The Information Management team at Morgan Stanley has built an RDF graph and a semantic knowledge base to help answer domain specific questions, formulate classification recommendations and deliver quality search to our internal users. In doing so over the past 4 years, we also helped other departments across the firm discover and embrace semantic data modeling for their own use cases. In the first part of our presentation, we would like to briefly describe the Semantic Modeling and Ontology consortium we created within the Firm before diving deeper into our knowledge base creation effort. We think that a knowledge base reflecting concepts specifically relevant to our company is extremely valuable to develop semantic technology applications. However, populating knowledge bases can be time consuming, costly and error prone, with an end product that is difficult to maintain. In the second part of this presentation, we would like to discuss a framework for automatic generation of a Simple Knowledge Organization System (SKOS) knowledge base from unstructured text. Our Natural Language Processing (NLP) engine parses the input text to create a semantic knowledge graph, which is then mapped to a SKOS knowledge model. During the linguistic understanding of the text, relevant domain concepts are identified and connected by semantic links -- abstractions of underlying relations between concepts that capture the overall meaning of text based on a corpus of roughly 6,500 policies and procedures published at Morgan Stanley.