Knowledge Base Challenges

Walid discusses the challenges of automatic knowledge base construction, emphasizing the need for accuracy and nuanced decision-making. He also touches on the importance of consolidating multiple knowledge bases for downstream applications, highlighting ongoing efforts in the data mining community.

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NLP Highlights
83 - Knowledge Base Construction, with Sebastian Riedel
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?
- Is knowledge work being automated?