Content related to What is an Enterprise Knowledge Graph and Why Do I Want One?
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
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
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
Graph Solutions PoC to Production: Overcoming the Barriers to Success (Part I)
Part I: A Review of Why Graph PoCs Struggle to Demonstrate Success or Progress to Production This is Part 1 of a two-part series on graph database PoC success and production deployment. Introduction I began my journey with graphs … Continue reading
Semantic Layer Strategy for Linked Data Investigations
A government organization sought to more effectively exploit their breadth of data generated by investigation activity of criminal networks for comprehensive case building and threat trend analysis. EK engaged with the client to develop a strategy and product vision for their semantic solution, paired with foundational semantic data models for meaningful data categorization and linking, architecture designs and tool recommendations for integrating and leveraging graph data, and entitlements designs for adhering to complex security standards. Continue reading