holonic v0.2.1 is live!
holonic v0.2.1 is live!
From filings to decisions: why LPG and Applied Knowledge Graphs fit financial document intelligence
Table of contents
stack ranking of REVENUE in the DBMS market
April means many things to many people. For some, it’s the first signs of spring after a long winter. For others (today in particular for my US-based readers) its tax time. On this particular April Wednesday (Wednesday is, after all, spaghetti day, as Tony Baer likes to remind me) it’s spaghetti time — at least on Gartner’s data management team!
I'm pleased to be able to share the 2026 DBMS Spaghetti chart (covering data from 2025).
Some things to keep in mind:
- This is only a small part of our market analysis. The underlying data that this is based on will publish shortly, including in-depth analysyis of the market, and of course, forecasts.
- This is a stack ranking of REVENUE in the DBMS market. Pure-play open source products are not included outside of commercialization vendors. If you are looking for a popularity contest, there are many other sources for that.
- This year's "Churn Index" sits at 42, down slightly from last year's 49. As a reminder, the Churn Index is is calculated as a percentage of vendors that either gained or lost market position in the stack ranking. We further provide a positive and negative churn index for those who are interested.
- The market remains settled at the top. There has been no churn in the top 9 vendors for the last 3 years. With the exception of Tencent surpassing Huawei, there has been no churn for the top 17 vendors for the past 2 years. I have my own predictions on what might happen next, but you’ll have to talk to me on inquiry for the details.
- Datastax has been acquired by IBM, and their revenue will henceforth be counted under IBM’s.
- ServiceNow is now included as a named vendor in 2025.
- Rocket Software has acquired Vertica, but that will not be reflected until next year’s analysis. That said, we no longer refer to “OpenText (Vertica)” in this year’s report, instead going with the more accurate “OpenText”.
- As always, pure-play cloud vendors are in light blue.
if you are a Gartner client and would like to discuss the DBMS market dynamics in more detail, please feel free to set up an inquiry. And keep an eye out for the detailed analysis which will publish in coming weeks.
Special thanks to Robin Schumacher, Ph.D., Harshita Chibber and the quant team at Gartner for sourcing the numbers.
stack ranking of REVENUE in the DBMS market
More ontology beginner mistakes
Fantastic list of Beginning Mistakes. Others I've found over the years:
* #Namespaces. Getting hung up too early on IRIs and namespaces - until you have an understand of the problem domain ex:_ works JUST fine.
* #Inheritance. Focusing on hierarchies and Inheritance before knowing the concrete classes you're working with. Inheritance is largely an optimisation step that should be done later.
* #Not_Building_Examples. Start with examples; they often clarify what you're actually modelling far better than Protege will do. Protege is a great tool, but keep it in the toolbox until you actually need it.
* #Upper_Ontologies. You don't NEED them. An upper ontology is a lot like a code framework; it's great for building interoperating systems and establishing interface abstractions, but until you know the general structure of your data, it is a distraction.
* #Things_Change. An ontology that does not account for change is an inventory, nothing more. Spend time understanding events, especially.
* #Turtle_Is_Just_An_Odd_Form_Of_JSON. Nope. Turtle is a way of describing a graph. Full stop. You can use JSON_LD as another way of describing that graph, but it's still RDF, and it follows the rules of RDF. If you are going to work with ontologies, you should learn Turtle, because it works on graph assumptions that exceed JSON.
* #Taxonomies_Matter. Taxonomies declare concepts, though they do not define them - that's what the ontological schema does, whether through OWL or through SHACL.
* #Established_Ontologies_Are_Useful_Not_Mandatory. There are many ontologies out there. Some are highly useful, some have limited utility, some are crap. Solve for your needs first, and if they happen to align with existing ontologies, great, but don't assume they HAVE to.
There are other lessons learned. I'd love to hear from others in that respect.
Reactome-ontology, a project aimed at representing Reactome knowledge in a more structured, reusable, and ontology-oriented way
reactome-ontology, a project aimed at representing Reactome knowledge in a more structured, reusable, and ontology-oriented way
Why a Knowledge Graph Reduces System Load for Join‑Heavy Queries
Why a Knowledge Graph Reduces System Load for Join‑Heavy Queries
In a graph (or RDF triple store), every node stores direct pointers to its neighbors. Following a relationship is a pointer lookup — not a search, not a join, and not an index scan.
