GraphRAG
Querying Diverse Datasets with MCP
Read about how to integrate JDBC-enabled relational data with your RDF graphs and how to query them both with natural language
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Graphwise: Setting the Gold Standard for Compliant Enterprise AI
Read about how Graphwise’s ISO/IEC 42001-certified GraphRAG technology helps enterprises meet EU AI Act compliance requirements.
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Advancing Power System Analysis with AI and Semantic Data
Read about how Talk2PowerSystem enables power system engineers to query complex grid data using natural language.
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Building Taxonomies with AI
Discover why GenAI doesn't replace taxonomies and how Graphwise platform bridges the gap between SMEs and taxonomists by using AI to automate hierarchies, synonyms, and definitions.
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Accelerating Discovery — How GraphRAG Drives ROI in R&D and Knowledge-Intensive Workflows
Read about how GraphRAG helps organizations cut search time, speed up discovery, and turn AI pilots into measurable ROI.
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Check the Fakes or How AI and Knowledge Graphs Bring Information Integrity
Read about Graphwise’s contributions to EC-funded projects on combatting disinformation
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Automating Data Privacy Compliance: Knowledge Graphs, Generative AI, and Real-Time Risk
Read about how How to use knowledge graphs and GenAI to automate data privacy compliance and govern emerging AI use cases effectively.
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From Retrieval to Reasoning: Enhancing HippoRAG with Graph-Based Semantics
Read about how an ontology-based knowledge graph boosts the multi-hop Q&A accuracy of one of the leading schemaless GraphRAG systems
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What Is GraphRAG?
Retrieval Augmented Generation enhances LLMs with external knowledge for more accurate, contextual question answering.
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What is Taxonomy?
A taxonomy is a key component in any system that enables users to find or discover the information they need.
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Smarter Self-Service — How GraphRAG Boosts ROI in Customer and Employee Support
Read about how GraphRAG reduces hallucinations, improves first-contact resolution, and delivers efficiency gains across customer and employee support.
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What Are Knowledge Embeddings?
Knowledge graph embeddings are vector representations of entities and relationships in a knowledge graph, used to predict missing links and facilitate machine learning tasks.
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