Content related to Identifying Security Risks Using Auto-Tagging and Text Analytics
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
How to Leverage LLMs for Auto-tagging & Content Enrichment
When working with organizations on key data and knowledge management initiatives, we’ve often noticed that a roadblock is the lack of quality (relevant, meaningful, or up-to-date) existing content an organization has. Stakeholders may be excited to get started with advanced … Continue reading
Defining Governance and Operating Models for AI Readiness of Knowledge Assets
Artificial intelligence (AI) solutions continue to capture both the attention and the budgets of many organizations. As we have previously explained, a critical factor to the success of your organization’s AI initiatives is the readiness of your content, data, and … Continue reading
Semantic Layer Strategy: The Core Components You Need for Successfully Implementing a Semantic Layer
Today’s organizations are flooded with opportunities to apply AI and advanced data experiences, but many struggle with where to focus first. Leaders are asking questions like: “Which AI use cases will bring the most value? How can we connect siloed … Continue reading
How to Ensure Your Content is AI Ready
In 1996, Bill Gates declared “Content is King” because of its importance (and revenue generating potential) on the World Wide Web. Nearly 30 years later, content remains king, particularly when leveraged as a vital input for Enterprise AI. Having AI-ready … 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
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
Breaking Down Types of Knowledge Assets and Their Impact
In their blog “What is Knowledge Asset?”, EK’s CEO Zach Wahl and Practice Lead for Semantic Design and Modeling, Sara Mae O’Brien-Scott, explored how organizations can define knowledge assets beyond just documents or data. It emphasizes that anything, from people … Continue reading
The Semantic Exchange Webinar Series Recap
Enterprise Knowledge recently completed the first round of our new webinar series The Semantic Exchange, which offers participants an opportunity to engage in Q&A with EK’s Semantic Design thought leaders. Participants were able to engage with EK’s experts on topics … Continue reading
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