reach a good prediction level. Moreover, several perspectives are proposed to extend the model prediction beyond the similarity dimension and to predict more qualitative dimensions of odors.The second axis presents an innovative strategy which allows integrating experts’ knowledge in the flavor analysis procedure. Three different types of heterogeneous data are embedded in a mathematical model: chemical data, sensory data and knowledge from expert flavorists. Experts’ knowledge is integrated owing to the development of an ontology, which is further used to define fuzzy rules optimized by evolutionary algorithms. The final output of the model is the prediction of red wines’ odor profile on the basis of their odorants’ composition. Overall, the thesis work brings original results allowing a better understanding of food odor construction and gives insights on the underlying relationships within the odor perceptual space for complex mixtures.
... This new ontology is a specialization of a core model, Process and Observation Ontology (PO2) 17,18 . The PO2 core model was designed to represent a generic process described by a set of steps and experimental observations available for the input and output components of each step of a process. ...
... The Process and Observation ontology (PO2) is the core model of the domain ontology PO2/TransformON. PO2 is dedicated to the generic modeling of both transformation processes and characterization processes 15,17,18 . PO2 core model reuses various existing ontologies such as SOSA/SSN (https://www.w3.org/TR/ vocab-ssn/), Time Ontology (https://www.w3.org/TR/owl-time/), and QUDT (https://qudt.org/) and is also situated within the BFO hierarchy (https://basic-formal-ontology.org/bfo-2020.html). ...
We are witnessing an acceleration of the global drive to converge consumption and production patterns towards a more circular and sustainable approach to the food system. To address the challenge of reconnecting agriculture, environment, food and health, collections of large datasets must be exploited. However, building high-capacity data-sharing networks means unlocking the information silos that are caused by a multiplicity of local data dictionaries. To solve the data harmonization problem, we proposed an ontology on food, feed, bioproducts, and biowastes engineering for data integration in a circular bioeconomy and nexus-oriented approach. This ontology is based on a core model representing a generic process, the Process and Observation Ontology (PO2), which has been specialized to provide the vocabulary necessary to describe any biomass transformation process and to characterize the food, bioproducts, and wastes derived from these processes. Much of this vocabulary comes from transforming authoritative references such as the European food classification system (FoodEx2), the European Waste Catalogue, and other international nomenclatures into a semantic, world wide web consortium (W3C) format that provides system interoperability and software-driven intelligence. We showed the relevance of this new domain ontology PO2/TransformON through several concrete use cases in the fields of process engineering, bio-based composite making, food ecodesign, and relations with consumer’s perception and preferences. Further works will aim to align with other ontologies to create an ontology network for bridging the gap between upstream and downstream processes in the food system.
... With this objective, our collaborative network gathering scientists in food process, oral physiology and sensory perception, ecodesign and computer science, built a first ontology for the ecodesign of transformation processes (Dibie, Dervaux, Doriot, Ibanescu, & P enicaud, 2016). This ontology has been extended to PO 2 , a process and observation ontology in food science (available at http://agroportal.lirmm.fr/ontologies/PO2/), to integrate data for the formulation of dairy products, taking into account nutritional and sensory properties (Ibanescu, Dibie, Dervaux, Guichard, & Raad, 2016). A database, called BaGaTel, structured by the PO 2 ontology has been built, hosted by the PLASTIC platform of INRA (http://www.pfl-cepia.inra.fr). ...
... Ontology is designed in two layers: a core layer and a domain layer. The core layer is composed of three main parts, presented in Fig. 1 (adapted from Ibanescu et al., 2016).The domain layer allows one to represent a specific domain, here the hard cheese production process. ...
To provide answers to sustainability challenges, a database called BaGaTel, guided by the PO² ontology, has been built to integrate data to reformulate dairy products taking into account nutritional and sensory properties together with environmental concerns. In this paper, BaGaTel was queried to address questions dealing with the eco-design of hard cheese processing, in relation to composition, sensory quality and rheological properties. For the formulation of hard cheese, BaGaTel made it possible to estimate missing data in a dataset supposing that samples have common characteristics. For environmental concerns, BaGaTel gave hints about relevant data that need to be acquired and made possible the estimation of missing data. The common vocabulary and structure provided by the PO² ontology allowed combining and integrating into BaGaTel data from different projects, giving relevant answers to different questions, and therefore proving its suitability as a support tool for multi-criteria assessment of food systems.
