Master of Information and Data Science (MIDS) alums Beth McBride, Ishani Cheshire, Indri Adisoemarta, Chase Martin, and Ambro ...

Abstract: Recently, self-supervised learning has shown great potential in Graph Neural Networks (GNNs) through contrastive learning, which aims to learn discriminative features for each node without ...

Learn how LLMs are transforming schema matching through semantic reasoning while deterministic validation keeps enterprise ...

Investopedia contributors come from a range of backgrounds, and over 25 years there have been thousands of expert writers and editors who have contributed. Dr. JeFreda R. Brown is a financial ...

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?

Long-horizon reasoning exposes a core weakness in AI agents: context windows fill up fast, and retrieval pipelines return noise instead of signal. To solve this, researchers at the National University ...

One of the greatest weaknesses of AI agents that read and understand vast amounts of enterprise data is "hallucination"—the generation of plausible-sounding but factually incorrect information. KAIST ...

AI vendors promote their enterprise products as if they’re turnkey solutions, but the chances are low that AI agents will hit the ground running right away. Unless you put in the effort to train a ...

Dr. JeFreda R. Brown is a financial consultant, Certified Financial Education Instructor, and researcher who has assisted thousands of clients over a more than two-decade career. She is the CEO of ...

Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks (GNNs)—AI systems used in applications from drug discovery to weather ...

This repository is the home for the source materials, sample code and examples for the book "Practical Gremlin - An Apache TinkerPop Tutorial". This is also the home for the air-routes dataset ...