6 years after, where are we? Interested by your feedback concerning the evolution of the graph technologies landscape and about what the current landscape is. https://lnkd.in/eEPkExH | 25 comments on LinkedIn

Today, Amazon Web Services (AWS) announced the general availability of Amazon Bedrock Knowledge Bases GraphRAG (GraphRAG), a capability in Amazon Bedrock Knowledge Bases that enhances Retrieval-Augmented Generation (RAG) with graph data in Amazon Neptune Analytics. In this post, we discuss the benefits of GraphRAG and how to get started with it in Amazon Bedrock Knowledge Bases.

pretty stoked that our paper on the vectorised #SPARQL execution engine in Stardog got accepted to the GRADES-NDA workshop at #SIGMOD2025. It's a cool piece of work describing how modern vectorised join algorithms, more widely known in the SQL world, make graph query processing much more efficient. Talking of up to an order of magnitude difference when it comes to analytical queries and large-scale joins. Hugely proud of my brilliant co-authors Simon Grätzer (the lead engineer on the BARQ project) and Lars Heling. It was their idea to do this work, and I couldn't be more proud that it worked out in the end. The preprint is now on arXiv: https://lnkd.in/eqXtVMqe

Discover how #Aerospike Graph overcomes #identityresolution limitations. Download our latest benchmark to: 💡 See how #AerospikeGraph scales efficiently from 200GB to 20TB without performance degradation across multiple real-world identity graph workloads 💡 Learn how to deploy high-performance identity graphs with fewer resources 💡 Use the results to plan your own scale-out graph infrastructure Get the benchmark here: https://lnkd.in/gZimB6Sh #AdTech #MarTech Ishaan Biswas Lyndon Bauto Phil Allsopp Matt Bushell Jim Doty

I was looking forward to speaking at next week's Knowledge Graph Conference, but I had a stroke in early March, so I've had to cut back my activity quite a lot. This short article talks about the overall problem/opportunity, which underpins the work in LDBC (Linked Data Benchmark Council), relating

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

📣 Byte #21: For those of you who want to visualize their graphs inside Jupyter notebooks - we have an exciting development! We recently released an integration with yWorks, who extended their yFiles Jupyter Graphs widget to support Kuzu databases! ✅ Once a Kuzu graph is created, we can instantiate the yFiles Jupyter KuzuGraphWidget, and use the `show_cypher` method to display a subgraph using regular Cypher queries. ✅ There are numerous custom layouts in the yFiles widget (tree, hierarchical, orthogonal, etc.). Give them a try! Here's an example of the tree layout, which is great for visualizing data like this that has rich tree structures. We can see the two-degree mentors of Christian Christiansen, a Nobel prize-winning laureate, in this example. ✅ You can customize the appearance of the nodes in the widget through `add_node_configuration` method. This way, you can display what you're looking for as you iterate through your graph building process. ✅ The Kuzu-yFiles integration is open source and you can begin using it right away for your own interactive visualizations. Give it a try and share around with fellow graph enthusiasts! pip install yfiles-jupyter-graphs-for-kuzu Docs page: https://lnkd.in/g97uSKRe GitHub repo: https://lnkd.in/gjA6ZjiF

In case you missed the Spanner Graph session at Google Cloud Next’25, the recording is now available: • Introduction of the graph space at 00:00 (https://lnkd.in/gsBFuDbt) • Spanner Graph overview at 07:24 (https://lnkd.in/ggxrzFrU) • How Snapchat builds its Identity Graph at 20:32 (https://lnkd.in/gFauYj-9) • Quick demo of an recommendation engine at 26:27 (https://lnkd.in/gvH4AbRF) • Recent launches at 32:00 (https://lnkd.in/gyCPq97t) • Vision: unified Google Cloud Graph solution with BigQuery Graph at 35:09 (https://lnkd.in/gRdbSMeu) I hope you like it! You can get started with Spanner Graph today! https://lnkd.in/gkwbGFbS Pratibha Suryadevara, Spoorthi Ravi, Sailesh Krishnamurthy, Andi Gutmans, Christopher Taylor, Girish Baliga, Tomas Talius, Candice Chen, Yun Zhang, Weidong Yang, Matthieu Besozzi, Giulia Rotondo, Leo Meyerovich, Thomas Cook, Arthur Bigeard #googlecloud #googlecloudnext25 #graphdatabases #spannergraph

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

We’re thrilled to announce new Text2Cypher models and Google’s MCP Toolbox for Databases from the collaboration between Google Cloud and Neo4j.