... Cet algorithme peut être guidé par un ensemble de contraintes sémantiques définies par les experts du domaine. Nous avons évalué notre approche sur des données scientifiques provenant des deux projets, CellExtraDry et CARéDAS, dont les données sont décrites dans le vocabulaire de l'ontologie PO 2 (Ibanescu et al., 2016). ...
... sel, composés d'arôme). Pour modéliser ces données, nous avons utilisé la version 1.5 15 de l'ontologie P O 2 -Process and Observation Ontology (Ibanescu et al., 2016). L'ontologie P O 2 permet de représenter des processus des transformations. ...
... Cet algorithme peut être guidé par un ensemble de contraintes sémantiques définies par les experts du domaine. Nous avons évalué notre approche sur des données scientifiques provenant des deux projets, CellExtraDry et CARéDAS, dont les données sont décrites dans le vocabulaire de l'ontologie PO 2 (Ibanescu et al., 2016). ...
... sel, composés d'arôme). Pour modéliser ces données, nous avons utilisé la version 1.5 15 de l'ontologie P O 2 -Process and Observation Ontology (Ibanescu et al., 2016). L'ontologie P O 2 permet de représenter des processus des transformations. ...
Most of the Linked Data applications currently rely on the use of owl:sameAs for linking ontology instances. However, several studies have noticed multiple misuses of this identity link, which can lead to erroneous statements or inconsistencies. We propose in this paper a new contextual identity link, that could serve as a replacement in linking identical instances in a specified context. To detect these contextual links, we have defined an algorithm named DECIDE, which has been tested on scientific knowledge bases from several INRA projects.
... PO2 can represent a food transformation process described by a set of experimental observations available at different scales and evolving in time through the different unit operations of a production process. The initial model called MS2O (Multi-scale Multi-step Ontology) was built to represent a bioprocess of yeast production and stabilization [14] and further developed to fit the need of dairy gel manufacturing and the integration of heterogeneous data [23,44]. Finally, the core model of PO2 was reengineered with SOSA/SSN to be able to adapt to any transformation process. ...
People often value the sensual, celebratory, and health aspects of food, but behind this experience exists many other value-laden agricultural production, distribution, manufacturing, and physiological processes that support or undermine a healthy population and a sustainable future. The complexity of such processes is evident in both every-day food preparation of recipes and in industrial food manufacturing, packaging and storage, each of which depends critically on human or machine agents, chemical or organismal ingredient references, and the explicit instructions and implicit procedures held in formulations or recipes. An integrated ontology landscape does not yet exist to cover all the entities at work in this farm to fork journey. It seems necessary to construct such a vision by reusing expert-curated fit-to-purpose ontology subdomains and their relationship, material, and more abstract organization and role entities. The challenge is to make this merger be, by analogy, one language, rather than nouns and verbs from a dozen or more dialects which cannot be used directly in statements about some aspect of the farm to fork journey without expensive translation or substantial dialect education in order to understand a particular text or domain of knowledge. This work focuses on the ontology components – object and data properties and annotations – needed to model food processes or more general process modelling within the context of the Open Biological and Biomedical Ontology Foundry and congruent ontologies. Ideally these components can be brought together in a general process ontology that can be specialized not only for the food domain but for carrying out other protocols as well. Many operations involved in food identification, preparation, transportation and storage – shaking, boiling, mixing, freezing, labeling, shipping – are actually common to activities from manufacturing and laboratory work to local or home food preparation.
... The integration of data and knowledge from different domains in food science (e.g. nutrition, food digestion, sensory and perception, eco-design, microbiology, biochemistry, process engineering) with data and knowledge in environmental analysis is difficult due to the heterogeneity of data sources in terms of formats and vocabularies used by domain experts (Ibanescu et al., 2018;Ibanescu et al., 2016). Process and Observation Ontology (PO2) has been designed to model the food transformation processes and their observations. ...
The food sector is driven by a large number of actors, including primary producers, manufacturers, logistics providers, retailers, and consumers. At each phase of the food value chain, a significant amount of data is generated that provides important information to the agents involved in processing and flow of food products from farm to fork. Proper handling of food data has a crucial role in providing safe, quality and affordable products to the increasing world population. The independent production of food data, without following any specific guidelines and procedures, often results in inconsistent and incomparable datasets that cannot be directly utilised by multiple users. Data harmonisation means reconciling various types, levels and sources of data in formats that are compatible and comparable, and thus useful for better decision making. In the food sector, one way of performing data harmonisation is to represent food data according to reliable classification and description systems. Another approach towards harmonisation is to match various food concepts to the existing and widely used ontologies. Furthermore, harmonisation is facilitated by following specific guidelines and procedures during data collection processes. This study explores some of the most important tools, frameworks and methodologies for data harmonisation in the food sector.
