GraphNews

EK dives into the compounding benefits of an incremental increase in a taxonomy's granularity and scope for an organization.

Editor’s note: David Hughes and Amy Hodler are speakers for ODSC East 2025 this May 13th-15th. Check out their talk, “Advancing GraphRAG…

Unlike traditional databases, knowledge graphs organize information as nodes and edges, making them better for AI systems that reason & infer.

🚀 Introducing RAG-Anything: All-in-One RAG System! ⚡ LightRAG + Multi-Modal = RAG-Anything 🔗 Get started today: https://lnkd.in/gF3D8rnc 📦 Install: pip install raganything No more switching between multiple tools or losing critical visual information! With RAG-Anything, you get ONE unified solution that understands your documents as completely as you do ✨ 🌟 What makes RAG-Anything innovative: - 🔄 End-to-End Multimodal Pipeline: Complete workflow from document ingestion and parsing to intelligent multimodal query answering. - 📄 Universal Document Support: Seamless processing of PDFs, Office documents (DOC/DOCX/PPT/PPTX/XLS/XLSX), images, and diverse file formats. - 🧠 Specialized Content Analysis: Dedicated processors for images, tables, mathematical equations, and heterogeneous content types. - 🔗 Multimodal Knowledge Graph: Automatic entity extraction and cross-modal relationship discovery for enhanced understanding. - ⚡ Adaptive Processing Modes: Flexible MinerU-based parsing or direct multimodal content injection workflows. - 🎯 Hybrid Intelligent Retrieval: Advanced search capabilities spanning textual and multimodal content with contextual understanding. 💡 Well-suited for: - 🎓 Academic research with complex documents - 📋 Technical documentation processing - 💼 Financial report analysis - 🏢 Enterprise knowledge management

Introducing UDA, the knowledge-graph-based architecture that translates conceptual domain models into consistent schemas and data…

Having become almost synonymous with Neo4j, the company that first brought them into the spotlight, knowledge graphs are becoming a staple…

Textual descriptions in cyber threat intelligence (CTI) reports, such as security articles and news, are rich sources of knowledge about cyber threats, crucial for organizations to stay informed...

By J Bittner Part 1 in our 5-part series: From Hallucination to Reasoning—The Case for Ontology-Driven AI Welcome to “Semantically Speaking”—a new series on what makes AI systems genuinely trustworthy, explainable, and future-proof. This is Part 1 in a 5-part journey, exploring why so many AI system

The tech industry has a voracious appetite for the Next Big Thing. But sometimes, it’s the older thing that ends up being the right tool for a new job.

Multi-modal graphs are everywhere in the digital world. Yet the tools used to understand them haven't evolved as much as one would expect. What if the same model could handle your social network analysis, molecular discovery, AND urban planning tasks? A new paper from Tsinghua University proposes Multi-modal Graph Large Language Models (MG-LLM) - a paradigm shift in how we process complex interconnected data that combines text, images, audio, and structured relationships. Think of it as ChatGPT for graphs, but, metaphorically speaking, with eyes, ears, and structural understanding. Their key insight? Treating all graph tasks as generative problems. Instead of training separate models for node classification, link prediction, or graph reasoning, MG-LLM frames everything as transforming one multi-modal graph into another. This unified approach means the same model that predicts protein interactions could also analyze social media networks or urban traffic patterns. What makes this particularly exciting is the vision for natural language interaction with graph data. Imagine querying complex molecular structures or editing knowledge graphs using plain English, without learning specialized query languages. The challenges remain substantial - from handling the multi-granularity of data (pixels to full images) to managing multi-scale tasks (entire graph input, single node output). But if successful, this could fundamentally change the level of graph-based insights across industries that have barely scratched the surface of AI adoption. ↓ 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐤𝐞𝐞𝐩 𝐮𝐩? Join my newsletter with 50k+ readers and be the first to learn about the latest AI research: llmwatch.com 💡

The European Union's (EU) Carbon Border Adjustment Mechanism (CBAM) aims to prevent carbon leakage and accelerate global decarbonization by aligning carbon

Transform a relational database into a knowledge graph using cognee and Kuzu, enabling advanced querying and semantic search on your data.

Anomaly detection with GNNs. Contribute to GraGODs/GraGOD development by creating an account on GitHub.

Gartner 2025 AI Hype Cycle: The focus is shifting from hype to foundational innovations Knowledge Graphs are a key part of the shift, positioned on the slope of enlightenment By Haritha Khandabattu and Birgi Tamersoy: Al investment remains strong, but focus is shifting from GenAl hype to foundational innovations like Al-ready data, Al agents, Al engineering and ModelOps. This research helps leaders prioritize high-impact, emerging Al techniques while navigating regulatory complexity and operational scaling. As Gartner notes, Generative AI capabilities are advancing at a rapid pace and the tools that will become available over the next 2-5 years will be transformative. The rapid evolution of these technologies and techniques continues unabated, as does the corresponding hype, making this tumultuous landscape difficult to navigate. These conditions mean GenAI continues to be a top priority for the C-suite. Weaving in another foundational concept, Systems of Intelligence as coined by Geoffrey Moore and reference by David Vellante and George Gilbert: Systems of Intelligence are the linchpin of modern enterprise architecture because [AI] agents are only as smart as the state of the business represented in the knowledge graph. If a platform controls that graph, it becomes the default policymaker for “why is this happening, what comes next, and what should we do?” For enterprises, there is only one feasible answer to the "who controls the graph" question: you should. To do that, start working on your enterprise knowledge graph today, if you haven't already. And if you are looking for the place to learn, network, and share experience and knowledge, look no further 👇 Connected Data London 2025 has been announced! 20-21 November, Leonardo Royal Hotel London Tower Bridge Join us for all things #KnowledgeGraph #Graph #analytics #datascience #AI #graphDB #SemTech 🎟️ Ticket sales are open. Benefit from early bird prices with discounts up to 30%. 2025.connected-data.london 📋 Call for submissions is open. Check topics of interest, submission process and evaluation criteria https://lnkd.in/dhbAeYtq 📺 Sponsorship opportunities are available. Maximize your exposure with early onboarding. Contact us at info@connected-data.london for more.

