The Evolution of Knowledge Management & Organizational Roles: Integrating KM, Data Management, and Enterprise AI through a Semantic Layer

On June 23, 2025, at the Knowledge Summit Dublin, Lulit Tesfaye and Jess DeMay presented “The Evolution of Knowledge Management (KM) & Organizational Roles: Integrating KM, Data Management, and Enterprise AI through a Semantic Layer.” The session examined how KM … Continue reading

Sara Nash Presenting at Data Architecture Online

Sara Nash, Principal Consultant at Enterprise Knowledge, will be moderating the keynote session titled “Data Architecture for AI” at Data Architecture Online’s annual event on Wednesday, July 23rd at 11:30am EST. Through this session, attendees will gain valuable insights into … Continue reading

Semantic Graphs in Action: Bridging LPG and RDF Frameworks

Enterprise Knowledge is pleased to introduce a new webinar titled, Semantic Graphs in Action: Bridging LPG and RDF Frameworks. This webinar will bring together four EK experts on graph technologies to explore the differences, complementary aspects, and best practices of … Continue reading

Optimizing Historical Knowledge Retrieval: Leveraging an LLM for Content Cleanup

Enterprise Knowledge (EK) recently worked with a Federally Funded Research and Development Center (FFRDC) that was having difficulty retrieving relevant content in a large volume of archival scientific papers. Researchers were burdened with excessive search times and the potential for knowledge loss … Continue reading

Graph Analytics in the Semantic Layer: Architectural Framework for Knowledge Intelligence

Introduction As enterprises accelerate AI adoption, the semantic layer has become essential for unifying siloed data and delivering actionable, contextualized insights. Graph analytics plays a pivotal role within this architecture, serving as the analytical engine that reveals patterns and relationships … Continue reading

What is a Knowledge Asset?

Over the course of Enterprise Knowledge’s history, we have been in the business of connecting an organization’s information and data, ensuring it is findable and discoverable, and enriching it to be more useful to both humans and AI. Though use … Continue reading

Beyond Traditional Machine Learning: Unlocking the Power of Graph Machine Learning

Traditional machine learning (ML) workflows have proven effective in a wide variety of use cases, from image classification to fraud detection. However, traditional ML leaves relationships between data points to be inferred by the model, which can limit its ability … Continue reading