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...

The paper 'Property Graph Standards: State of the Art and Open Challenges' (VLDB 2025), Haridimos Kondylakis and his colleagues take an in-depth look at the current state of property graph standards, which form the basis of many modern graph databases. While property graphs have become a popular way to show complex, connected data (think nodes and edges with flexible key–value properties), the ecosystem is still divided. Each vendor or tool implements its own version of 'the standard', which makes interoperability, schema definition and query translation difficult. The authors review the major initiatives to standardise property graphs and demonstrate the current situation: efforts from LDBC, GQL and ISO are advancing the field, but challenges remain. The biggest gaps lie in schema constraints, data validation, and cross-system compatibility — all of which are crucial if graph systems are to become integral components of enterprise data architectures. The paper calls for a unified model in which graph structure, constraints, and semantics are shared across tools and databases. This isn't just academic. It's about ensuring that graph data can be trusted. It's also about making sure that it is portable. And that it can be used at scale. In simple terms, property graphs are maturing. The next step is not just to connect data, but to agree on how we define, validate and exchange those connections. Article: https://lnkd.in/eva_xSsT

"Semantic layer” means different things depending on who you ask. In my latest newsletter, published on Medium first this time, I look at the two definitions and how they can work together. Are you using a semantic layer? if so, which type? #SemanticLayer #DataGovernance #AnalyticsEngineering #DataandAI | 25 comments on LinkedIn

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

A Practical Builders Guide

Explore the latest insights and updates from RyuGraph

AIOTI WG Standardisation Focus Group on Semantic Interoperability has prepared a report on Data to Ontology Mapping. A key challenge people face when using ontologies is […]

A first look at TypeDB 3 benchmarks, showing great performance at scale, real-world OLTP workloads, and comparisons to previous versions, and Neo4j.

You can process graphs in DuckDB! In this post, we show how to use DuckDB and the DuckPGQ community extension to analyze financial data for fraudulent patterns with the SQL/PGQ graph syntax that's part of SQL:2023.

The summer has been quite busy, and we are very thrilled to announce the release of Gephi Lite v1.0! This marks for us the first version of Gephi Lite we are really proud about. You can play with i…

An open-source Text2SQL tool that transforms natural language into SQL using graph-powered schema understanding. Ask your database questions in plain English, QueryWeaver handles the weaving. - Fal...

DuckDB community extension that adds support for SQL/PGQ and graph algorithms

Explore an iterative verification and correction refinement process aimed at improving Text2Cypher performance.

Graph database implementing the RDF and SPARQL standards. Very fast and scales to hundreds of billions of triples on a single commodity machine. - ad-freiburg/qlever

When we present QLever, people often ask "how is this possible" as our speed and scale is on another dimension. We now have a page in the wiki that goes into a bit more detail on why and how this is possible. In short: • Purpose built for large scale graph data, not retrofitted • Indexing optimized for fast queries without full in-memory loading • Designed in C++ for efficiency and low overhead • Integrated full text and spatial search in the same engine • Fast interactive queries even on hundreds of billions of triples Link to the wiki page in the comments.

Qlever: graph database implementing the RDF and SPARQL standards. Very fast and scales to hundreds of billions of triples on a single commodity machine. Sounds to good to be true, anyone tested this out? https://lnkd.in/esXKt79J #GraphDatbase #ontology #RDF | 14 comments on LinkedIn

Graph drafts, LLM verifies. Contribute to vitali87/speculant-graph development by creating an account on GitHub.

Discover how LMPG transforms graph databases by treating properties as first-class citizens rather than simple node attributes. This comprehensive technical guide explores RushDB's groundbreaking architecture that enables automatic schema evolution, property-first queries, and cross-domain analytics impossible in traditional property graphs or RDF systems.

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

Simplify graph emebeddings ↙️↙️↙️ Developing a fast vector indexing datastore engine 🚂 at `arrowspace` led me into defining a fast way for doing graph embeddings. What I came up with is a process that is categorised as inductive graph embeddings, aka infer the embedding of an added node without retraining on the graph. `arrowspace` work similarly to Laplacian Eigenmaps with some relevant tweaks to achieve performance as described in https://lnkd.in/eGgeKbdM This method is a sequence of linear operations, compared to similar algorithms it uses spectral properties instead of random walks so to achieve faster training speed 🚄 How faster will be the object of a future blogpost. Practical comparison summary: * Inductiveness: `arrowspace` (spectral operator on features) and GraphSAGE are inductive; DeepWalk/node2vec are typically transductive * Online cost: `arrowspace`’s operator application is lightweight; GraphSAGE requires model inference; node2vec/DeepWalk usually require rerunning or approximations to add nodes * Quality: Laplacian embeddings benchmark strongly against node2vec and are competitive with deep methods (VGAE) depending on graph properties and metrics, suggesting `arrowspace`’s embeddings will be solid baselines or better for community-structured retrieval tasks * Integration: `arrowspace` emphasizes Rust/native vector indexing with spectral augmentation, complementing external training stacks rather than replacing them. This simplifies this kind of processes compared to Deep Learning and random walks approaches. Please follow for more updates. #graphembeddings #graphs #embeddings #search #algorithm

Songjie Niu, Kaisen Yang, Rui Zhao, Yichao Liu, Zonglin Li, Hongning Wang, Wenguang Chen. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Text2graph is and online service for transforming free text into a knowledge graph form (nodes and relationships). The graph can be also exported using Cypher or Gremlin statements for quick import into your favourite database.

Knowledge graphs have garnered significant research attention and are widely used to enhance downstream applications. However, most current studies mainly focus on static knowledge graphs, whose...

Most knowledge graph practitioners treat ontologies as sophisticated dictionaries — structured vocabularies and entity hierarchies…

Construction of a large painter network with ~3000 painters using the PainterPalette dataset, connecting painters if they lived at the same place for long enough time. - me9hanics/painter-network-e...

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

Discover what's possible with Nodestream: a declarative framework for building, maintaining, and analyzing graph data, compatible with Neo4j & Amazon Neptune.

🚀 𝗰𝗶𝘁𝘆𝟮𝗴𝗿𝗮𝗽𝗵 𝘃𝟬.𝟭.𝟲 𝗶𝘀 𝗻𝗼𝘄 𝗹𝗶𝘃𝗲! 🚀 city2graph is a Python library that converts geospatial datasets into graphs (networks). 🔗 GitHub https://lnkd.in/gmu6bsKR What's New: 🛣️ 𝐌𝐞𝐭𝐚𝐩𝐚𝐭𝐡𝐬 𝐟𝐨𝐫 𝐇𝐞𝐭𝐞𝐫𝐨𝐠𝐞𝐧𝐞𝐨𝐮𝐬 𝐆𝐫𝐚𝐩𝐡𝐬 - Generate node connections by a variety of relations (e.g. amenity → street → street → amenity) 🗺️ 𝐂𝐨𝐧𝐭𝐢𝐠𝐮𝐢𝐭𝐲 𝐆𝐫𝐚𝐩𝐡 - Analyse spatial adjacency and neighborhood relationships with the new contiguity graph support 🔄 𝐎𝐃 𝐌𝐚𝐭𝐫𝐢𝐱 - Work seamlessly with OD matrices for migration and mobility flow analysis You can now install the latest version via pip and conda. For more examples, please see the document: https://city2graph.net/ As always, contributors are most welcome! #UrbanAnalytics #GraphAnalysis #OpenSource #DataScience #GeoSpatial #NetworkScience #UrbanPlanning #Python #SpatialAnalysis | 25 comments on LinkedIn