Why knowledge mining? Well, real-world data is messy. It often spans media types (e.g. text documents, PDF files, images, databases), changes constantly, and carries valuable knowledge in a way that is not readily usable. The typical solution pattern for extracting information from data is a data ingestion, enrichment and exploration model.
Each of these brings its own challenges to the table—from large-scale change tracking to file format support, and even composition of multiple AI models. Developers can do this today, but it takes a huge amount of effort, requires branching into multiple unrelated domains (from cracking PDFs to handling AI model composition), and distracts from the primary goal. This is where knowledge mining comes in.
This is part one of a two-part workshop.