Data Graphs' new contextual rendered data views change the way we navigate and view data in multimodal knowledge graphs.

A practical guide to visualizing and exploring knowledge graphs (RDF/OWL and property graphs) with yFiles: predicate-aware analysis, schema vs. instance views, appropriate layouts, semantic styling, and interaction patterns like predicate filters and progressive disclosure.

The world of graph technology is vast and evolving. We're here to help you make sense of the key trends and players.

Calling all Graph Explorers! 📣 I'm excited to share that open-source Graph Explorer v2.4.0 is now released, and it includes a new SPARQL editor! Release notes: https://lnkd.in/ePhwPQ5W This means that in addition to being a powerful no-code exploration tool, you can now start your visualization and exploration by writing queries directly in SPARQL. (Gremlin & openCypher too for Property Graph workloads). This makes Graph Explorer an ideal companion for Amazon Neptune, as it supports connections via all three query languages, but you can connect to other graph databases that support these languages too. 🔹 Run it anywhere (it's open source): https://lnkd.in/ehbErxMV 🔹 Access through the AWS console in a Neptune graph notebook: https://lnkd.in/gZ7CJT8D Special thanks go to Kris McGinnes for his efforts. #AWS #AmazonNeptune #GraphExplorer #SPARQL #Gremlin #openCypher #KnowledgeGraph #OpenSource #RDF #LPG

Transform Claude's Hidden Memory Into Interactive Knowledge Graphs Universal tool to visualize any Claude user's memory.json in beautiful interactive graphs. Transform your Claude Memory MCP data into stunning interactive visualizations to see how your AI assistant's knowledge connects and evolves over time. Enterprise teams using Claude lack visibility into how their AI assistant accumulates and organizes institutional knowledge. Claude Memory Viz provides zero-configuration visualization that automatically finds memory files and displays 72 entities with 93 relationships in real-time force-directed layouts. Teams can filter by entity type, search across all data, and explore detailed connections through rich tooltips. The technical implementation supports Claude's standard NDJSON memory format, automatically detecting and color-coding entity types from personality profiles to technical tools. Node size reflects connection count, while adjustable physics parameters enable optimal spacing for large knowledge graphs. Built with Cytoscape.js for performance optimization. Built with the philosophy "Solve it once and for all," the tool works for any Claude user with zero configuration. The visualizer automatically searches common memory file locations, provides demo data fallback, and offers clear guidance when files aren't found. Integration requires just git clone and one command execution. This matters because AI memory has been invisible to users, creating trust and accountability gaps in enterprise AI deployment. When teams can visualize how their AI assistant organizes knowledge, they gain insights into decision-making patterns and can optimize their AI collaboration strategies. 👩‍💻https://lnkd.in/e__RQh_q | 10 comments on LinkedIn

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.

Discover how to empower LLM development through effective knowledge graph visualization. Learn to leverage yFiles for intuitive, interactive diagrams that simplify debugging and optimization in AI applications.

🌟 Calling all teachers, students and practitioners in the Semantic Web and knowledge graph community! 🌟 Looking for a fresh, engaging dataset to build… | 18 comments on LinkedIn

📣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.

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Navigating the EU AI Act: Introducing the AI Act Requirements Graph As the EU AI Act moves closer to becoming law, understanding its complex web of… | 11 comments on LinkedIn