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Semantically interoperable census data: unlocking the semantics of census data using ontologies and linked data.

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Summary
This summary is machine-generated.

This study introduces an ontology to transform Canadian Census of Population data into linked data. This approach tackles data wrangling and referential equivalence challenges, improving data integration and visualization.

Keywords:
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Area of Science:

  • Social Sciences
  • Data Science
  • Information Science

Background:

  • Canadian Census of Population provides valuable socioeconomic data for public and private sectors.
  • Existing census data presents challenges including time-consuming data wrangling, issues with referential equivalence across datasets, and difficulties in machine interpretation of natural language descriptions.
  • These limitations hinder efficient data integration and utilization for planning and decision-making.

Purpose of the Study:

  • To develop and propose an ontology for representing Canadian Census of Population data as linked data.
  • To address challenges in data wrangling, referential equivalence, and machine interpretability.
  • To evaluate the proposed ontology's effectiveness and discuss its advantages for data integration and visualization.

Main Methods:

  • Development of a novel ontology tailored for Canadian Census of Population data.
  • Representation of census data as linked data using the developed ontology.
  • Evaluation of the ontology through competency questions derived from real-world use cases.

Main Results:

  • The proposed ontology effectively addresses data wrangling and referential equivalence issues inherent in census data.
  • Linked data representation facilitates improved machine interpretability and deconstruction of census information.
  • Demonstrated advantages of the ontology for enhanced data integration and visualization capabilities.

Conclusions:

  • The ontology provides a robust framework for converting Canadian Census of Population data into a machine-readable linked data format.
  • Linked census data significantly enhances data accessibility, integration, and analytical potential.
  • This approach offers a scalable solution for managing and utilizing complex socioeconomic datasets.