By a mildly exasperated knowledge engineer

Introducing metis: an enterprise AI platform from metaphactory. Get trusted, context-aware, knowledge-driven AI for actionable insights & intelligent agents.

𝐁𝐨𝐨𝐤 𝐩𝐫𝐨𝐦𝐨𝐭𝐢𝐨𝐧 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐢𝐬 𝐨𝐧𝐞 𝐢𝐬 𝐰𝐨𝐫𝐭𝐡 𝐢𝐭.. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐚𝐭 𝐢𝐭𝐬 𝐛𝐞𝐬𝐭.. This masterpiece was published by Salvatore Raieli and Gabriele Iuculano, and it is available for orders from today, and it's already a 𝐁𝐞𝐬𝐭𝐬𝐞𝐥𝐥𝐞𝐫! While many resources focus on LLMs or basic agentic workflows, what makes this book stand out is its deep dive into grounding LLMs with real-world data and action through the powerful combination of 𝘙𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭-𝘈𝘶𝘨𝘮𝘦𝘯𝘵𝘦𝘥 𝘎𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯 (𝘙𝘈𝘎) 𝘢𝘯𝘥 𝘒𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘎𝘳𝘢𝘱𝘩𝘴. This isn't just about building Agents; it's about building AI that reasons, retrieves accurate information, and acts autonomously by leveraging structured knowledge alongside advanced LLMs. The book offers a practical roadmap, packed with concrete Python examples and real-world case studies, guiding you from concept to deployment of intelligent, robust, and hallucination-minimized AI solutions, even orchestrating multi-agent systems. Order your copy here - https://packt.link/RpzGM #AI #LLMs #KnowledgeGraphs #AIAgents #RAG #GenerativeAI #MachineLearning

Design infrastructure for a generative AI application with GraphRAG using Spanner Graph.

How should we organize knowledge to provide the best context for agents? We show how knowledge graphs could play a key role in enhancing context for agents.

This study addresses the challenges of tracking and analyzing students' learning trajectories, particularly the issue of inadequate knowledge coverage in course assessments. Traditional assessment...

We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. Our system leverages large language models to simultaneously...

I've been working on autonomous AI systems, and wanted to share some thoughts on what I believe makes them effective. The challenge isn't just making AI that follows instructions well, but creating systems that can reason, and act independently.

LLMs already contain overlapping world models. You just have to ask them right. Ontologists reply to an LLM output, “That’s not a real ontology—it’s not a formal conceptualization.” But that’s just the No True Scotsman fallacy dressed up in OWL. Boring. Not growth-oriented. Look forward, angel. A foundation model is a compression of human knowledge. The real problem isn't that we "lack a conceptualization". The real problem with an FM is that they contain too many. FMs contain conceptualizations—plural. Messy? Sure. But usable. At Stardog, we’re turning this latent structure into real ontologies using symbolic knowledge distillation. Prompt orchestration → structure extraction → formal encoding. OWL, SHACL, and friends. Shake till mixed. Rinse. Repeat. Secret sauce simmered and reduced. This isn't theoretical hard. We avoid that. It’s merely engineering hard. We LTF into that! But the payoff means bootstrapping rich, new ontologies at scale: faster, cheaper, with lineage. It's the intersection of FM latent space, formal ontology, and user intent expressed via CQs. We call it the Symbolic Latent Layer (SLL). Cute eh? The future of enterprise AI isn’t just documents. It’s distilling structured symbolic knowledge from LLMs and plugging it into agents, workflows, and reasoning engines. You don’t need a priesthood to get a formal ontology anymore. You need a good prompt and a smarter pipeline and the right EKG platform. There's a lot more to say about this so I said it at Stardog Labs https://lnkd.in/eY5Sibed | 17 comments on LinkedIn

Graph is the new star schema. Change my mind. Why? Your agents can't be autonomous unless your structured data is a graph. It is really very simple. 1️⃣ To act autonomously, an agent must reason across structured data. Every autonomous decision - human or agent - hinges on a judgment: have I done enough? “Enough" boils down to driving the probability of success over some threshold. 2️⃣ You can’t just point the agent at your structured data store. Context windows are too small. Schema sprawl is too real. If you think it works, you probably haven’t tried it. 3️⃣ Agent must first retrieve - with RAG - the right tables, columns, and snippets. Decision making is a retrieval problem before it’s a reasoning problem. 4️⃣ Standard RAG breaks on enterprise metadata. The corpus is too entity-rich. Semantic similarity is breaking on enterprise help articles - it won't perform on column descriptions. 5️⃣ To make structured RAG work, you need a graph. Just like unstructured RAG needed links between articles, structured RAG needs links between tables, fields, and - most importantly - meaning. Yes, graphs are painful. But so was deep learning—until the return was undeniable. Agents need reasoning over structured data. That makes graphs non-optional. The rest is just engineering. Let’s stop modeling for reporting—and start modeling for autonomy. | 28 comments on LinkedIn

