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

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)

Your agents, multi-agent systems and LMs apps are still failing with basic logic? We got you covered. Today we're excited to announce Synalinks 0.3 our Keras-based neuro-symbolic framework that bridges the gap between neural networks and symbolic reasoning. Our latest release focuses entirely on the Knowledge Graph layer, delivering production-ready solutions for real-world applications: - Fully constrained KG extraction powered by Pydantic: ensuring that relations connect to the correct entity types. - Seamless integration with our Agents/Chain-of-Thought and Self-Critique modules. - Automatic entity alignment with HSWN. - KG extraction and retrieval optimizable with OPRO and RandomFewShot algorithms. - 100% reliable Cypher query generation through logic-enhanced hybrid triplet retrieval (works with local models too!). - We took extra care to avoid Cypher injection vulnerabilities (yes, we're looking at you, LangGraph 👀) - The retriever don't need the graph schema, as it is included in the way we constrain the generation, avoiding context pollution (hence better accuracy). - We also fixed Synalinks CLI for Windows users along with some minor bug fixes. Our technology combine constrained structured output with in-context reinforcement learning, making enterprise-grade reasoning both highly efficient and cost-effective. Currently supporting Neo4j with plans to expand to other graph databases. Built this initially for a client project, but the results were too good not to share with the community. Want to add support for your preferred graph database? It's just one file to implement! Drop a comment and let's make it happen! #AI #MachineLearning #KnowledgeGraphs #NeuralNetworks #Keras #Neo4j #AIAgents #TechInnovation #OpenSource | 10 comments on LinkedIn

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

Want to explore the Anthropic Transformer-Circuit's as a queryable graph? Wrote a script to import the graph json into Neo4j - code in Gist. https://lnkd.in/eT4NjQgY https://lnkd.in/e38TfQpF Next step - write directly from the circuit-tracer library to the graph db. https://lnkd.in/eVU_t6mS

A serious knowledge graph effort is much more than a bit of Github, but customers and adventurous minds keep asking me if there is an easy to use (read: POC click-and-go solution) graph RAG open source stack they can use to generate knowledge graphs. So, here is my list of projects I keep an eye on. Mind, there is nothing simple if you venture into graphs, despite all the claims and marketing. Things like graph machine learning, graph layout and distributed graph analytics is more than a bit of pip install. The best solutions are hidden inside multi-nationals, custom made. Equity firms and investors sometimes ask me to evaluate innovations. It's amazing what talented people develop and never shows up in the news, or on Github. TrustGraph - The Knowledge Platform for AI https://trustgraph.ai/ The only one with a distributed architecture and made for enterprise KG. itext2kg - https://lnkd.in/e-eQbwV5 Clean and plain. Wrapped prompts done right. Fast GraphRAG - https://lnkd.in/e7jZ9GZH Popular and with some basic visualization. ZEP - https://lnkd.in/epxtKtCU Geared towards agentic memory. Triplex - https://lnkd.in/eGV8FR56 LLM to extract triples. GraphRAG Local with UI - https://lnkd.in/ePGeqqQE Another starting point for small KG efforts. Or to convince your investors. GraphRAG visualizer - https://lnkd.in/ePuMmfkR Makes pretty pictures but not for drill-downs. Neo4j's GraphRAG - https://lnkd.in/ex_A52RU A python package with a focus on getting data into Neo4j. OpenSPG - https://lnkd.in/er4qUFJv Has a different take and more academic. Microsoft GraphRAG - https://lnkd.in/e_a-mPum A classic but I don't think anyone is using this beyond experimentation. yWorks - https://www.yworks.com If you are serious about interactive graph layout. Ogma - https://lnkd.in/evwnJCBK If you are serious about graph data viz. Orbifold Consulting - https://lnkd.in/e-Dqg4Zx If you are serious about your KG journey. #GraphRAG #GraphViz #GraphMachineLearning #KnowledgeGraphs

Integrating Neo4j, DuckDB, and Kestra for Metadata-Driven Graph Design

Contribute to LMMApplication/RAKG development by creating an account on GitHub.

Official Implementation of "Affordable AI Assistants with Knowledge Graph of Thoughts" - spcl/knowledge-graph-of-thoughts

In this post, we’ll dive into Kuzu’s core query capabilities and demonstrate how it pairs nicely with G.V()’s rich visualization tools.

An open-source Python framework for real-time, graph-based knowledge in Neo4j

How LLMs can used in a knowledge extraction and visualization pipeline

"I'm Ukrainian and I'm wearing a suit, so no complaints about me from the Oval Office" - that's the start of my lecture about building Artificial Intelligence with Croissant ML in the Dataverse data platform, for the Bio x AI Hackathon kick-off event in Berlin. https://lnkd.in/ePYHCfJt * 750,000+ FAIR datasets across the world forcing the innovation of the whole data landscape. * A knowledge graph with 50M+ triples. * AI-ready metadata exports. * Qdrant as a vector storage, Google Meta Mistral AI as LLM model providers. * Adrian Gschwend Qlever as fastest triple store for Dataverse knowledge graphs Multilingual, machine-readable, queryable scientific data at scale. If you're interested, you can also apply for the 2-month #BioAgentHack online hackathon: • $125K+ prizes • Mentorship from Biotech and AI leaders • Build alongside top open-science researchers & devs More info: https://lnkd.in/eGhvaKdH

Transform UML into a formal OWL ontology and SHACL shapes - OP-TED/model2owl

What’s So Special About Apache Iceberg? Apache Iceberg is one of the most fascinating technologies when it comes to standardized access to large analytic tables. And Apache Iceberg combines very well with the idea of virtual knowledge graphs.

