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D1.3 Knowledge graph construction using integration plug-ins for automated collection - Full report
Data mining and semantic modelling strategies are needed to create the GLOMICAVE’s knowledge base, containing omics data extracted from referent data repositories. This deliverable describes the methodological approach for building the knowledge graph and also, the applied to techniques to build it inside the GLOMICAVE digital architecture. More specifically, this document presents the construction of the GLOMICAVE knowledge graph through the adaptation and extension of a pre-existing semantic data model called ISA1 (Investigation, Study, Assay) to the adoption of other standards and enable data harmonization between omics information.
The elaborated approach and semantic model (ontology) is publicly available in the following URL: https://w3id.org/def/isa-glomicave/
The design and implementation of the GLOMICAVE semantic model add significant value to:• Support the data representation to enable homogenization between data sources and increase the knowledge base.
• Provide metadata and context-based information to interlink scientific outputs, in that case related to multi-omics repositories.
• Generating open linked data related to omics experiments.
• Support the elaboration of complementary data analysis.
To collect all this information a set of plug-ins (one per repository) has been developed to gather data on demand. From the data collected in raw format, a transformation to include context-based information (semantic metadata) is performed based on the concepts and terms defined under the extended ontology. To enable a data understanding, the present deliverable also includes a dashboarding tools to interact with the data and validate the correct data gathering from the repositories considering the omics experiments.