❓ How can you turn business questions into production-ready agentic knowledge graphs? Join Prashanth Rao and Dennis Irorere at the Agentic AI Summit to find out. Prashanth is an AI Engineer and DevRel lead at Kùzu Inc.—the open-source graph database startup—where he blends NLP, ML, and data engineering to power agentic workflows. Dennis is a Data Engineer at Tripadvisor’s Viator Marketing Technology team and Director of Innovation at GraphGeeks, driving scalable, AI-driven graph solutions for customer growth. In “Agentic Workflows for Graph RAG: Building Production-Ready Knowledge Graphs,” they’ll guide you through three hands-on lessons: 🔹 From Business Question to Graph Schema – Modeling your domain for downstream agents and LLMs, using live data sources like AskNews. 🔹 From Unstructured Data to Agent-Ready Graphs with BAML – Writing declarative pipelines that reliably extract entities and relationships at scale. 🔹 Agentic Graph RAG in Action – Completing the loop: translating NL queries into Cypher, retrieving graph data, and synthesizing responses—with fallback strategies when matches are missing. If you’re building internal tools or public-facing AI agents that rely on knowledge graphs, this workshop is for you. 🗓️ Learn more & register free: https://hubs.li/Q03qHnpQ0 #AgenticAI #GraphRAG #KnowledgeGraphs #AgentWorkflows #AIEngineering #ODSC #Kuzu #Tripadvisor

Find out how to combine a knowledge graph with RAG for GraphRAG. Provide more complete GenAI outputs.

In this position paper "Integrating Knowledge Graphs with Symbolic AI: The Path to Interpretable Hybrid AI Systems in Medicine" my L3S Research Center and TIB – Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek colleagues around Maria-Esther Vidal have nicely laid out some research challenges on the way to interpretable hybrid AI systems in medicine. However, I think the conceptual framework is broadly applicable way beyond medicine. For example, my former colleagues and PhD students at eccenca are working on operationalizing Neuro-Symbolic AI for Enterprise Knowledge Management with eccenca's Corporate Memory. The paper outlines a compelling architecture for combining sub-symbolic models (e.g., deep learning) with symbolic reasoning systems to enable AI that is interpretable, robust, and aligned with human values. eccenca implements these principles at scale through its neuro-symbolic Enterprise Knowledge Graph platform, Corporate Memory for real-world industrial settings: 1. Symbolic Foundation via Semantic Web Standards - Corporate Memory is grounded in W3C standards (RDF, RDFS, OWL, SHACL, SPARQL), enabling formal knowledge representation, inferencing, and constraint validation. This allows to encode domain ontologies, business rules, and data governance policies in a machine-interpretable and human-verifiable manner. 2. Integration of Sub-symbolic Components - it integrates LLMs and ML models for tasks such as schema matching, natural language interpretation, entity resolution, and ontology population. These are linked to the symbolic layer via mappings and annotations, ensuring traceability and explainability. 3. Neuro-Symbolic Interfaces for Hybrid Reasoning - Hybrid workflows where symbolic constraints (e.g., SHACL shapes) guide LLM-based data enrichment. LLMs suggest schema alignments, which are verified against ontological axioms. Graph embeddings and path-based querying power semantic search and similarity. 4. Human-in-the-loop Interactions - Domain experts interact through low-code interfaces and semantic UIs that allow inspection, validation, and refinement of both the symbolic and neural outputs, promoting human oversight and continuous improvement. Such an approach can power Industrial Applications, e.g. in digital thread integration in manufacturing, compliance automation in pharma and finance and in general, cross-domain interoperability in data mesh architectures. Corporate Memory is a practical instantiation of neuro-symbolic AI that meets industrial-grade requirements for governance, scalability, and explainability – key tenets of Human-Centric AI. Check it out here: https://lnkd.in/evyarUsR #NeuroSymbolicAI #HumanCentricAI #KnowledgeGraphs #EnterpriseArchitecture #ExplainableAI #SemanticWeb #LinkedData #LLM #eccenca #CorporateMemory #OntologyDrivenAI #AI4Industry

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

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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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.

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

A Practical Implementation

The quest to build truly intelligent systems hinges on our ability to represent, manage, and reason over complex knowledge. Knowledge…