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A knowledge-based system for generating interaction networks from ecological data

2017

  • Authors:
    Willem Coetzer , Deshen Moodley , Aurona Gerber

    Publication date:
    2017

    Institution:
    CSIR Meraka Institute, UKZN, UCT, UP

    Output type:
    Journal paper

    Elsevier

    Abstract:

    Semantic heterogeneity hampers efforts to find, integrate, analyse and interpret ecological data.
    An application case-study is described, in which the objective was to automate the integration
    and interpretation of heterogeneous, flower-visiting ecological data. A prototype knowledgebased
    system is described and evaluated. The system's semantic architecture uses a combination
    of ontologies and a Bayesian network to represent and reason with qualitative, uncertain
    ecological data and knowledge. This allows the high-level context and causal knowledge of
    behavioural interactions between individual plants and insects, and consequent ecological
    interactions between plant and insect populations, to be discovered. The system automatically
    assembles ecological interactions into a semantically consistent interaction network (a new
    design of a useful, traditional domain model). We discuss the contribution of probabilistic
    reasoning to knowledge discovery, the limitations of knowledge discovery in the application
    case-study, the impact of the work and the potential to apply the system design to the study of
    ecological interaction networks in general.