Most large enterprises struggle to adopt knowledge graphs and semantic models given years of investments in legacy mainframe, relational database and data warehousing technologies. Because legacy data has been created and retained in source application schemas with zero semantic structure, the cost and complexity to convert to a universal-semantic data model is prohibitive, often hindering knowledge graph projects. How do you untrap 20+ years and petabytes of data and activate it to leverage the benefits of semantics and Knowledge Graphs?
Join us in this working session where we will walk through (1) advances in data formats, such as JSON Linked Data (JSON-LD), that make conversion of flat, relational data into serialized RDF viable at scale; and (2) advances in machine learning and AI in data classification to automate the linking of semantics to flat, structured data. By the end of this session, we will have walked through a scenario using commercial tools available in the marketplace today to integrate legacy data from multiple traditional data stores into a Knowledge Graph, as well as discuss practical considerations for how to adopt in your organization.