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yfiles jupyter graphs for sparql: The open-source adapter for working with RDF databases
yfiles jupyter graphs for sparql: The open-source adapter for working with RDF databases
📣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
GitHub - yWorks/yfiles-jupyter-graphs-for-sparql: The open-source adapter for working with RDF databas
·linkedin.com·
yfiles jupyter graphs for sparql: The open-source adapter for working with RDF databases
RDF-to-Gephi
RDF-to-Gephi
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from neo4j), no decent working plugin exist to load RDF data (yes, there was a "SemanticWebImport" plugin, but it looks outdated, with an old documentation, and does not work with latest - 0.10 - version of Gephi).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.
·linkedin.com·
RDF-to-Gephi
Nakala : from an RDF dataset to a query UI in minutes - SHACL automated generation and Sparnatural - Sparna Blog
Nakala : from an RDF dataset to a query UI in minutes - SHACL automated generation and Sparnatural - Sparna Blog
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…
·blog.sparna.fr·
Nakala : from an RDF dataset to a query UI in minutes - SHACL automated generation and Sparnatural - Sparna Blog
GitHub - apache/incubator-hugegraph: A graph database that supports more than 100+ billion data, high performance and scalability (Include OLTP Engine & REST-API & Backends)
GitHub - apache/incubator-hugegraph: A graph database that supports more than 100+ billion data, high performance and scalability (Include OLTP Engine & REST-API & Backends)
A graph database that supports more than 100+ billion data, high performance and scalability (Include OLTP Engine & REST-API & Backends) - apache/incubator-hugegraph
·github.com·
GitHub - apache/incubator-hugegraph: A graph database that supports more than 100+ billion data, high performance and scalability (Include OLTP Engine & REST-API & Backends)
A zero-hallucination AI chatbot that answered over 10000 questions of students at the University of Chicago using GraphRAG
A zero-hallucination AI chatbot that answered over 10000 questions of students at the University of Chicago using GraphRAG
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
a zero-hallucination AI chatbot that answered over 10000 questions of students at the University of Chicago
·linkedin.com·
A zero-hallucination AI chatbot that answered over 10000 questions of students at the University of Chicago using GraphRAG
SimGRAG is a novel method for knowledge graph driven RAG, transforms queries into graph patterns and aligns them with candidate subgraphs using a graph semantic distance metric
SimGRAG is a novel method for knowledge graph driven RAG, transforms queries into graph patterns and aligns them with candidate subgraphs using a graph semantic distance metric
SimGRAG is a novel method for knowledge graph driven RAG, transforms queries into graph patterns and aligns them with candidate subgraphs using a graph…
SimGRAG is a novel method for knowledge graph driven RAG, transforms queries into graph patterns and aligns them with candidate subgraphs using a graph semantic distance metric
·linkedin.com·
SimGRAG is a novel method for knowledge graph driven RAG, transforms queries into graph patterns and aligns them with candidate subgraphs using a graph semantic distance metric
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
🌟 TGB 2.0 @NeurIPS 2024 🌟 We are very happy to share that our paper TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs… | 11 comments on LinkedIn
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
·linkedin.com·
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
graphgeeks-lab/awesome-graph-universe: A curated list of resources for graph-related topics, including graph databases, analytics and science
graphgeeks-lab/awesome-graph-universe: A curated list of resources for graph-related topics, including graph databases, analytics and science
A curated list of resources for graph-related topics, including graph databases, analytics and science - graphgeeks-lab/awesome-graph-universe
Awesome Graph Universe 🌐 Welcome to Awesome Graph Universe, a curated list of resources, tools, libraries, and applications for working with graphs and networks. This repository covers everything from Graph Databases and Knowledge Graphs to Graph Analytics, Graph Computing, and beyond. Graphs and networks are essential in fields like data science, knowledge representation, machine learning, and computational biology. Our goal is to provide a comprehensive resource that helps researchers, developers, and enthusiasts explore and utilize graph-based technologies. Feel free to contribute by submitting pull requests! 🚀
·github.com·
graphgeeks-lab/awesome-graph-universe: A curated list of resources for graph-related topics, including graph databases, analytics and science
cosdata/cosdata: Cosdata: A cutting-edge AI data platform for next-gen search pipelines. Features semantic search, knowledge graphs, hybrid capabilities, real-time scalability, and ML integration. Designed for immutability and version control to enhance AI projects.
cosdata/cosdata: Cosdata: A cutting-edge AI data platform for next-gen search pipelines. Features semantic search, knowledge graphs, hybrid capabilities, real-time scalability, and ML integration. Designed for immutability and version control to enhance AI projects.
Cosdata: A cutting-edge AI data platform for next-gen search pipelines. Features semantic search, knowledge graphs, hybrid capabilities, real-time scalability, and ML integration. Designed for immu...
·github.com·
cosdata/cosdata: Cosdata: A cutting-edge AI data platform for next-gen search pipelines. Features semantic search, knowledge graphs, hybrid capabilities, real-time scalability, and ML integration. Designed for immutability and version control to enhance AI projects.
PyG 2.6 is here
PyG 2.6 is here
🚀 PyG 2.6 is here! 🎉 We’re excited to announce the release of PyG 2.6.0, packed with incredible updates for graph learning! Here’s a quick rundown of what’s… | 14 comments on LinkedIn
PyG 2.6 is here
·linkedin.com·
PyG 2.6 is here