... Pour illustrer notre approche, nous utilisons l'ontologie Process and Observation Ontology (PO 2 ), écrite en OWL 2 1 et dédiée à la représentation des procédé de transformation (Ibanescu et al., 2016). Un procédé de transformation y est représenté comme un succession d'étapes, avec plusieurs participants et observations. ...
... • Contextually linked knowledge graphs for life sciences [Ibanescu et al., 2016, Raad et al., 2018c. Cases in which objects can not be declared the same are quite common in scientific data, where experiments are mostly conducted by several scientists, in various circumstances, using similar but different products. ...
In the absence of a central naming authority in the Web of Data, it is common for different knowledge graphs to refer to the same thing by different names (IRIs). Whenever multiple names are used to denote the same thing, owl:sameAs statements are needed in order to link the data and foster reuse. Such identity statements have strict logical semantics, indicating that every property asserted to one name, will also be inferred to the other, and vice versa. While such inferences can be extremely useful in enabling and enhancing knowledge-based systems such as search engines and recommendation systems, incorrect use of identity can have wide-ranging effects in a global knowledge space like the Web of Data. With several studies showing that owl:sameAs is indeed misused for different reasons, a proper approach towards the handling of identity links is required in order to make the Web of Data succeed as an integrated knowledge space. By relying on a collection of 558 million identity statements, this thesis shows how network metrics such as the community structure of the owl:sameAs graph can be used in order to detect possibly erroneous identity assertions. In addition, as a way to limit the excessive and incorrect use of owl:sameAs, we define a new relation for asserting the identity of two class instances in a specific context. This identity relation is accompanied by an approach for automatically detecting these links, with the ability of using certain expert constraints for filtering irrelevant contexts. As a first experiment, the detection and exploitation of the detected contextual identity links are conducted on a knowledge graph for life sciences, constructed in the context of this thesis in a collaboration with experts from the French National Institute of Agricultural Research (INRA).
... Cette ontologie, appelée PO 2 , a été élaborée avec l'aide d'experts issus de domaines de recherche variés, allant de la production à la consommation des produits laitiers. Elle est composée de trois parties principales (Ibanescu et al., 2016). La première partie concerne le procédé de fabrication qui contient les concepts : procédé, itinéraire et étape. ...
L’objectif de cette étude est de permettre la mise en relation de données hétérogènes, issues de projets divers grâce à leur implémentation dans une base de données appelée BaGaTel, structurée selon une ontologie. L’exemple choisi pour cette mise en relation de données est le fromage, dont les différents paramètres et spécificités ont été renseignés dans l’ontologie.
... fr/PortailNutriSensAl/). It was built using the PO 2 ontology [39]. PO 2 is a food science process and observation ontology dedicated to the eco-design of transformation processes and nutritional and sensory properties in the field of dairy products [33,[35][36][37][40][41][42][43]. ...
Multi-criteria reverse engineering (MRE) has arisen from the cross-fertilization of advances in mathematics and shifts in social demand. MRE, thus, marks a progressive switch (a) from empirical to formal approaches able to simultaneously factor in diverse parameters, such as environment, economics, and health; (b) from mono-criterion optimization to multi-criteria decision analysis; (c) from forward engineering, observing the results of process conditions, to reverse engineering, selecting the right process conditions for a target output. The food sector has been slow to adopt reverse engineering, but interest is surging now that the industry is looking to shift production towards personalized food. MRE has followed a heterogeneous development trajectory and found applications in different disciplines. The scope of this review spans MRE applications in the food sector covering food packaging and food consumption and focuses on demonstrating potentialities of MRE in a complex field like food. We explain how MRE enables the development of sustainable processes, looking at similar approaches used in sectors other than food. Building on this extensive review, we sketch out some guidelines on approaches to be used in future MRE applications in food, working up from the problem statement.
... Our approach has been evaluated on two scientific datasets exploited using the 1.4 version 5 of the ontology PO 2 [17], which aims at modelling transformation processes. Each process can be conducted over several itineraries, with each itinerary representing a sequence of transformation steps (drying, heating, etc.). ...
