The source code for neo4j-gspatial is finally open-source
[I’m thrilled to announce that the source code for neo4j-gspatial is finally open-source!
the source code for neo4j-gspatial is finally open-source
Not All Reasoning Is the Same
Why OWL Profiles Exist and Why Production Keeps Ignoring Them
Some Context on Context Graphs - The GraphRAG Curator
Most business intelligence is tacit. It lives in people's heads, emails, and chats rather than in transactional databases. In theory, Context Graphs capture an audit trail of these informal interactions so that AI doesn't just follow rigid rules, but understands the intent and exceptions that make a business actually run. In that sense, Andreas Blumauer of Graphwise points out that a context graph is a kind of knowledge graph. “The consensus across the community ,” he says, “ is that a Context Graph is an operationalized Knowledge Graph.”
Why Healthcare Leads in Knowledge Graphs | Towards Data Science
How science, regulation, collaboration, and public funding shaped the world’s most mature semantic infrastructure
Healthcare is the most mature industry in the use of knowledge graphs for a few fundamental reasons. At its core, medicine is grounded in empirical science (biology, chemistry, pharmacology) which makes it possible to establish a shared understanding of the types of things that exist, how they interact, and causality. In other words, healthcare lends itself naturally to ontology.
The industry also benefits from a deep culture of shared controlled vocabularies. Scientists and clinicians are natural librarians. By necessity, they meticulously list and categorize everything they can find, from genes to diseases. This emphasis on classification is reinforced by a commitment to empirical, reproducible observation, where data must be comparable across institutions, studies, and time.
Finally, there are structural forces that have accelerated maturity: strict regulation; strong pre-competitive collaboration; sustained public funding; and open data standards. All of these factors incentivize shared standards and reusable knowledge rather than isolated, proprietary models.
Demystifying SKOS for Practitioners: A Practical Guide to Controlled Vocabularies
Semantics, standards, and structure: how SKOS, taxonomies, and controlled vocabularies power interoperability, governance, and meaning at scale in modern data ecosystems
5 Graph Tech Predictions for 2026 according to the Experts
Learn what graph technology experts and practitioners believe the year ahead has in store for the world of connected data – from AI to hypergraphs and more.
The Trillion-Dollar Rebranding of Context Graphs
Plagiarism, VC Marketing, and the Erosion of Truth There’s a particular kind of intellectual dishonesty that thrives in the technology industry. It doesn’t look like plagiarism in the traditional sense—no copied paragraphs, no lifted sentences.
Own the Ontology or Rent Your Future - The four capability gaps that quietly sink knowledge graphs and make agentic AI ungovernable
Part 1b of The Ontology Imperative: Building Trustworthy Agentic AI
Own the Ontology or Rent Your Future – The four capability gaps that make agentic AI ungovernable
UPSERT vs RDF: Same Goal, Different Rules
Why RDF stores don’t overwrite values on import — and what to do instead
2026 Knowledge Management Priorities and Trends Survey Report
This report presents findings from APQC’s 2026 Knowledge Management Priorities and Trends Survey, with insights from global participants across industries and roles. The research explores how KM teams are adapting to rapid technological change, especially the integration of AI, and identifies the top priorities, opportunities, threats, and skillsets shaping the future of KM.Key highlights include:
How Fastweb + Vodafone reimagined data workflows with Spanner & BigQuery | Google Cloud Blog
Following the acquisition of Vodafone Italy by Swisscom, these European telco leaders saw an opportunity to rethink how they serve customers.
Top 10 Graph Databases for Retrieval-Augmented Generation (Free & Paid)
Graph Databases for RAG + How to choose
The AI Evolution of Graph Search at Netflix
From Structured Queries to Natural Language
Ontological Prisms & The Geometry of Knowledge
Preamble The rapid and pervasive expansion of generative artificial intelligence (GenAI) technologies has highlighted the inherent limitations of purely neural approaches, as well as the benefits o…
We've released `kglab` version 1.x
We've released `kglab` version 1.x, which is long overdue -- this should have been positioned as a full release years ago!
