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Computational Chemistry Data Management Platform Based on the Semantic Web.

Bing Wang1, Paul A Dobosh1, Stuart Chalk2

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This study introduces a semantic web platform for publishing computational chemistry data. It converts data into linked data formats, making it accessible for both chemists and computer agents.

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Area of Science:

  • Computational Chemistry
  • Semantic Web Technologies
  • Data Publishing

Background:

  • Computational chemistry generates vast amounts of data.
  • Standardized data formats are crucial for data sharing and integration.
  • Existing data publishing methods lack semantic interoperability.

Purpose of the Study:

  • To develop a formal data publishing platform for computational chemistry.
  • To leverage semantic web technologies for enhanced data accessibility.
  • To integrate computational chemistry data into a Giant Global Graph (GGG).

Main Methods:

  • Encapsulation of computational chemistry data into CSX (Common Standard for eXchange) XML files.
  • Transformation of CSX XML to JSON-LD using XSLT, based on the Gainesville Core (GC) ontology.
  • Conversion of JSON-LD to Turtle (TTL) triples for web publication.

Main Results:

  • A functional data publishing platform for computational chemistry data.
  • Successfully converted and published computational chemistry data in linked data formats.
  • Enabled data accessibility through a web portal for computer agents and chemists.

Conclusions:

  • Semantic web technologies provide a robust framework for publishing computational chemistry data.
  • The developed platform facilitates data integration and accessibility within the scientific community.
  • This approach supports the vision of a Giant Global Graph for scientific data.