Content related to Choosing the Right Approach: LLMs vs. Traditional Machine Learning for Text Summarization
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 Elliot Risch, James Egan, David … Continue reading
Auto-Classification for the Enterprise: When to Use AI vs. Semantic Models
Auto-classification is a valuable process for adding context to unstructured content. Nominally speaking, some practitioners distinguish between auto-classification (placing content into pre-defined categories from a taxonomy) and auto-tagging (assigning unstructured keywords or metadata, sometimes generated without a taxonomy). In this article, I use ‘auto-classification’ in the broader sense, encompassing both approaches. Continue reading
LLM Solutions PoC to Production: From RAGs to Riches (Part 1)
In the past year, many of the organizations EK has partnered with have been developing Large Language Model (LLM) based Proof-of-Concepts (PoCs). These projects are often pushed for by an enthusiastic IT Team, or internal initiative – with the low … 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
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 Taxonomy Management Through Knowledge Intelligence
In today’s data-driven world, managing taxonomies has become increasingly complex, requiring a balance between precision and usability. The Knowledge Intelligence (KI) framework – a strategic integration of human expertise, AI capabilities, and organizational knowledge assets – offers a transformative approach … 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