Most of the Linked Data applications currently rely on the use of owl : sameAs for linking ontology instances. However, several studies have noticed multiple misuses of this identity link. These misuses, which are mainly caused by the lack of other well-defined linking alternatives, can lead to erroneous statements or inconsistencies. We propose in this paper a new contextual identity link: identiConTo that could serve as a replacement for owl : sameAs in linking identical instances in a specified context. To detect these contextual links, we have defined an algorithm named DECIDE that has been tested on scientific knowledge bases describing transformation processes.
... To illustrate our approach, we propose to use the Process and Observation Ontology (PO 2 ) [9], written in OWL 2, designed to represent transformation processes. A transformation process is denoted as a sequence of steps (i.e. ...
Probabilistic Relational Models (PRMs) extend Bayesian networks (BNs) with the notion of class of relational databases. Because of their richness, learning them is a difficult task. In this paper, we propose a method that learns a PRM from data using the semantic knowledge of an ontology describing these data in order to make the learning easier. To present our approach, we describe an implementation based on an ontology of transformation processes and compare its performance to that of a method that learns a PRM directly from data. We show that, even with small datasets, our approach of learning a PRM using an ontology is more efficient.
... Furthermore, [MS] 2 O has been re-engineered to make it applicable to other domains, as long as they are devoted to transformation processes, but independently of the product submitted to the transformation. The new PO 2 ontology (Process and Observation Ontology) has been successfully applied to dairy gels (Ibanescu, Dibie, Dervaux, Guichard, & Raad, 2016). These generic knowledge integration approaches are anticipated to drive the development of bio-based products according to nutritional and/or functional properties while increasing efficiency within a wider eco-design approach. ...
This article presents an overview of five use-cases that illustrate food-system modeling approaches, most of them combining quantitative and qualitative methods. The cases deal with issues as diverse as: the modeling of winemaking fermentation; the choice of a food packaging material; the design of solid foam cereal products; argumentation modeling to support multistakeholder decision-making, and the control of freeze-drying for eco-design purposes. They tackle several challenges to modeling and knowledge engineering in food systems: capitalizing data and knowledge; structuring a shared vocabulary and domain knowledge in an ontology; formalizing viewpoints and contradictions from food-related debates; supporting multi-criteria decision-making.
... Notre approche a été évaluée sur des données scientifiques relatives au domaine de l'agroalimentaire. Nous avons exploité la version 1.4 de l'ontologie P O 2 (Ibanescu et al., 2016 ...
De nombreuses applications du Web de données exploitent des liens d'identités déclarés à l'aide du constructeur owl :sameAs. Cependant, différentes études ont montré qu'une utilisation abusive de ces liens peut conduire à des inférences erronées ou contradictoires. Dans ce papier nous proposons de calculer des liens d'identités contextuels qui permettent d'expliciter les contextes dans lesquels ces liens sont valides. La notion de contexte que nous proposons est représentée en se basant sur l'ontologie de domaine dans laquelle les instances sont représentées. Nous avons expérimenté cette approche dans le domaine des données scientifiques où les éléments décrivant les expériences partagent rarement un lien d'identité tel que défini par owl :sameAs.
The deployment of various networks (e.g., Internet of Things [IoT] and mobile networks), databases (e.g., nutrition tables and food compositional databases), and social media (e.g., Instagram and Twitter) generates huge amounts of food data, which present researchers with an unprecedented opportunity to study various problems and applications in food science and industry via data-driven computational methods. However, these multi-source heterogeneous food data appear as information silos, leading to difficulty in fully exploiting these food data. The knowledge graph provides a unified and standardized conceptual terminology in a structured form, and thus can effectively organize these food data to benefit various applications. In this review, we provide a brief introduction to knowledge graphs and the evolution of food knowledge organization mainly from food ontology to food knowledge graphs. We then summarize seven representative applications of food knowledge graphs, such as new recipe development, diet-disease correlation discovery, and personalized dietary recommendation. We also discuss future directions in this field, such as multimodal food knowledge graph construction and food knowledge graphs for human health.
