The Experts below are selected from a list of 29532 Experts worldwide ranked by ideXlab platform

Joel Abecassis - One of the best experts on this subject based on the ideXlab platform.

  • An iterative approach to build relevant Ontology-aware data-driven models
    Information Sciences, 2013
    Co-Authors: Rallou Thomopoulos, Sébastien Destercke, Brigitte Charnomordic, Johnson Iyan, Joel Abecassis
    Abstract:

    In many fields involving complex environments or living organisms, data-driven models are useful to make simulations in order to extrapolate costly experiments and to Design decision-support tools. Learning methods can be used to build interpretable models from data. However, to be really useful, such models must be trusted by their users. From this perspective, the domain expert knowledge can be collected and modelled to help guiding the learning process and to increase the confidence in the resulting models, as well as their relevance. Another issue is to Design relevant ontologies to formalize complex knowledge. Interpretable predictive models can help in this matter. In this paper, we propose a generic iterative approach to Design Ontology-aware and relevant data-driven models. It is based upon an Ontology to model the domain knowledge and a learning method to build the interpretable models (decision trees in this paper). Subjective and objective evaluations are both involved in the process. A case study in the domain of Food Industry demonstrates the interest of this approach.

  • An iterative approach to build relevant Ontology-aware data-driven models - application to food processes
    2012
    Co-Authors: Rallou Thomopoulos, Sébastien Destercke, Brigitte Charnomordic, Joel Abecassis
    Abstract:

    In experimental Life Sciences, simulations are needed in order to extrapolate costly experiments and to Design decision-support tools. When extensive mathematical knowledge is not available at the desired scale, expertise and data-driven models can be used as a basis for these simulations. We propose a generic iterative approach to Design Ontology-aware and relevant data-driven models. It is based upon an Ontology to model the domain knowledge, and it uses decision trees as the learning method.

  • An iterative approach to build relevant Ontology-aware data-driven models - application to food processes
    2012
    Co-Authors: Rallou Thomopoulos, Sébastien Destercke, Brigitte Charnomordic, Joel Abecassis
    Abstract:

    In experimental Life Sciences, simulations are needed in order to extrapolate costly experiments and to Design decision-support tools. When extensive mathematical knowledge is not available at the desired scale, expertise and data-driven models can be used as a basis for these simulations. Decision tree algorithms are efficient approaches for data-driven discovery of complex non obvious relationships. Their readability and the absence of a priori assumptions make them particularly useful for variable selection in highly multidimensional problems, therefore they are ideal to display statistically important variables on which the expert should focus. However, to be really useful, such models must be trusted by their users. From this perspective, the domain expert knowledge can be collected and modelled to help guiding the learning process and to increase the confidence in the resulting models, as well as their relevance. We propose a generic iterative approach to Design Ontology-aware and relevant data-driven models. It is based upon an Ontology to model the domain knowledge, and it uses decision trees as the learning method. Relationships between concepts from the Ontology and variables from the data sets are formalized and various data processing techniques are presented to build more significant variables from the original ones, exploiting both the Ontology and expert feedback. Subjective and objective evaluations are both involved in the process. Starting from an initial data set, an initial data-driven model is learnt (step 1). This model is first evaluated with numerical criteria, and submitted to domain experts, who may enrich the Ontology by suggesting new relations between some variables (step 2). Data transformations are applied according to these new relations (step 3). The whole process is repeated iteratively. A case study concerning the impact of agri-food transformation processes on the nutritional quality of wheat-based products is presented to demonstrate the interest of this approach.

Rallou Thomopoulos - One of the best experts on this subject based on the ideXlab platform.

  • An iterative approach to build relevant Ontology-aware data-driven models
    Information Sciences, 2013
    Co-Authors: Rallou Thomopoulos, Sébastien Destercke, Brigitte Charnomordic, Johnson Iyan, Joel Abecassis
    Abstract:

    In many fields involving complex environments or living organisms, data-driven models are useful to make simulations in order to extrapolate costly experiments and to Design decision-support tools. Learning methods can be used to build interpretable models from data. However, to be really useful, such models must be trusted by their users. From this perspective, the domain expert knowledge can be collected and modelled to help guiding the learning process and to increase the confidence in the resulting models, as well as their relevance. Another issue is to Design relevant ontologies to formalize complex knowledge. Interpretable predictive models can help in this matter. In this paper, we propose a generic iterative approach to Design Ontology-aware and relevant data-driven models. It is based upon an Ontology to model the domain knowledge and a learning method to build the interpretable models (decision trees in this paper). Subjective and objective evaluations are both involved in the process. A case study in the domain of Food Industry demonstrates the interest of this approach.

  • An iterative approach to build relevant Ontology-aware data-driven models - application to food processes
    2012
    Co-Authors: Rallou Thomopoulos, Sébastien Destercke, Brigitte Charnomordic, Joel Abecassis
    Abstract:

    In experimental Life Sciences, simulations are needed in order to extrapolate costly experiments and to Design decision-support tools. When extensive mathematical knowledge is not available at the desired scale, expertise and data-driven models can be used as a basis for these simulations. We propose a generic iterative approach to Design Ontology-aware and relevant data-driven models. It is based upon an Ontology to model the domain knowledge, and it uses decision trees as the learning method.

