Context graphs is the new buzzword for agentic Systems of Knowledge, uncovering the key role of the tribal knowledge hidden in decision threads that inform enterprise activity.
Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...
Microsoft has spent three years and billions of dollars trying to make its Copilot the transformative AI layer for knowledge work. Enterprise adoption stalled. Pricing faced pushback. Internal emails ...
The Kennedy College of Science, Richard A. Miner School of Computer & Information Sciences, invites you to attend a doctoral dissertation proposal defense by Nidhi Vakil, titled: "Foundations for ...
Asana’s new Claude integration embeds project management inside Anthropic’s AI chatbot, turning natural-language chats into ...
1 College of Design and Architecture, Zhejiang University of Technology, Hangzhou, China 2 College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China ...
According to DeepLearning.AI, Christoph Meyer, Principal AI Scientist, and Lars Heling, Senior Knowledge Engineer at SAP, presented at AI Dev 25 x NYC on leveraging knowledge graphs to boost AI agent ...
Introduction: Clinical decision-making in hepatology is currently challenged by the rapid expansion of medical knowledge and the limitations of Large Language Models (LLMs), specifically their ...
Abstract: Integrating social relationships and knowledge graph (KG) in centralized recommendations is an effective method to mitigate data sparsity and cold-start problems. However, current research ...
Knowledge graph (KG) containing various types of auxiliary information about users and items has been proven to be effective to improve the performance of recommendation. Existing KG aware methods ...
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