Designing new processes for bio-based and biodegradable food packaging is an environmental and economic challenge. Due to the multiplicity of the parameters, such an issue requires an approach that proposes both (1) to integrate heterogeneous data sources and (2) to allow causal reasoning. In this article, we present POND (Process and observation ONtology Discovery), a workflow dedicated to answering expert queries on domains modeled by the Process and Observation Ontology (PO\(^2\)). The presentation is illustrated with a real-world application on bio-composites for food packaging to solve a reverse engineering problem, using a novel dataset composed of data from different projects.KeywordsOntologyProbabilistic modelCausalityFood packaging
The BaGaTel database, guided by an ontology of dairy gels, has been developed to organise and integrate data on dairy products (process, composition, structure, nutritional, sensory and environmental quality), using a common vocabulary and metadata organisation. BaGaTel was queried to explore relationships between composition, rheological properties and sensory perception in 68 model cheeses from six different projects. Principal component analyses were performed on the total set of 68 samples and on sample subsets. Sensory hardness was well explained by the low moisture-in-non-fat-substances ratio. As expected, salty taste was correlated with salt content but, interestingly, in cheese with a low amount of salt, salty taste was less intense at low water content and was perceived better with increased chewing activity. In cheeses with a high amount of salt, salty test was less intense at high protein content. Salty taste was also influenced by lipid content and correlated with fat perception.
In many agri-food companies, food quality is often managed using expertise gained through experience. Overall quality enhancement may come from sharing collective expertise. In this paper, we describe the design and implementation of a complete methodology allowing an expert knowledge base to be created and used to recommend the technical action to take to maintain food quality. We present its functional specifications, defined in cooperation with several industrial partners and technical centres over the course of several projects carried out in recent years. We propose a systematic methodology for collecting the knowledge on a given food process, from the design of a questionnaire to the synthesis of the information from completed questionnaires using a mind map approach. We then propose an original core ontology for structuring knowledge as possible causal relationships between situations of interest. We describe how mind map files generated by mind map tools are automatically imported into a conceptual graph knowledge base, before being validated and finally automatically processed in a graph-based visual tool. A specific end-user interface has been designed to ensure that end-user experts in agri-food companies can use the tool in a convenient way. Finally, our approach is compared with current research.
In this paper we describe a formal ontological description of the Business Process Modelling Notation (BPMN), one of the most popular languages for business process modelling. The proposed ontology (the BPMN Ontology) provides a classification of all the elements of BPMN, together with the formal description of the attributes and conditions describing how the elements can be combined in a BPMN business process description. Using the classes and properties defined in the BPMN Ontology any BPMN diagram can be represented as an A-box (i.e., a set of instances and assertions on them) of the ontology: this allows the exploitation of ontological reasoning services such as consistency checking and query answering to investigate the compliance of a process with the BPMN Specification as well as other structural property of the process. The paper also presents the modelling process followed for the creation of the BPMN Ontology, and describes some application scenarios exploiting the BPMN Ontology.
This paper describes an ontology for modeling any food processing chain. It is intended for data and knowledge integration and sharing. The proposed ontology (Onto-FP) is built based on four main concepts: Product, Operation, Attribute and Observation. This ontology is able to represent food product transformations as well as temporal sequence of food processes. The Onto-FP can be easy integrated to other domains due to its consistencies with DOLCE ontology. We detail an application in the domain of winemaking and prove that it can be easy queried to answer questions related to data classification, food process itineraries and incomplete data identification.
For human beings, the mouth is the first organ to perceive food and the different signalling events associated to food breakdown. These events are very complex and as such, their description necessitates combining different data sets. This study proposed an integrated approach to understand the relative contribution of main food oral processing events involved in aroma release during cheese consumption. In vivo aroma release was monitored on forty eight subjects who were asked to eat four different model cheeses varying in fat content and firmness and flavoured with ethyl propanoate and nonan-2-one. A multiblock partial least square regression was performed to explain aroma release from the different physiological data sets (masticatory behaviour, bolus rheology, saliva composition and flux, mouth coating and bolus moistening). This statistical approach was relevant to point out that aroma release was mostly explained by masticatory behaviour whatever the cheese and the aroma, with a specific influence of mean amplitude on aroma release after swallowing. Aroma release from the firmer cheeses was explained mainly by bolus rheology. The persistence of hydrophobic compounds in the breath was mainly explained by bolus spreadability, in close relation with bolus moistening. Resting saliva poorly contributed to the analysis whereas the composition of stimulated saliva was negatively correlated with aroma release and mostly for soft cheeses, when significant.
The word “ontology” is used with different senses in different communities. The most radical difference is perhaps between
the philosophical sense, which has of course a well-established tradition, and the computational sense, which emerged in the
recent years in the knowledge engineering community, starting from an early informal definition of (computational) ontologies
as “explicit specifications of conceptualizations”. In this paper we shall revisit the previous attempts to clarify and formalize
such original definition, providing a detailed account of the notions of conceptualization and explicit specification, while discussing at the same time the importance of shared explicit specifications.