  • An iterative approach to build relevant Ontology-aware data-driven models - application to food processes
    2012
    Co-Authors: Rallou Thomopoulos, Sébastien Destercke, Brigitte Charnomordic, Joel Abecassis
    Abstract:

    In experimental Life Sciences, simulations are needed in order to extrapolate costly experiments and to Design decision-support tools. When extensive mathematical knowledge is not available at the desired scale, expertise and data-driven models can be used as a basis for these simulations. Decision tree algorithms are efficient approaches for data-driven discovery of complex non obvious relationships. Their readability and the absence of a priori assumptions make them particularly useful for variable selection in highly multidimensional problems, therefore they are ideal to display statistically important variables on which the expert should focus. However, to be really useful, such models must be trusted by their users. From this perspective, the domain expert knowledge can be collected and modelled to help guiding the learning process and to increase the confidence in the resulting models, as well as their relevance. We propose a generic iterative approach to Design Ontology-aware and relevant data-driven models. It is based upon an Ontology to model the domain knowledge, and it uses decision trees as the learning method. Relationships between concepts from the Ontology and variables from the data sets are formalized and various data processing techniques are presented to build more significant variables from the original ones, exploiting both the Ontology and expert feedback. Subjective and objective evaluations are both involved in the process. Starting from an initial data set, an initial data-driven model is learnt (step 1). This model is first evaluated with numerical criteria, and submitted to domain experts, who may enrich the Ontology by suggesting new relations between some variables (step 2). Data transformations are applied according to these new relations (step 3). The whole process is repeated iteratively. A case study concerning the impact of agri-food transformation processes on the nutritional quality of wheat-based products is presented to demonstrate the interest of this approach.

Sébastien Destercke - One of the best experts on this subject based on the ideXlab platform.

  • An iterative approach to build relevant Ontology-aware data-driven models
    Information Sciences, 2013
    Co-Authors: Rallou Thomopoulos, Sébastien Destercke, Brigitte Charnomordic, Johnson Iyan, Joel Abecassis
    Abstract:

    In many fields involving complex environments or living organisms, data-driven models are useful to make simulations in order to extrapolate costly experiments and to Design decision-support tools. Learning methods can be used to build interpretable models from data. However, to be really useful, such models must be trusted by their users. From this perspective, the domain expert knowledge can be collected and modelled to help guiding the learning process and to increase the confidence in the resulting models, as well as their relevance. Another issue is to Design relevant ontologies to formalize complex knowledge. Interpretable predictive models can help in this matter. In this paper, we propose a generic iterative approach to Design Ontology-aware and relevant data-driven models. It is based upon an Ontology to model the domain knowledge and a learning method to build the interpretable models (decision trees in this paper). Subjective and objective evaluations are both involved in the process. A case study in the domain of Food Industry demonstrates the interest of this approach.

  • An iterative approach to build relevant Ontology-aware data-driven models - application to food processes
    2012
    Co-Authors: Rallou Thomopoulos, Sébastien Destercke, Brigitte Charnomordic, Joel Abecassis
    Abstract:

    In experimental Life Sciences, simulations are needed in order to extrapolate costly experiments and to Design decision-support tools. When extensive mathematical knowledge is not available at the desired scale, expertise and data-driven models can be used as a basis for these simulations. We propose a generic iterative approach to Design Ontology-aware and relevant data-driven models. It is based upon an Ontology to model the domain knowledge, and it uses decision trees as the learning method.

  • An iterative approach to build relevant Ontology-aware data-driven models - application to food processes
    2012
    Co-Authors: Rallou Thomopoulos, Sébastien Destercke, Brigitte Charnomordic, Joel Abecassis
    Abstract:

    In experimental Life Sciences, simulations are needed in order to extrapolate costly experiments and to Design decision-support tools. When extensive mathematical knowledge is not available at the desired scale, expertise and data-driven models can be used as a basis for these simulations. Decision tree algorithms are efficient approaches for data-driven discovery of complex non obvious relationships. Their readability and the absence of a priori assumptions make them particularly useful for variable selection in highly multidimensional problems, therefore they are ideal to display statistically important variables on which the expert should focus. However, to be really useful, such models must be trusted by their users. From this perspective, the domain expert knowledge can be collected and modelled to help guiding the learning process and to increase the confidence in the resulting models, as well as their relevance. We propose a generic iterative approach to Design Ontology-aware and relevant data-driven models. It is based upon an Ontology to model the domain knowledge, and it uses decision trees as the learning method. Relationships between concepts from the Ontology and variables from the data sets are formalized and various data processing techniques are presented to build more significant variables from the original ones, exploiting both the Ontology and expert feedback. Subjective and objective evaluations are both involved in the process. Starting from an initial data set, an initial data-driven model is learnt (step 1). This model is first evaluated with numerical criteria, and submitted to domain experts, who may enrich the Ontology by suggesting new relations between some variables (step 2). Data transformations are applied according to these new relations (step 3). The whole process is repeated iteratively. A case study concerning the impact of agri-food transformation processes on the nutritional quality of wheat-based products is presented to demonstrate the interest of this approach.

