Presentation covers a technology used to automate Knowledge extraction from Text. This novel technology blends neural language models, semantic tech, rule systems, linguistic theory to achieve reliable extraction performance. Specifically, the dicussion will focus on the work done together with Dow/Factiva, involving the extraction of facts buried in news articles, news letters, reports, etc. about the subject area of ESG (Environmental..) through the application of a homegrown ESG taxonomy and ontology. Extracted facts are output as RDF triples and ingested into a Semantic Knowledge Graph stored in a triple store. Also, the Knowledge graph supports BI & reporting over these facts (inferred facts with reasoning!) through standard graph queries.