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 ...
Abstract: Graph invariant learning (GIL) seeks invariant relations between graphs and labels under distribution shifts. Recent works try to extract an invariant subgraph to improve out-of-distribution ...
Abstract: The current knowledge graph-based joint extraction method for hazard source identification in integrated utility tunnels has been applied to emergency response management, assisting ...
Transform any structured repository (Kubernetes manifests, Terraform, API specs, Docs, etc.) into queryable knowledge graphs. An AI assistant (like Claude Code) analyzes your repository, identifies ...
Microsoft Corp. today is expanding its Fabric data platform with the addition of native graph database and geospatial mapping capabilities, saying the enhancements enhance Fabric’s capacity to power ...
Currently, the graph search functionality in mem0 uses a hardcoded system prompt for entity extraction that cannot be customized. This limits users' ability to fine-tune entity extraction for ...
ABSTRACT: Knowledge Graph (KG) and neural network (NN) based Question-answering (QA) systems have evolved into the realm of intelligent information retrieval as they have been able to reach a high ...
If you’re like me, you’ve heard plenty of talk about entity SEO and knowledge graphs over the past year. But when it comes to implementation, it’s not always clear which components are worth the ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results