A significant amount of time in data engineering is dedicated to building connections and semantic meaning around pieces of information. Linked data technologies provide a means of tightly coupling metadata with raw information. In this episode Brian Platz explains how JSON-LD can be used as a shared representation of linked data for building semantic data products.
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- Your host is Tobias Macey and today I'm interviewing Brian Platz about using JSON-LD for building linked-data products
- How did you get involved in the area of data management?
- Can you describe what the term "linked data product" means and some examples of when you might build one?
- What is the overlap between knowledge graphs and "linked data products"?
- What is JSON-LD?
- What are the domains in which it is typically used?
- How does it assist in developing linked data products?
- what are the characteristics that distinguish a knowledge graph from
- What are the layers/stages of applications and data that can/should incorporate JSON-LD as the representation for records and events?
- What is the level of native support/compatibiliity that you see for JSON-LD in data systems?
- What are the modeling exercises that are necessary to ensure useful and appropriate linkages of different records within and between products and organizations?
- Can you describe the workflow for building autonomous linkages across data assets that are modelled as JSON-LD?
- What are the most interesting, innovative, or unexpected ways that you have seen JSON-LD used for data workflows?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on linked data products?
- When is JSON-LD the wrong choice?
- What are the future directions that you would like to see for JSON-LD and linked data in the data ecosystem?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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- Knowledge Graph
- Adjacency List
- RDF == Resource Description Framework
- Semantic Web
- Open Graph
- RDF Triple
- IDMP == Identification of Medicinal Products
- FIBO == Financial Industry Business Ontology
- OWL Standard
- Forward-Chaining Rules
- SHACL == Shapes Constraint Language)
- Zero Knowledge Cryptography
- Turtle Serialization