Content related to What are the Different Types of Graphs? The Most Common Misconceptions and Understanding Their Applications
Women’s Health Foundation – Semantic Classification POC
A humanitarian foundation focusing on women’s health faced a complex problem: determining the highest impact decision points in contraception adoption for specific markets and demographics. Two strategic objectives drove the initiative—first, understanding the multifaceted factors (from product attributes to social influences) that guide women’s contraceptive choices, and second, identifying actionable insights from disparate data sources. The key challenge was integrating internal survey response data with internal investment documents to answer nuanced competency questions such as, “What are the most frequently cited factors when considering a contraceptive method?” and “Which factors most strongly influence adoption or rejection?” This required a system that could not only ingest and organize heterogeneous data but also enable executives to visualize and act upon insights derived from complex cross-document analyses. Continue reading
Cutting Through the Noise: An Introduction to RDF & LPG Graphs
Graph is good. From capturing business understanding to support standardization and data analytics to informing more accurate LLM results through Graph-RAG, knowledge graphs are an important component of how modern businesses translate data and content into actionable knowledge and information. … Continue reading
Unlocking Knowledge Intelligence from Unstructured Data
Introduction Organizations generate, source, and consume vast amounts of unstructured data every day, including emails, reports, research documents, technical documentation, marketing materials, learning content and customer interactions. However, this wealth of information often remains hidden and siloed, making it challenging … Continue reading
Enterprise AI Architecture Series: How to Inject Business Context into Structured Data using a Semantic Layer (Part 3)
Introduction AI has attracted significant attention in recent years, prompting me to explore enterprise AI architectures through a multi-part blog series this year. Part 1 of this series introduced the key technical components required for implementing an enterprise AI architecture. … Continue reading
What is Semantics and Why Does it Matter?
This white paper will unpack what semantics is, and walk through the benefits of a semantic approach to your organization’s data across search, usability, and standardization. As a knowledge and information management consultancy, EK works closely with clients to help … Continue reading
How a Semantic Layer Transforms Engineering Research Industry Challenges
To drive future innovation, research organizations increasingly seek to develop advanced platforms that enhance the findability and connectivity of their knowledge, data, and content–empowering more efficient and impactful R&D efforts. However, many face challenges due to decentralized information systems, where … Continue reading
The Minimum Requirements To Consider Something a Semantic Layer
Semantic Layers are an important design framework for connecting information across an organization in preparation for Enterprise AI and Knowledge Intelligence. But with every new technology and framework, interest in utilizing the technological advance outpaces experience in effective implementation. As … Continue reading
The Resource Description Framework (RDF)
Simply defined, a knowledge graph is a network of entities, their attributes, and how they’re related to one another. While these networks can be captured and stored in a variety of formats, most implementations leverage a graph based tool or … Continue reading
Multimodal Graph RAG (mmGraphRAG): Incorporating Vision in Search and Analytics
David Hughes, Principal Data & AI Solution Architect at Enterprise Knowledge, presented “Unleashing the Power of Multimodal GraphRAG: Integrating Image Features for Deeper Insights” at Data Day Texas 2025 in Austin, TX on Saturday, January 25th. In this presentation, David … Continue reading
A Guide to Selecting the Right Auto-Tagging Approach
Auto-tagging processes automate the manual labor of applying relevant keyword tags to data and content, enhancing accessibility and improving the organization of large datasets. Whether you’re trying to improve how quickly you find data or embarking on a content cleanup … Continue reading