Text mining and knowledge graphs connect cell-culture parameters to glycosylation for faster bioprocess optimization.

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.

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

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 ...

Wonder Cabinet is an independent podcast from Anne Strainchamps and Steve Paulson, Peabody Award-winning creators of public radio's To The Best Of Our Knowledge. For 35 years, that show brought ...

Mobilizing existing natural and social science, new technologies, and indigenous and local knowledge can inform us about the drivers of biodiversity loss and effective approaches to recovery, ...

Abstract: Knowledge-intensive question answering (QA) requires deep reasoning over heterogeneous sources and factually consistent answers, but existing RAG and GraphRAG frameworks are limited by ...

Abstract: Aiming at the issue that most of the existing knowledge graph-based methods for personalized learning resource recommendations do not take full advantage of collaborative signals from ...

Knowledge graph retrieval to improve multi-hop Q&A performance, optimized with GNN + LLM models. RAG on large knowledge graphs that require multi-hop retrieval and reasoning, beyond node ...

This repository contains the implementation of AutoSchemaKG, a novel framework for automatic knowledge graph construction that combines schema generation via conceptualization. The framework is ...