Content related to A Guide to Selecting the Right Auto-Tagging Approach
Humanitarian Foundation – SemanticRAG POC
A humanitarian foundation needed to demonstrate the ability of its Graph Retrieval Augmented Generation (GRAG) system to answer complex, cross-source questions. In particular, the task was to evaluate the impact of foundation investments on strategic goals by synthesizing information from publicly available domain data, internal investment documents, and internal investment data. The challenge laid in …. 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
Leveraging Institutional Knowledge to Improve AI Success
In an age where organizations are seeking competitive advantages from new technologies, having high-quality knowledge readily available for use by both humans and AI solutions is an imperative. Organizations are making large investments in deploying AI. However, many are … Continue reading
Understanding the Role of Knowledge Intelligence in the CRISP-DM Framework: A Guide for Data Science Projects
In today’s rapidly advancing field of data science, where new technologies and methods continuously emerge, it’s essential to have a structured approach to navigate the complexities of data mining and analysis. The CRISP-DM framework–short for Cross-Industry Standard Process for Data … Continue reading
What are the Different Types of Graphs? The Most Common Misconceptions and Understanding Their Applications
Over 80% of enterprise data remains unstructured, and with the rise of artificial intelligence (AI), traditional relational databases are becoming less effective at capturing the richness of organizational knowledge assets, institutional knowledge, and interconnected data. In modern enterprise data solutions, … 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
AI & Taxonomy: the Good and the Bad
The recent popularity of new machine learning (ML) and artificial intelligence (AI) applications has disrupted a great deal of traditional data and knowledge management understanding and tooling. At EK, we have worked with a number of clients who have questions–how … Continue reading
Extracting Knowledge from Documents: Enabling Semantic Search for Pharmaceutical Research and Development
The Challenge A major pharmaceutical research and development company faced difficulty creating regulatory reports and files based on years of drug experimentation data. Their regulatory intelligence teams and drug development chemists spent dozens of hours searching through hundreds of thousands … Continue reading