https://w3id.org/np/RADgrhIUesgIkJSVaSpFKGHMCuvd3XAIGnDFd07AmcvsE
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<https://doi.org/10.14279/depositonce-9977> dct:title "EG-ICE 2020 Workshop on Intelligent Computing in Engineering" .
orcid:0000-0001-8492-0354 schema:affiliation <https://ror.org/01swzsf04>;
foaf:name "Ashley Caselli" .
orcid:0000-0002-1401-5110 schema:affiliation <https://ror.org/01swzsf04>;
foaf:name "Gilles Falquet" .
orcid:0000-0003-3660-0270 schema:affiliation <https://ror.org/01swzsf04>;
foaf:name "Vincenzo Daponte" .
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dct:abstract "The automated regulation compliance checking in the domain of Architecture, Engineering, and Construction (AEC) still presents many challenges to face. The completeness and correctness of the input data affect the results of the compliance checking. Refinement operations on data represented through knowledge graphs can help the assessment of these data properties. In this work, a model to represent rules holding complex constraints is proposed and conveyed through an ontology. This model aims to overcome existing expressiveness limitations and to provide a conceptual tool to represent both compliance regulations and domain experts' knowledge encoded through heuristics. These heuristics are used to drive the refinement process needed prior the compliance checking. The proposed model is intended as the first step towards an automatic execution process of both types of rule instances. The model has been assessed by representing regulations issued by Swiss entities and domain experts' heuristics targeting data of an area of the city of Geneva.";
dct:date "2020";
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dct:title "A Rule Language Model for Subsurface Data Refinement";
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