What is the Difference Between a Semantic Layer and a Context Layer? When to Use a Knowledge Graph vs. a Context Graph

Before AI became part of everyday conversations, most enterprise knowledge and data projects had a somewhat straightforward goal: to create a “single source of truth.” In theory, this meant that everyone in the company could look at the same search results and data dashboards and get the same answers to basic questions like, “Who is our expert on a given topic?” or “What was our revenue last quarter?” In practice, we know how hard it has been to answer those questions. Teams tend to debate the definition of terms and negotiate which data to measure, obscuring one obvious answer. It’s refreshing, however, that the industry now strongly agrees – semantics is key to making AI work in the enterprise. Ironically, even with this agreement, data practitioners are still struggling to agree on the terms themselves; what they mean and how all the pieces should fit together to actually deliver value.