How Taxonomies and Ontologies Enable Explainable AI
Taxonomy and ontology models are essential to unlocking the value of knowledge assets. They provide the structure needed to connect fragmented information across an organization, enabling explainable AI. As part of a broader Knowledge Intelligence (KI) strategy, these models help … Continue reading
Top Ways to Get Your Content and Data Ready for AI
As artificial intelligence has quickly moved from science fiction, to pervasive internet reality, and now to standard corporate solutions, we consistently get the question, “How do I ensure my organization’s content and data are ready for AI?” Pointing your organization’s … Continue reading
Webinar: Semantic Graphs in Action – Bridging LPG and RDF Frameworks
As organizations increasingly prioritize linked data capabilities to connect information across the enterprise, selecting the right graph framework to leverage has become more important than ever. In this webinar, graph technology experts from Enterprise Knowledge Elliott Risch, James Egan, David … 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
The Role of Taxonomy in Labeled Property Graphs (LPGs) & Graph Analytics
Taxonomies play a critical role in deriving meaningful insights from data by providing structured classifications that help organize complex information. While their use is well-established in frameworks like the Resource Description Framework (RDF), their integration with Labeled Property Graphs (LPGs) … Continue reading
Top Semantic Layer Use Cases and Applications (with Real World Case Studies)
Today, most enterprises are managing multiple content and data systems or repositories, often with overlapping capabilities such as content authoring, document management, or data management (typically averaging three or more). This leads to fragmentation and data silos, creating significant inefficiencies. … Continue reading
Expertise Augmentation for Full Lifecycle AI/ML Operations
The current job market for these unique positions is dire, and delays in hiring can translate directly to delays in your projects. In-house skill gaps in AI technologies can be a formidable obstacle in your organization’s technical evolution, often blocking … Continue reading
AI Model Quality Assessment & Governance Framework
Many AI initiatives fail because there’s no plan for scale and maintenance. Has your organization already deployed AI/ML models and you’re looking for proven ways to maintain it? Working with some of the world’s largest and most complex organizations, EK … Continue reading