After seeing one too many Neo4j tutorials teaching developers to hide nouns inside relationships, I’ve had enough. Here’s why this…

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Simple way of starting with ontologies: start with your favorite LLM and the following prompt:

Graph-constrained Reasoning (GCR) is a novel framework that bridges structured knowledge in knowledge graphs (KG) with unstructured reasoning in LLMs.

Large language models (LLMs) have demonstrated remarkable capabilities in a wide range of tasks, yet their application to specialized domains remains challenging due to the need for deep...

Labels as the Real Semantic Layer of LPG in GraphRAG and Applied Knowledge Graphs

Turn your data DataFrames into semantic Knowledge Graphs with only three lines of code.

Your AI agents can't reason about what they can't structurally represent.

Inside LinkedIn’s AI ‘cookbook’: How engineers scaled GenAI to 1.3B users by distilling models to 220M parameters for a 10x throughput gain.

Why schema, storage, and indexing matter more than model tweaks in building effective graph-grounded RAG systems

I used the o word last week and it hit a few nerves. Ontologies bring context. But then context engineering is very poorly understood. Agent engineers speak about it, expect everyone is doing it, know but almost everyone is winging it. Here's what context engineering is definitely not - ie. longer prompts. What it actually is - the right information, with the right meaning, at the right time. Not more but the right information with the right meaning. Sounds super abstract. That's why a brief video that actually breaks down how to load context. Okay. Not brief. but context needs context.

Your LLM based agent can't reason about what it can't traverse.

Alhamdulillah, ATOM is finally here! A scalable and fast approach that can build and continuously update temporal knowledge graphs, inspired by atomic bonds. Just as matter is formed from atoms, and galaxies are formed from stars, knowledge is likely to be formed from atomic knowledge graphs. Atomic knowledge graphs were born from our intention to solve a common problem in LLM-based KG construction methods: exhaustivity and stability. Often, these methods produce unstable KGs that change when rerunning the construction process, even without changing anything. Moreover, they fail to capture all facts in the input documents and usually overlook the temporal and dynamic aspects of real-world data. What is the solution? Atomic facts that are temporally aware. Instead of constructing knowledge graphs from raw documents, we split them into atomic facts, which are self-contained and concise propositions. Temporal atomic KGs are constructed from each atomic fact. Then, we defined how the temporal atomic KGs would be merged at the atomic level and how the temporal aspects would be handled. We designed a binary merge algorithm that combines two TKGs and a parallel merge process that merges all TKGs simultaneously. The entire architecture operates in parallel. ATOM employs dual-time modeling that distinguishes observation time from validity time and has 3 main modules: - Module 1 (Atomic Fact Decomposition) splits input documents observed at time t into atomic facts using LLM-based prompting, where each temporal atomic fact is a short, self-contained snippet that conveys exactly one piece of information. - Module 2 (Atomic TKGs Construction) extracts 5-tuples in parallel from each atomic fact to construct atomic temporal KGs, while embedding nodes and relations and addressing temporal resolution during extraction. - Module 3 (Parallel Atomic Merge) employs a binary merge algorithm to merge pairs of atomic TKGs through iterative pairwise merging in parallel until convergence, with three resolution phases: (1) entity resolution, (2) relation name resolution, and (3) temporal resolution that merges observation and validity time sets for relations with similar (e_s, r_p, e_o). The resulting TKG snapshot is then merged with the previous DTKG to yield the updated DTKG. Results: Empirical evaluations demonstrate that ATOM achieves ~18% higher exhaustivity, ~17% better stability, and over 90% latency reduction compared to baseline methods (including iText2KG), demonstrating strong scalability potential for dynamic TKG construction. Check our ATOM's architecture and code: Preprint Paper: https://lnkd.in/dsJzDaQc Code: https://lnkd.in/drZUyisV Website: (coming soon) Example use cases: (coming soon) Special thanks to the dream team: Ludovic Moncla, Khalid Benabdeslem, Rémy Cazabet, Pierre Cléau 📚📡 | 14 comments on LinkedIn

