Abstract: When it comes to the marriage of graph neural networks (GNNs) and model extraction attacks, the deployment of GNNs within Machine Learning as a Service (MLaaS) through a publicly ...
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 ...
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 ...
Document-level Role Filler Extraction exhibits a wide range of application value in natural language processing, including information retrieval, article summarization and trends analysis of world ...
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