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
GraphNews
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.
What Other Industries Can Learn from Healthcare’s Knowledge Graphs | Towards Data Science
How shared meaning, evidence, and standards create durable semantic infrastructure
The Knowledge Graph Competitive Landscape: What Google, Microsoft, and the Smartest Enterprises Already Know
Google's real AI advantage is not Gemini. It is 15 years of structured knowledge. This article launches The Ontology Imperative, a series on building trustworthy agentic AI.
Part 1a: what Google, Microsoft, and the smartest enterprises already know.
Five helpful learning resources to learn Gephi
🎓Want to learn Gephi? Don’t think twice! I’m sharing five helpful learning resources !
1.-Gephi Quick Start
https://lnkd.in/gD6fPEJK
2.- Visualizing networks (Gephi)
by Dr. Mathieu Jacomy
https://lnkd.in/gwG_EtSb
3.-GEPHI – Introduction to Network Analysis and Visualization
by Dr. Martin Grandjean
https://lnkd.in/geeCDG4P
4.-Documentation for Gephi: core functions and plugins
by Dr. Clément Levallois
https://lnkd.in/gw6RPsrN
5.-Gephi Tutorials: Learning Resources for Everyone
by Dr. Verónica Espinoza
https://lnkd.in/gDq5XX-e
_________
#Gephi #NetworkAnalysis #DataVisualization #NetworkVisualization #GraphTheory #DigitalMethods #ComputationalSocialScience #DataScience #OpenSourceTools #ResearchTools #SocialMedia #VisualAnalytics #ComplexNetworks #AcademicResearch
Want to learn Gephi? Don’t think twice! I’m sharing five helpful learning resources
Enhancing link prediction in biomedical knowledge graphs with BioPathNet - Nature Biomedical Engineering
Understanding how genes, proteins, diseases, and drugs interact is one of the biggest challenges in modern biomedicine. Traditional link‑prediction methods — from similarity metrics to node embeddings — often fall short when relationships span multiple hops or involve noisy, heterogeneous data. This new approach introduces BioPathNet, a powerful graph machine learning framework that pushes the boundaries of what’s possible in knowledge graphs.
Semantic Data Modeling, Graph Query, and SQL, Together at Last?
We're connecting some parallel threads on semantic modeling and graph query with our continued focus on making SQL easier to use.
Semantic modeling is about bringing higher-level business logic definitions into the database (rather than a layer above), so they can be queried directly with SQL. We use measure columns to solve double-counted aggregates. And we model the graph relationships (joins) in the schema, making it easy to express joins with just path traversals.
https://storage.googleapis.com/gweb-research2023-media/pubtools/1030704.pdf
Semantic Data Modeling, Graph Query, and SQL, Together at Last?
LLMs are a dead end. Not because they are useless, but because they put intelligence in the wrong place.
LLMs are a dead end.
LLMs are a dead end. Not because they are useless, but because they put intelligence in the wrong place.
Lettria fine-tunes Gemma 3 for reliable knowledge graph creation
Gemma 3 is at the core of Lettria’s new text-to-graph model that sets a new standard for enterprise-grade reasoning and reliability.
How Do Context Graphs and Knowledge Graphs Differ From Each Other? | LinkedIn
In a Nutshell: Context Graphs are not a replacement for Knowledge Graphs; they are an evolution that builds upon them. A Context Graph is effectively a Knowledge Graph + Time + Decision Lineage.
UCP: the Schema.org for Agentic Commerce | LinkedIn
Google Makes Machine-Readable Metadata Great. Again! But standards and shared ontologies work differently for Google, Microsoft, and the enterprises.
Knowledge Graphs, Context Graphs & Events
The discussion about context graphs, which kicked off a couple of weeks ago in the AI/Semantics community, is now beginning to gain some steam.
Topological and Temporal Aspects of Graphs
Topological and Temporal Aspects of Graphs
The Content of the Context
What "semantics" actually is and what we should focus on
On Context and Knowledge Graphs | LinkedIn
There is a lot of discussion going on about Context Graph and how they extend Knowledge Graphs. I think this discussion misses a very powerful aspect of the RDF stack (a language to represent knowledge graphs): named graphs.
On Ontology, ontologies, Conceptualizations, Modeling Languages, and (Meta)Models.