After seeing yet another Graph RAG demo using Neo4j with no ontology, I decided to show what real semantic Graph RAG looks like. The Problem with Most Graph RAG Demos: Everyone's building Graph RAG with LPG databases (Neo4j, TigerGraph, Arrango etc.) and calling it "knowledge graphs." But here's the thing: Without formal ontologies, you don't have a knowledge graph—you just have a graph database. The difference? ❌ LPG: Nodes and edges are just strings. No semantics. No reasoning. No standards. ✅ RDF/SPARQL: Formal ontologies (RDFS/OWL) that define domain knowledge. Machine-readable semantics. W3C standards. Built-in reasoning. So I Built a Real Semantic Graph RAG Using: - Microsoft Agent Framework - AI orchestration - Formal ontologies - RDFS/OWL knowledge representation - Ontotext GraphDB - RDF triple store - SPARQL - semantic querying - GPT-5 - ontology-aware extraction It's all on github, a simple template as boilerplate for you project: The "Jaguar problem": What does "Yesterday I was hit by a Jaguar" really mean? It is impossible to know without concept awareness. To demonstrate why ontologies matter, I created a corpus with mixed content: 🐆 Wildlife jaguars (Panthera onca) 🚗 Jaguar cars (E-Type, XK-E) 🎸 Fender Jaguar guitars I fed this to GPT-5 along with a jaguar conservation ontology. The result? The LLM automatically extracted ONLY wildlife-related entities—filtering out cars and guitars—because it understood the semantic domain from the ontology. No post-processing. No manual cleanup. Just intelligent, concept-aware extraction. This is impossible with LPG databases because they lack formal semantic structure. Labels like (:Jaguar) are just strings—the LLM has no way to know if you mean the animal, car, or guitar. Knowledge Graphs = "Data for AI" LLMs don't need more data—they need structured, semantic data they can reason over. That's what formal ontologies provide: ✅ Domain context ✅ Class hierarchies ✅ Property definitions ✅ Relationship semantics ✅ Reasoning rules This transforms Graph RAG from keyword matching into true semantic retrieval. Check Out the Full Implementation, the repo includes: Complete Graph RAG implementation with Microsoft Agent Framework Working jaguar conservation knowledge graph Jupyter notebook: ontology-aware extraction from mixed-content text https://lnkd.in/dmf5HDRm And if you have gotten this far, you realize that most of this post is written by Cursor ... That goes for the code too. 😁 Your Turn: I know this is a contentious topic. Many teams are heavily invested in LPG-based Graph RAG. What are your thoughts on RDF vs. LPG for Graph RAG? Drop a comment below! #GraphRAG #KnowledgeGraphs #SemanticWeb #RDF #SPARQL #AI #MachineLearning #LLM #Ontology #KnowledgeRepresentation #OpenSource #neo4j #graphdb #agentic-framework #ontotext #agenticai | 148 comments on LinkedIn

✅ Some state-of-the-art methods for knowledge graph (KG) construction that implement incrementality build a graph from around 3k atomic facts in 4–7 hours, while ATOM achieves the same in just 20 minutes using only 8 parallel threads and a batch size of 40 for asynchronous LLM API calls. ❓ What’s the secret behind this performance? 👉 The architecture. The parallel design. ❌ Incrementality in KG construction was key, but it significantly limits scalability. This is because the method must first build the KG and compare it with the previous one before moving on to the next chunk. That’s why we eliminated this in iText2KG. ❓ Why is scalability so important? The short answer: real-time analytics. Fast dynamic TKG construction enables LLMs to reason over them and generate responses instantly, in real time. Discover more secrets behind this parallel architecture by reading the full paper (link in the first comment).

How to stay sane about “semantic Graph RAG” when your job is shipping reliable systems, not winning ontology theology wars. You don’t wake up in the morning thinking about OWL profiles or SPARQL entailment regimes.

Looking to run local GraphRAG or other graph analytics use cases? With DGX Spark, you can prepare your local text files for graph use cases at your desk.In t...

