Content related to The Role of Taxonomy in Labeled Property Graphs (LPGs) & Graph Analytics
Semantic Layer for Content Discovery, Personalization, and AI Readiness
A professional association needed to improve their members’ content experiences. With tens of thousands of content assets published across 50 different websites and 5 disparate content management systems (CMSes), they struggled to coordinate a content strategy and improve content discovery. They could not keep up with the demands of managing content … Continue reading
Semantic Search Advisory and Implementation for an Online Healthcare Information Provider
The medical field is an extremely complex space, with thousands of concepts that are referred to by vastly different terms. These terms can vary across regions, languages, areas of practice, and even from clinician to clinician. Additionally, patients often communicate … 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
Navigating System Limitations for Taxonomy Implementation
When navigating the transition from designing a taxonomy to implementing it in the intended systems, it can be common to encounter a gap between ideal implementation (hierarchical tagging without system-imposed limits, controlled by tight role-based user permissions), and reality. Continue reading
Semantic Layer Maturity Framework Series: Taxonomy
Taxonomy is foundational to the Semantic Layer. A taxonomy establishes the essential semantic building blocks upon which everything else is built, starting by standardizing naming conventions and ensuring consistent terminology. From there, taxonomy concepts are enriched with additional context, such … 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
Enhancing Insurance Fraud Detection through Graph-Based Link Analysis
A national agency overseeing insurance claims engaged EK to advise on developing and implementing graph-based analytics to support fraud detection. EK applied key concepts such as knowledge graphs, graph-based link analysis for detecting potentially suspicious behavior, and the underlying technology architecture required to instantiate a fully functional solution at the agency to address client challenges. Continue reading
How to Implement a Semantic Layer: A Proven Operating Model
As organizations invest in enterprise AI and knowledge intelligence, the semantic layer serves as a critical foundation for providing a consistent, contextual framework that connects data assets across multiple sources to enable shared understanding, interoperability, and more intelligent use of … Continue reading