How Relational Databases Work
To answer a question that spans multiple tables (for example, `employees` → `departments`):
1. The database scans or indexes the `employees` table.
2. For each matching row, it looks up the corresponding entry in `departments`.
3. It materializes intermediate join results—often spilling to disk when memory runs out.
Every `JOIN` adds a new multiplication of complexity.
With five joins, even a well‑indexed system may evaluate **billions of combinations**, hitting CPU, I/O, and RAM hard.
How Knowledge Graphs Work
In a graph, relationships are stored as edges that directly link nodes.
To find “which department does employee E work in?” the database:
1. Follows a pointer from the employee node to its `works_in` edge.
2. Follows that edge to the department node.
What This Means for Your Architecture
- Smaller hardware footprint – Run multi‑hop analytics on less hardware.
- Lower memory pressure – No temporary join tables clogging cache.
- Predictable performance – Execution time tied to path length, not data volume.
- More concurrent queries – Lightweight traversals free up CPU for other workloads.
The Bottom Line
A knowledge graph replaces costly, exponential‑time joins with lightweight, pointer‑level traversals.
For any problem involving connected data — customers to products, components to failures, molecules to diseases — graph architecture cuts system load by orders of magnitude and keeps performance stable as your data grows.
**That’s why knowledge graphs are the natural home for relationship‑rich intelligence.**
👉 **Follow me for Knowledge Management and Neuro‑Symbolic AI daily nuggets.
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TEGR: Typed-Edge Graph Retrieval for Multi-Hop RAG
A chunk-level approach to GraphRAG with a contradiction-detection trick nobody else seems to be doing.
The unified graph solution with Spanner Graph and BigQuery Graph | Google Cloud Blog
A unified operational and analytical solution with Spanner Graph and BigQuery Graph that minimizes data movements without compromising performance.
Semantic and Knowledge Graph Roles
A Comprehensive Global Job Market Survey, 2024—2026
Kg Survey 95 Listings
Build Agents That Never Forget
A first-principles walk through agent memory (open-source).
I built an Agent Memory System for myself and got 90.8% (end-to-end) on LongMemEval
I tried Mem0, Zep, and a few others for agent memory. None of them solved the right problem. : LocalLLaMA
Subreddit to discuss locally hostable AI.
Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory | Cool Papers - Immersive Paper Discovery
Structured memory representations such as knowledge graphs are central to autonomous agents and other long-lived systems. However, most existing approaches model time as discrete metadata, either sorting by recency (burying old-yet-permanent knowledge), simply overwriting outdated facts, or requiring an expensive LLM call at every ingestion step, leaving them unable to distinguish persistent facts from evolving ones. To address this, we introduce RoMem, a drop-in temporal knowledge graph module for structured memory systems, applicable to agentic memory and beyond. A pretrained Semantic Speed Gate maps each relation's text embedding to a volatility score, learning from data that evolving relations (e.g., "president of") should rotate fast while persistent ones (e.g., "born in") should remain stable. Combined with continuous phase rotation, this enables geometric shadowing: obsolete facts are rotated out of phase in complex vector space, so temporally correct facts naturally outrank contradictions without deletion. On temporal knowledge graph completion, RoMem achieves state-of-the-art results on ICEWS05-15 (72.6 MRR). Applied to agentic memory, it delivers 2-3x MRR and answer accuracy on temporal reasoning (MultiTQ), dominates hybrid benchmark (LoCoMo), preserves static memory with zero degradation (DMR-MSC), and generalises zero-shot to unseen financial domains (FinTMMBench).
An ontological approach to foster the convergence, interoperability and operationalization of frameworks for Trustworthy AI
AI systems are consistently evolving in terms of both capability and autonomy with an holistic social impact. In this context of proliferation and fast technological evolution, the scientific...
An ontological approach to foster the convergence, interoperability and operationalization of frameworks for Trustworthy AI
Beyond RAG for Cyber Threat Intelligence: A Systematic Evaluation...