This paper focuses on the knowledge representation for an interdisciplinary project concerning transformation processes in food science. The use case concerns the production of stabilized micro-organisms performed at INRA (French National Institute for Agricultural Research). Experimental observations are available for some inputs of the production processes, at different steps and at a certain scale. Available data sets are described using different vocabularies and are stored in different formats. Therefore there is a need to define an ontology, called \([MS]^2O\), as a common and standardized vocabulary. Users’ requirements were defined through competency questions and the ontology was validated against these competency questions. \([MS]^2O\) ontology aims to play a key role as the representation layer of the querying and simulation systems of the project. This leads to the possibility of comparing different production scenarios and suggesting improvements.
How do you approach answering queries when your data is stored in multiple databases that were designed independently by different people? This is first comprehensive book on data integration and is written by three of the most respected experts in the field. This book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. Data integration is the problem of answering queries that span multiple data sources (e.g., databases, web pages). Data integration problems surface in multiple contexts, including enterprise information integration, query processing on the Web, coordination between government agencies and collaboration between scientists. In some cases, data integration is the key bottleneck to making progress in a field. The authors provide a working knowledge of data integration concepts and techniques, giving you the tools you need to develop a complete and concise package of algorithms and applications. * Offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. *Enables you to build your own algorithms and implement your own data integration applications *Companion website with numerous project-based exercises and solutions. Video interview(s) and video lectures from authors. Links to commercially available software allowing readers to build their own algorithms and implement their own data integration applications. Facebook page for reader input during and after publication.
In contrast to other approaches that provide methodological guidance for ontology engineering, the NeOn Methodology does not prescribe a rigid workflow, but instead it suggests a variety of pathways for developing ontologies. The nine scenarios proposed in the methodology cover commonly occurring situations, for example, when available ontologies need to be re-engineered, aligned, modularized, localized to support different languages and cultures, and integrated with ontology design patterns and non-ontological resources, such as folksonomies or thesauri. In addition, the NeOn Methodology framework provides (a) a glossary of processes and activities involved in the development of ontologies, (b) two ontology life cycle models, and (c) a set of methodological guidelines for different processes and activities, which are described (a) functionally, in terms of goals, inputs, outputs, and relevant constraints; (b) procedurally, by means of workflow specifications; and (c) empirically, through a set of illustrative examples.
In an environment where supply chains compete against supply chains, information sharing among supply chain partners using information systems is a competitive tool. Supply chain ontology has been proposed as an important medium for attaining information systems interoperability. Ontology has its origin in philosophy, and the computing community has adopted ontology in its language. This paper presents a study of state of the art research in supply chain ontology and identifies the outstanding research gaps. Six supply chain ontology models were identified from a systematic review of literature. A seven point comparison framework was developed to consider the underlying concepts as well as application of the ontology models. The comparison results were then synthesised into nine gaps to inform future supply chain ontology research. This work is a rigorous and systematic attempt to identify and synthesise the research in supply chain ontology.
MS]ˆ2O -A multiscale and multi-step ontology for transformation processes: Application to microorganisms
- J Dibie
- S Dervaux
- E Doriot
- L Ibanescu
- C Pénicaud
Dibie, J., Dervaux, S., Doriot, E., Ibanescu, L., Pénicaud, C.: [MS]ˆ2O -A multiscale and multi-step ontology for transformation processes: Application to microorganisms. In: Haemmerlé, O., Stapleton, G., Faron-Zucker, C. (eds.) Graph-Based
Representation and Reasoning -22nd International Conference on Conceptual
Structures, ICCS 2016, Annecy, France, July 5-7, 2016, Proceedings. Lecture Notes
in Computer Science, vol. 9717, pp. 163-176. Springer (2016)
Ontology-based model for food transformation processes -application to winemaking
- A Muljarto
- J Salmon
- P Neveu
- B Charnomordic
- P Buche
- S Closs
- R Studer
- E Garoufallou
Muljarto, A., Salmon, J., Neveu, P., Charnomordic, B., Buche, P.: Ontology-based
model for food transformation processes -application to winemaking. In: Closs, S.,
Studer, R., Garoufallou, E., Sicilia, M. (eds.) Metadata and Semantics Research -8th Research Conference, MTSR 2014, Karlsruhe, Germany, November 27-29, 2014.
Proceedings. Communications in Computer and Information Science, vol. 478, pp.
329-343. Springer (2014)
Principles of Data Integration
- A Doan
- A Y Halevy
- Z G Ives