jaygee-on-github
commented
3 months ago - Determine what parts of a Dataset, the variables it measures and/or other schemas in a schema.org JSON-LD form our target metadata for the catalog entries. Especially when it comes to variables that measure exposures, we will learn how in schema.org JSON-LD we can represent their spatial and/or temporal coverage. In the process we hope to evaluate the use of Uber's H3 Hexagons.
- Determine how we will load the schema.org JSON.LD catalog entries into the GIS backbone. Here we will learn about the GIS backbone interfaces we can use to push metadata into one or more of the backbone tables.
- Decide on a standard input format for the schema.org JSON-LD catalog entries. Here we will study and learn which input formats users prefers when they describe exposure datasets.
- Provide step by step directions for building a mapping table that moves standard input into the schema.org JSON-LD. Here we will learn how to document and communicate mapping tables that are RDF documents themselves.
- Decide on a shape for the schema.org JSON-LD knowledge graph that captures all the catalog entries. Here we will learn how to develop ways to frame our knowledge graphs for fun and ease of use.
- Provide step by step instructions for navigating and/or querying this knowledge graph. Here maybe not in time for the global symposium we will learn how to make our knowledge graph queryable using an LLM. Note that LLMs are beginning to accept knowledge graphs as input.
- Capture the entire workflow in a YouTube. Here we will test maybe two approaches and learn from users which one is more successful. In one approach we might voice over a PowerPoint. In another approach we might narrate the execution of a Jupyter notebook.
AEW0330
tibbben
Catalog learning objectives