The Model Context Protocol gives users a wide range of services they can connect to from the comfort of their AI client.

Graph Data Modeling Without Graph Databases: PostgreSQL and Hybrid Approaches for Agentic Systems 🖇️ Organizations implementing AI systems today face a practical challenge: maintaining multiple specialized databases (vector stores, graph databases, relational systems) creates significant operational complexity, increases costs, and introduces synchronization headaches. Companies like Writer (insight from a recent Waseem Alshikh interview with Harrison Chase) have tackled this problem by implementing graph-like structures directly within PostgreSQL, eliminating the need for separate graph databases while maintaining the necessary functionality. This approach dramatically simplifies infrastructure management, reduces the number of systems to monitor, and eliminates error-prone synchronization processes that can cost thousands of dollars in wasted resources. For enterprises focused on delivering business value rather than managing technical complexity, these PostgreSQL-based implementations offer a pragmatic path forward, though with important trade-offs when considering more sophisticated agentic systems. Writer implemented a subject-predicate-object triple structure directly in PostgreSQL tables rather than using dedicated graph databases. This approach maintains the semantic richness of knowledge graphs while leveraging PostgreSQL's maturity and scalability. Writer kept the conceptual structure of triples that underpin knowledge graphs implemented through a relational schema design. Instead of relying on native graph traversals, Writer developed a fusion decoder that reconstructs graph-like relationships at query time. This component serves as the bridge between the storage layer (PostgreSQL with its triple-inspired structure) and the language model, enabling sophisticated information retrieval without requiring a dedicated graph database's traversal capabilities. The approach focuses on query translation and result combination rather than storage structure optimization. Complementing the triple-based approach, PostgreSQL with extensions (PG Vector and PG Vector Scale) can function effectively as a vector database. This challenges the notion that specialized vector databases are necessary, Treating embeddings as derived data leads to a more natural and maintainable architecture. This reframes the database's role from storing independent vector embeddings to managing derived data that automatically synchronizes with its source. But a critical distinction between retrieval systems and agentic systems need to be made. While PostgreSQL-based approaches excel at knowledge retrieval tasks where the focus is on precision and relevance, agentic systems operate in dynamic environments where context evolves over time, previous actions influence future decisions, and contradictions need to be resolved. This distinction drives different architectural requirements and suggests potential complementary roles for different database approaches. | 15 comments on LinkedIn

Learn how to use Spanner Graph with LangChain workflows to build advanced, relationship-aware RAG systems, a.k.a. GraphRAG.

Learn about different types of graphs and their applications in data management and AI, as well as common misconceptions, in this article by Lulit Tesfaye.

Read about how our partners from Semantic Partners explore the latest iteration of the GraphDB's Talk To Your Graph feature

When implementing graph databases in modern software development, we often face a significant challenge: bridging the conceptual gap between ontology-focused knowledge representation and…

Build a graph for RAG application for a price of a chocolate bar! What is GraphRAG for you? What is GraphRAG? What does GraphRAG mean from your perspective? What if you could have a standard RAG and a GraphRAG as a combi-package, with just a query switch? The fact is, there is no concrete, universal

🚀 Introducing 𝗖𝘆𝗩𝗲𝗿: Schema-Aware Cypher Query Validation for Neo4j! We’re excited to share 𝗖𝘆𝗩𝗲𝗿, the Python library we developed to validate… | 12 comments on LinkedIn

Biel/Bienne, Switzerland – February 24, 2025 – Knowledge graphs are becoming critical infrastructure for enterprises handling large-scale, interconnected data. Yet, many existing solutions struggle with scalability, performance, and cost—forcing organizations into proprietary ecosystems with high op

Can work on ontologies with Neo4j, including those based on OWL (Web Ontology Language). However, Neo4j alone is not an ontology reasoner.

Bridge the gap and unlock hidden potential within your unstructured data

Dublin, Jan. 31, 2025 (GLOBE NEWSWIRE) -- The

New features include community summaries, local and global retrievers, running retrievers in parallel, and custom prompt instructions.

When (not) to use GraphRAG

Imagine you’re solving a complex puzzle with scattered pieces of information.

Speaker: Gábor Szárnyas (LDBC)Event: Data Analytics developer room at FOSDEM 2025Talk page: https://fosdem.org/2025/schedule/track/analytics/Slides: https://...

Spanner Graph, now GA, includes a notebook experience, query feature and performance improvements and integrations with first- and third-party tools.

We're very happy to announce our latest release of Kùzu, version 0.8.0, is now available and ready to use! This release brings an exciting new feature that…