Linking Data Semantics
Understanding how to effectively link disparate data sets requires recognizing the underlying semantics, which often extend beyond what's available in a database. By utilizing RDF to create graph structures, one can merge data more intuitively. The discussion also highlights the importance of using SPARQL for querying and OWL for defining ontologies, making it easier to navigate complex relationships in data.In this clip
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Software Engineering Radio - the podcast for professional software developers
Episode 116: The Semantic Web with Jim Hendler
Related Questions
I'm thinking that classical or deep ML solutions have a flaw in that they cannot be extended. For example, if one builds a model and wants to introduce a new feature, typically the model has to be retrained from scratch. So, I'm considering building a Knowledge Graph using LLMs. This knowledge graph would have to include time-dependent data (for example, it should be able to retrieve the "current" President of the USA and also previous presidents if asked). I'm thinking this Knowledge Graph could be used for Retrieval Augmented Generation (RAG) to help with business goals or maybe used with more clever User Interfaces (UIs). I'm not sure how to build or populate this KG and also have a rough idea of how to use it. Can you help me with suggesting particular paths for building and populating this Knowledge Graph?
What is a knowledge graph?
Tell me about knowledge representation