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Multi-modal graphs are everywhere in the digital world. Yet the tools used to understand them haven't evolved as much as one would expect. What if the same model could handle your social network analysis, molecular discovery, AND urban planning tasks? A new paper from Tsinghua University proposes Multi-modal Graph Large Language Models (MG-LLM) - a paradigm shift in how we process complex interconnected data that combines text, images, audio, and structured relationships. Think of it as ChatGPT for graphs, but, metaphorically speaking, with eyes, ears, and structural understanding. Their key insight? Treating all graph tasks as generative problems. Instead of training separate models for node classification, link prediction, or graph reasoning, MG-LLM frames everything as transforming one multi-modal graph into another. This unified approach means the same model that predicts protein interactions could also analyze social media networks or urban traffic patterns. What makes this particularly exciting is the vision for natural language interaction with graph data. Imagine querying complex molecular structures or editing knowledge graphs using plain English, without learning specialized query languages. The challenges remain substantial - from handling the multi-granularity of data (pixels to full images) to managing multi-scale tasks (entire graph input, single node output). But if successful, this could fundamentally change the level of graph-based insights across industries that have barely scratched the surface of AI adoption. ↓ 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐤𝐞𝐞𝐩 𝐮𝐩? Join my newsletter with 50k+ readers and be the first to learn about the latest AI research: llmwatch.com 💡

HippoRAG is a technique inspired from the interactions between the cortex and hippocampus to improve knowledge retrieval for large language models (LLM).

Navigating the impact of graph databases and how AI agents address the limitations of static graphs with continuous knowledge base…

In this Breaking Analysis we examine how Snowflake moves Beyond Walled Gardens and is entering a world where it faces new competitive dynamics from SaaS vendors like Salesforce, ServiceNow, Palantir and of course Databricks.

📣 AI Engineer World's Fair 2025: GraphRAG Track Spotlight! 🚀 So grateful to have hosted the GraphRAG Track at the Fair. The sessions were great, highlighting the depth and breadth of graph thinking for AI. Shoutouts to... - Mitesh Patel "HybridRAG" as a fusion of graph and vector retrieval designed to master complex data interpretation and specialized terminology for question answering - Chin Keong Lam "Wisdom Discovery at Scale" using Knowledge Augmented Generation (KAG) in a multi agent system with n8n - Sam Julien "When Vectors Break Down" carefully explaining how graph-based RAG architecture achieved a whopping 86.31% accuracy for dense enterprise knowledge - Daniel Chalef "Stop Using RAG as Memory" explored temporally-aware knowledge graphs, built by the open-source Graphiti framework, to provide precise, context-rich memory for agents, - Ola Mabadeje "Witness the power of Multi-Agent AI & Network Knowledge Graphs" showing dramatic improvements in ticket resolution efficiency and overall execution quality in network operations. - Thomas Smoker "Beyond Documents"! casually mentioning scraping the entire internet to distill a knowledge graph focused with legal agents - Mark Bain hosting an excellent Agentic Memory with Knowledge Graphs lunch&learn, with expansive thoughts and demos from Vasilije Markovic Daniel Chalef and Alexander Gilmore Also, of course, huge congrats to Shawn swyx W and Benjamin Dunphy on an excellent conference. 🎩 #graphrag Neo4j AI Engineer

Semantic technology and AI are revolutionizing how we find information online: content creation, SEO, reshaping user discovery in the digital world.

Ontology Alignment (OA) is fundamental for achieving semantic interoperability across diverse knowledge systems. We present OntoAligner, a comprehensive, modular, and robust Python toolkit for...

Contribute to GDD-Nantes/SPARQLLM development by creating an account on GitHub.

Repository for the Procedural Knowledge Ontology (PKO) - GitHub - perks-project/pk-ontology: Repository for the Procedural Knowledge Ontology (PKO)

Last week, I was happy to be able to attend the 22nd European Semantic Web Conference. I’m a regular at this conference and it’s great to see many friends and colleagues as well as meet…

This article present what graph embeddings are, their use, and the comparison of the most commonly used graph embedding approaches.

A Practical Implementation

In a knowledge graph, more expressive nodes are clearly more useful, dramatically more valuable nodes – when we focus on the right nodes. This was a key lesson I learned building knowledge graphs at LinkedIn with the terrific team that I assembled.