🚀 LLMs can be powerful tools to extract information from texts and automatically populate Knowledge Graphs guided by ontologies given as inputs. BUT how good are they? To reply to this question, we need benchmarks! 💡 With Lettria, we build the Text2KGBench-LettrIA benchmark covering 19 different ontologies in various domains (company, film, food, politician, sports, monument, etc.) and consisting of near 5k sentences strictly annotated with triples conforming to these ontologies (208 classes, 426 properties) yielding more than 17k triples. What's more? We throw a lot of compute to compare the performance and efficiency of numerous Closed LLMs models and variants (GPT4, Claude 3, Gemini) and numerous fine-tuned Open Weights models (Mistral 3, Qwen 3, Gemma 3, Phi 4). ✨Key take-away: when being provided with high quality data, fine-tuned open models largely outperform larger, proprietary counterparts! 📄 Curious about the detailed results? Read our paper at https://lnkd.in/e-EZCjWm See our presentation at https://lnkd.in/eEdCCpdA that I have just presented at the Knowledge Base Construction from Pre-Trained Language Models Workshop colocated with the ISWC - International Semantic Web Conference. You want to use these results in your operations? Sign-up for using the newly released PERSEUS model, https://lnkd.in/e7exyJHc Joint work with Julien PLU, Oscar Moreno Escobar, Edouard Trouillez, Axelle Gapin, Pasquale Lisena, Thibault Ehrhart #iswc2025 #LLMs #KnowledgeGraphs #NLP #Research EURECOM, Charles Borderie

🎧 Exciting news! The audiobook version of "Knowledge Graphs and LLMs in Action" is now available! Are you busy but would love to learn how to build powerful and explainable AI solutions? No problem! Manning has just released the audio version of our book. Now you can listen while you're: - Running and training for your next marathon 🏃 - Commuting to the office 🚗 - Sitting in the parking lot waiting for your kids to finish their violin lesson 🎻 Your schedule is packed, but that shouldn't stop you from mastering these powerful AI techniques. Get your copy here: https://hubs.la/Q03MVhhk0 And don't forget to use discount code: lagraphs40 for 40% off! Clever solutions for smart people.

Knowledge Graphs (KGs) have gained attention due to their ability to represent structured and interlinked information. KGs represent knowledge in the form of relations between entities, referred to as...

How semantic layers and GraphRAG provide the foundation for building trustworthy AI systems that can deliver reliable results.

Build AI agents with LangGraph and cognee: persistent semantic memory across sessions for cleaner context and higher accuracy. See the demo—get started now.

Your agents NEED a semantic layer 🫵 Traditional RAG systems embed documents, retrieve similar chunks, and feed them to LLMs. This works for simple Q&A. It fails catastrophically for agents that need to reason across systems. Why? Because semantic similarity doesn't capture relationships. Your vector database can tell you that two documents are "about bonds." It can't tell you that Document A contains the official pricing methodology, Document B is a customer complaint referencing that methodology, and Document C is an assembly guide that superseded both. These relationships are invisible to embeddings. What semantic layers provide: Entity resolution across data silos. When "John Smith" in your CRM, "J. Smith" in email, and "john.smith@company.com" in logs all map to the same person node, agents can traverse the complete context. Cross-domain entity linking through knowledge graphs. Products in your database connect to assembly guides, which link to customer reviews, which reference support tickets. Single-query traversal instead of application-level joins. Provenance-tracked derivations. Every extracted entity, inferred relationship, and generated embedding maintains lineage to source data. Critical for regulatory compliance and debugging agent behavior. Ontology-grounded reasoning. Financial instruments mapped to FIBO standards. Products mapped to domain taxonomies. Agents reason with structured vocabulary, not statistical word associations. The technical implementation pattern: Layer 1: Unified graph database supporting vector, structured, and semi-structured data types in single queries. Layer 2: Entity extraction pipeline with coreference resolution and deduplication across sources. Layer 3: Relationship inference and cross-domain linking using both explicit identifiers and contextual signals. Layer 4: Separation of first-party data from derived artifacts with clear tagging for safe regeneration. The result: Agents can traverse "Product → described_in → AssemblyGuide → improved_by → CommunityTip → authored_by → Expert" in a single graph query instead of five API calls with application-level joins. Model Context Protocol is emerging as the open standard for semantic tool modeling. Not just describing APIs, but encoding what tools do, when to use them, and how outputs compose. This enables agents to discover and reason about capabilities dynamically. The competitive moat isn't your model choice. The moat is your knowledge graph architecture and the accumulated entity relationships that took years to build. | 28 comments on LinkedIn

