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Graph-based ontology reasoning for formal verification of BREEAM rules

Abstract : Globally, the need to check regulation compliance for sustainability has become central in the delivery of construction projects. This is partly due to policies by various governments requiring existing and new buildings to comply with certain standards or regulations. However, the verification of whether a building complies with any particular standard or regulation has proven challenging in practice. The purpose of formal verification is to prove that under a certain set of assumptions, a building will adhere to a certain set of requirements, for example the minimum performance standards of key environmental issues. Compliance checking requires different criteria often difficult to straightforwardly define and combine in an integrated fashion for providing holistic interpretation to facilitate easy decision-making. Such criteria, their various flows and combinations can easily be dealt with using conceptual graph theories and Semantic Web concepts which allow rules to be imbued to facilitate reasoning. The aim of this study is to tap on conceptual graphs and Semantic Web concepts to develop a system for checking Building Research Establishment Environmental Assessment Methodology (BREEAM) sustainability standard compliance in the French construction industry. A conceptual graph-based framework that formally describes BREEAM requirements and visually analyse compliance checking processes has been proposed. When implemented in a software that integrates conceptual graphs and Semantic Web knowledge, automatic reasoning allows both the logical specification and the visual interpretation to be displayed and further provides a semantic support for compliance checking information.
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https://hal.archives-ouvertes.fr/hal-02134720
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Submitted on : Monday, May 20, 2019 - 4:50:44 PM
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Bernard Kamsu-Foguem, Fonbeyin Henry Abanda, Mamadou Bilo Doumbouya, Justine Flore Tchouanguem. Graph-based ontology reasoning for formal verification of BREEAM rules. Ecological Informatics, Elsevier, 2018, 55, pp.14-33. ⟨10.1016/j.cogsys.2018.12.011⟩. ⟨hal-02134720⟩

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