VCityTeam / UD-Graph

Repository on graph models for Urban Data
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UD-Graph

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Repository for transforming and manipulating urban data models and data with knowledge graphs.

This work is part of the larger Virtual City Project of LIRIS UMR 5205 CNRS, in conjunction with the Université de Lyon, and The Université Lumière Lyon.

A dockerized version of UD-Graph has been created to facilitate the reproducibility of the proposed transformation workflows possible with this repository.

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Directories

Transformations

The Transformations directory contains the following implementations:

  1. ShapeChange (UML-to-OWL)
    • ShapeChange configuration files for CityGML 2.0 and 3.0 UML models to RDF/OWL ontologies
  2. XML-to-OWL
    • Scripts for converting XML files into RDF/OWL individuals based in Python
    • Scripts for converting XML files into RDF/OWL individuals based in XSLT
  3. XSD-to-OWL
    • Scripts for converting XML Schema into RDF/OWL ontologies
  4. test-data
    • Stores for test GML, RDF, OWL, UML, and XML Schema data See here for more information.

Datasets

Contains datasets generated using model driven transformations and scripts in the Transformations directory. See here for more information.

Ontologies

Up to date versions of several proposed ontologies are available on https://datasets.liris.cnrs.fr/rdfowl-urban-data-ontologies-version1, notably several ontologies based on the CityGML model.

Some of these ontologies are created using model driven transformations and scripts in the Transformations directory. Minor ontology editing is done using the Protégé resource, which is supported by grant GM10331601 from the National Institute of General Medical Sciences of the United States National Institutes of Health.

SPARQL Queries

Contains basic queries to be used on the RDF generated from XML to OWL scripts and their results. See here for more information.

Rules

The Rules directory contains a proof of concept test suite for executing SWRL rules over Semantic Web knowledge graphs. Through reasoning, these rules can be used either to intuite new information about data or when data is inconsistent. See here for more information.