We've released `kglab` version 1.x, which is long overdue
From 'Policy as Code' to 'Policy as Graph'
From 'Policy as Code' to 'Policy as Graph'?
From 'Policy as Code' to 'Policy as Graph'?
Lance graph benchmarks
Check out these lance-graph benchmarks on an artificial social network dataset 👇🏽. The query suite used tests for a Cypher query workload across different dimensions. Q8/9 in this suite are especially challenging for most systems. Systems like Kùzu Inc and LadybugDB (fork of Kuzu) perform especially well because they innovate on factorization and hybrid join algorithms (WCOJ + binary joins).
The #Lance format (upon which lance-graph is built) is designed for fast random access + scans, and this benchmark is just the beginning! Stay tuned for more benchmarks and written content on this. 🚀
Many thanks to our collaborators Chunxu Tang and Beinan Wang for their hard work on this project!
lance-graph repo: https://lnkd.in/gm7iGPit
benchmark repo: https://lnkd.in/gxETZE7R
lance-graph benchmarks
How to become a Taxonomist or Ontologist.
A lot of people have been reaching out to me asking how to become a Taxonomist or Ontologist.
how to become a Taxonomist or Ontologist.
Everyone is suddenly talking about Context Graphs
Everyone is suddenly talking about Context Graphs.
Everyone is suddenly talking about Context Graphs
SPARQL for SQL Developers: A Translation Guide
Getting started with SPARQL for knowledge engineering!
Patterns All the Way Down: A Generalization for Graph-Like Things
Or: how trying to “say something” about paths led to a recursive data structure that subsumes graphs entirely*
Kanon 2 Enricher, the world’s first hierarchical graphitization model
Inside every legal document is a hidden lattice of implied relationships, rules, and formally defined terms and entities.
Kanon 2 Enricher, the world’s first hierarchical graphitization model. With a propr
Rules on Graphs in Graphs of Rules, Part 1
Inference rules are useful. They bring declarative expressivity — stating what follows from facts rather than how to compute it. But for users, their most salient feature is that they make queries simpler and faster.
Taxonomist Role in the New World of Generative AI - Enterprise Knowledge
What are the implications of Generative AI for the role of the Taxonomist? Yumiko's blog pulls together collective experiences to highlight how taxonomists fit into this new paradigm, and how your organization can leverage this synergy to optimize your taxonomy and semantic processes.
graphina: A graph data science library for Rust 🦀 with Python bindings 🐍
Graphina is a graph data science library for Rust. It provides common data structures and graph algorithms for analyzing real-world networks, such as social, transportation, and biological networks. It also helps power Onager – graph analytics on DuckDB – which we featured last week.
Compared to other Rust graph libraries, Graphina aims to provide a more high-level API and a wide range of ready-to-use algorithms for network analysis and graph mining tasks. Graphina aims to be as feature-rich as NetworkX but with the speed and performance benefits of Rust. And if you’re a Pythonista, the PyGraphina Python library allows users to use Graphina in Python.
How Daimler Trucks North America built a living knowledge graph of its business
Combining Neo4j with Claude, MCP, and network monitoring has given the truck giant real-time visibility into how its systems, data, and processes interconnect
Relationships and Knowledge Systems
The Architecture of Meaning
Context Graphs and the Future of AI Autonomy
Fundamentals and context as an use case of data operating systems.
Context Graphs Are a Trillion-Dollar Opportunity. But Who Actually Captures It?
Jaya Gupta’s thesis is right about context graphs, and wrong about who wins. In a world of heterogeneity, the integrator always wins, not the application.
Ontologies, Context Graphs, and Semantic Layers: What AI Actually Needs in 2026
We've been working on semantic representation for decades—knowledge graphs, ontologies, semantic layers. Jessica Talisman untangles what they actually are and what AI needs from them.