🎉 We're thrilled to unveil Synalinks (🧠🔗), an open-source framework designed to streamline the creation, evaluation, training, and deployment of…

📣Hey Semantic Web/SPARQL/RDF/OWL/Knowledge graph community: Finally! We heard you! I just got this fresh from the dev kitchen: 🎉 Try our free SPARQL query result visualization widget for Jupyter Notebooks! Based on our popular generic graph visualization widget for Jupyter, this widget makes it super convenient to add beautiful graph visualizations of your SPARQL queries to your Jupyter Notebooks. Check out the example notebooks for Google Colab in the GitHub repo https://lnkd.in/e8JP-eiM ✨ This is a pre-1.0-release but already quite capable, as it builds on the well-tested generic widget. We are looking to get your feedback on the features for the final release, so please do take a look and let me know your feedback here, or tell us on GitHub! What features are you missing? What do you like best about the widget? Let me know in the comments and I'll talk to the devs 😊 #sparql #rdf #owl #semanticweb #knowledgegraphs #visualization

I have never been a fan of the "bubble and arrows" kind of graph visualizations. It is generaly useless. But when you can see the entire graph, and can tune the rendering, you start understanding the topology and structure - and ultimately you can tell a story with your graph (and that's what we all love, stories). Gephi is a graph visualization tool to tell these sort of stories with graphs, that has been around for 15 (20 ?) years. Interestingly, while quite a number of Gephi plugins exist to load data (including from neo4j), no decent working plugin exist to load RDF data (yes, there was a "SemanticWebImport" plugin, but it looks outdated, with an old documentation, and does not work with latest - 0.10 - version of Gephi). This doesn't tell anything good for the semantic knowledge graph community. A few weeks ago I literally stumbled upon an old project we developed in 2017 to convert RDF graphs into the GEXF format that can be loaded in Gephi. Time for a serious cleaning, reengineering, and packaging ! So here is a v1.0.0 of the rebranded rdf2gephi utility tool ! The tool runs as a command line that can read an RDF knowledge graph (from files or a SPARQL endpoint), execute a set of SPARQL queries, and turn that into a set of nodes and edges in a GEXF file. rdf2gephi provides default queries to run a simple conversion without any parameters, but most of the time you will want to tune how your graph is turned into GEXF nodes and edges (for example, in my case, `org:Membership` entities relating `foaf:Persons` with `org:Organizations` are not turned into nodes, but into edges, and I want to ignore some other entities). And then what ? then you can load the GEXF file in Gephi, and run a few operations to showcase your graph (see the little screencast video I recorded) : run a layout algorithm, color nodes based on their rdf:type or another attribute you converted, change their size according to the (in-)degree, detect clusters based on a modularity algorithm, etc. etc. - and then export as SVG, PNG, or another format. Also, one of the cool feature supported by the GEXF format are dynamic graphs, where each nodes and edges can be associated to a date range. You can then see your graph evolving through time, like in a movie ! I hope I will be able to tell a more concrete Gephi-powered, RDF-backed graph-story in a future post ! All links in comments.

Here is a usecase of an automated version of Sparnatural submitted as an example for Veronika Heimsbakk’s SHACL for the Practitioner upcoming book about the Shapes Constraint Language (SHACL). “ The Sparnatural knowledge graph explorer leverages SHACL specifications to drive a user interface (UI) that allows end users to easily discover the content of an RDF graph. What…

UChicago Genie is now open source! How we built a zero-hallucination AI chatbot that answered over 10000 questions of students at the University of… | 25 comments on LinkedIn

Graph Retrieval Augmented Generation (Graph RAG) is emerging as a powerful addition to traditional vector search retrieval methods. Graphs are great at repre...

We contributed recently to the "awesome semantic shapes" repository. This is a community-curated list of RDF shape resources, be it validators, generators…

Authors: Makbule Gulcin Ozsoy, Leila Messallem, Jon Besga

We're excited to publicly release the Diffbot GraphRAG LLM! With larger and larger frontier LLMs, we realized that they would eventually hit a limit in terms… | 48 comments on LinkedIn

What if creating Linked Open Data was less like coding and more like writing? Could anyone extend the Semantic Web by sharing a document? Publish a knowledge… | 13 comments on LinkedIn

Join us for the first GraphGeeks Talk of 2025!! 🌐 "Exploring News at Scale with On-the-Fly Graphs" 🗓️ January 15th at 9:00 AM PT | 17:00 London Join…