🕸️ Can LLMs Really Build Knowledge Graphs We Can Trust? There’s a growing trend: “Let’s use LLMs to build knowledge graphs.” It sounds like the perfect shortcut - take unstructured data, prompt an LLM, and get a ready-to-use graph. But… are we sure those graphs are trustworthy? Before that, let’s pause for a second: 💡 Why build knowledge graphs at all? Because they solve one of AI’s biggest weaknesses - lack of structure and reasoning. Graphs let us connect facts, entities, and relationships in a way that’s transparent, queryable, and explainable. They give context, memory, and logic - everything that raw text or embeddings alone can’t provide. Yet, here’s the catch when using LLMs to build them: 🔹 Short context window - LLMs can only “see” a limited amount of data at once, losing consistency across larger corpora. 🔹 Hallucinations - when context runs out or ambiguity appears, models confidently invent facts or relations that never existed. 🔹 Lack of provenance - LLM outputs don’t preserve why or how a link was made. Without traceability, you can’t audit or explain your graph. 🔹 Temporal instability - the same prompt can yield different graphs tomorrow, because stochastic generation ≠ deterministic structure. 🔹 Scalability & cost - large-scale graph construction requires persistent context and reasoning, which LLMs weren’t designed for. Building knowledge graphs isn’t just data extraction - it’s engineering meaning. It demands consistency, provenance, and explainability, not just text generation. LLMs can assist in this process, but they shouldn’t be the architect. The next step is finding a way to make graphs both trustworthy and instant - without compromising one for the other. | 11 comments on LinkedIn

"GraphRAG chatter is louder than its footprint in production." That line from Ben Lorica's piece on Gradient Flow stopped me in my tracks: https://lnkd.in/dmC-ykAu I was reading it because of my deep interest in graph-based reasoning, and while the content is excellent, I was genuinely surprised by the assessment of GraphRAG adoption. The article suggests that a year after the initial buzz, GraphRAG remains mostly confined to graph vendors and specialists, with little traction in mainstream AI engineering teams. Here's the thing: at GraphAware, we have GraphRAG running in production: our AskTheDocs conversational interface in Hume uses this approach to help customers query documentation, and the feedback has been consistently positive. It's not an experiment—it's a production feature our users rely on daily. So I have a question for my network (yes, I know you're a bit biased—many of you are graph experts, after all 😊): Where is GraphRAG actually working in production? I'm not looking for POCs, experiments, or "we're exploring it." I want to hear about real, deployed systems serving actual users. Success stories. Production use cases. The implementations that are quietly delivering value while the tech commentary wonders if anyone is using this stuff. If you have direct or indirect experience with GraphRAG in production, I'd love to hear from you: - Drop a comment below - Send me a DM - Email me directly I want to give these cases a voice and learn from what's actually working out there. Who's building with GraphRAG beyond the buzz? #GraphRAG #KnowledgeGraphs #AI #ProductionAI #RAG

SuperMemory is just one example of a growing ecosystem of knowledge graph systems (Graphiti- Zep, Fast Graph RAG, TrustGraph...) Some in Python, some TypeScript with the added benefit of having graph visualization. Even in Rust and Go there is a growing list of open source graph-RAG. Ontology (LLM generated in particular) seems to have its own moment in the sun with a growing interest in RDF, OWL, SHACL and whatnot. Whether the big guys (OpenAI, Microsoft...) will launch something ontological remains to be seen. They likely leave it to triple-store vendors to figure it out. https://lnkd.in/e3HAiC8c #KnowledgeGraph #GraphRAG

Is OpenAI quietly moving toward knowledge graphs? Yesterday’s OpenAI DevDay was all about new no-code tools to create agents. Impressive. But what caught my attention wasn’t what they announced… it’s what they didn’t talk about. During the summer, OpenAI released a Cookbook update introducing the concept Temporal Agents (see below) connecting it to Subject–Predicate–Object triples: the very foundation of a knowledge graph. If you’ve ever worked with graphs, you know this means something big: they’re not just building agents anymore they’re building memory, relationships, and meaning. When you see “London – isCapitalOf – United Kingdom” in their official docs, you realize they’re experimenting with how to represent knowledge itself. And with any good knowledge graph… comes an ontology. So here’s my prediction: ChatGPT-6 will come with a built-in graph that connects everything about you. The question is: do you want their AI to know everything about you? Or do you want to build your own sovereign AI, one that you own, built from open-source intelligence and collective knowledge? Would love to know what you think. Is that me hallucinating or is that a weak signal?👇 | 62 comments on LinkedIn