Brigitte Charnomordic - One of the best experts on this subject based on the ideXlab platform.

  • An iterative approach to build relevant Ontology-aware data-driven models
    Information Sciences, 2013
    Co-Authors: Rallou Thomopoulos, Sébastien Destercke, Brigitte Charnomordic, Johnson Iyan, Joel Abecassis
    Abstract:

    In many fields involving complex environments or living organisms, data-driven models are useful to make simulations in order to extrapolate costly experiments and to Design decision-support tools. Learning methods can be used to build interpretable models from data. However, to be really useful, such models must be trusted by their users. From this perspective, the domain expert knowledge can be collected and modelled to help guiding the learning process and to increase the confidence in the resulting models, as well as their relevance. Another issue is to Design relevant ontologies to formalize complex knowledge. Interpretable predictive models can help in this matter. In this paper, we propose a generic iterative approach to Design Ontology-aware and relevant data-driven models. It is based upon an Ontology to model the domain knowledge and a learning method to build the interpretable models (decision trees in this paper). Subjective and objective evaluations are both involved in the process. A case study in the domain of Food Industry demonstrates the interest of this approach.

  • An iterative approach to build relevant Ontology-aware data-driven models - application to food processes
    2012
    Co-Authors: Rallou Thomopoulos, Sébastien Destercke, Brigitte Charnomordic, Joel Abecassis
    Abstract:

    In experimental Life Sciences, simulations are needed in order to extrapolate costly experiments and to Design decision-support tools. When extensive mathematical knowledge is not available at the desired scale, expertise and data-driven models can be used as a basis for these simulations. We propose a generic iterative approach to Design Ontology-aware and relevant data-driven models. It is based upon an Ontology to model the domain knowledge, and it uses decision trees as the learning method.

  • An iterative approach to build relevant Ontology-aware data-driven models - application to food processes
    2012
    Co-Authors: Rallou Thomopoulos, Sébastien Destercke, Brigitte Charnomordic, Joel Abecassis
    Abstract:

    In experimental Life Sciences, simulations are needed in order to extrapolate costly experiments and to Design decision-support tools. When extensive mathematical knowledge is not available at the desired scale, expertise and data-driven models can be used as a basis for these simulations. Decision tree algorithms are efficient approaches for data-driven discovery of complex non obvious relationships. Their readability and the absence of a priori assumptions make them particularly useful for variable selection in highly multidimensional problems, therefore they are ideal to display statistically important variables on which the expert should focus. However, to be really useful, such models must be trusted by their users. From this perspective, the domain expert knowledge can be collected and modelled to help guiding the learning process and to increase the confidence in the resulting models, as well as their relevance. We propose a generic iterative approach to Design Ontology-aware and relevant data-driven models. It is based upon an Ontology to model the domain knowledge, and it uses decision trees as the learning method. Relationships between concepts from the Ontology and variables from the data sets are formalized and various data processing techniques are presented to build more significant variables from the original ones, exploiting both the Ontology and expert feedback. Subjective and objective evaluations are both involved in the process. Starting from an initial data set, an initial data-driven model is learnt (step 1). This model is first evaluated with numerical criteria, and submitted to domain experts, who may enrich the Ontology by suggesting new relations between some variables (step 2). Data transformations are applied according to these new relations (step 3). The whole process is repeated iteratively. A case study concerning the impact of agri-food transformation processes on the nutritional quality of wheat-based products is presented to demonstrate the interest of this approach.

Martin Torngren - One of the best experts on this subject based on the ideXlab platform.

  • Design Ontology in a Case Study for Cosimulation in a Model-Based Systems Engineering Tool-Chain
    IEEE Systems Journal, 2020
    Co-Authors: Jinzhi Lu, Guoxin Wang, Martin Torngren
    Abstract:

    Cosimulation is an important system-level verification approach aimed at integrating multidomain and multi-physics models during complex system development. Currently, the lack of integrating system development process with cosimulations leads to gaps between them, decreasing the effectiveness and efficiency of system development. Model-based systems engineering (MBSE) tool-chains have been proposed to facilitate the integration of complex system development and automated verification using a model-based approach. However, due to the lack of formal and structured specifications, development information sharing is difficult for supporting MBSE facilitating automated cosimulations. In order to formalize cosimulation in an MBSE tool-chain, a scenario-based Ontology is developed in this paper, using formal web Ontology language (OWL). Ontology refers to a specification expressing the cosimulation implementations as well as the development information represented in the models supporting the MBSE. It is illustrated by a case study of a cosimulation based on Simulink. Protocol and resource description framework (RDF) query language (SPARQL) and semantic query-enhanced web rule language queries are proposed for evaluating the Ontology's completeness and logic for supporting cosimulations. The result demonstrates that the scenario-based Ontology formalizes the information related to automated cosimulation development and configurations while using the proposed MBSE tool-chain.