Cyber threat intelligence (CTI) analysts must answer complex questions over large collections of narrative security reports. Retrieval-augmented generation (RAG) systems help language models...
KG4CUT: an ontology to facilitate cutting tool selection and interoperability
Selecting cutting tools for milling is a critical and complex task that directly affects product quality, cost, and operational efficiency. The growing diversity of tools and vendor-specific catalogues makes this process especially challenging, particularly for less experienced operators. In this paper, we present KG4CUT, an application ontology aligned with W3C Semantic Web standards and FAIR principles, designed to standardize and integrate cutting tool information across providers. To demonstrate its practical utility, we populated a knowledge graph using an automated pipeline that extracts structured data from real-world PDF catalogues. This graph serves as both a proof of concept and a functional basis for intelligent tool recommendation and cutting parameter retrieval, based on material properties, operation types, and geometric constraints. Evaluation with domain experts showed improved retrieval efficiency and reduced selection errors. KG4CUT thus supports the digitalization of machining knowledge and enables faster, more accurate process planning in industrial settings.
GraphRAG: Building a Smarter AI System (full walkthrough)
📈 Get started with SerpApi for free 👉 https://serpapi.link/thu-vu💻 Git repo for this tutorial 👉 https://github.com/thu-vu92/graphRAG📝 Original Microsoft...
A Conceptual Data Model (CDM) is a high-level representation of business concepts and their relationships, independent of any technical implementation
This fourth edition of the newsletter is dedicated to conceptual data modeling for AI. A Conceptual Data Model (CDM) is a high-level representation of business concepts and their relationships, independent of any technical implementation.
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LATRACE-AI: Multimodal memory service for text, audio, image, and video with tenant-isolated typed graph retrieval.
Multimodal memory service for text, audio, image, and video with tenant-isolated typed graph retrieval. - ZXXZ1000/LATRACE-AI
Unifying Ontology Construction and Semantic Alignment for...
While enterprises amass vast quantities of data, much of it remains chaotic and effectively dormant, preventing decision-making based on comprehensive information. Existing neuro-symbolic approaches rely on disjoint pipelines and struggle with error propagation. We introduce the large ontology model (LOM), a unified framework that seamlessly integrates ontology construction, semantic alignment, and logical reasoning into a single end-to-end architecture. LOM employs a construct-align-reason (CAR) pipeline, leveraging its unified architecture across all three stages: it first autonomously constructs a domain-specific ontological universe from raw data, then aligns neural generation with this structural reality using a graph-aware encoder and reinforcement learning, and finally executes deterministic reasoning over the constructed topology, node attributes and relation types. We evaluate LOM on a comprehensive benchmark constructed from diverse real-world enterprise datasets. Experimental results demonstrate that LOM-4B achieves 88.8% accuracy in ontology completion and 94% in complex graph reasoning tasks, significantly outperforming state-of-the-art LLMs. These findings validate that autonomous logical construction is essential for achieving deterministic, enterprise-grade intelligence.
Putting it all together: A demonstrator for knowledge engineering
Part 5 of my series on from Data Engineering to Knowledge Engineering
When Predicates Lie: SHACL, Reification, and the Event-First Ontology
Kurt Cagle and Chloe Shannon
ndoli: A personal knowledge graph (second brain) for Claude Code — tracks your professional network, projects, and opportunities using semantic web standards (RDF/OWL/SHACL).
A personal knowledge graph (second brain) for Claude Code — tracks your professional network, projects, and opportunities using semantic web standards (RDF/OWL/SHACL). - SteveHedden/ndoli
Beyond Graphify: Why the Enterprise Needs More Than a Folder-to-Graph Tool - AllegroGraph
There has been a lot of excitement lately around ideas like the “LLM wiki” and tools such as Graphify. The appeal is easy to understand. Instead of forcing an LLM … Continue reading Beyond Graphify: Why the Enterprise Needs More Than a Folder-to-Graph Tool →
Why Machines Need Embeddings: Turning Graph Structure into Features
This is Part 5 in a 5-part series on Graph Algorithms: Congratulations. We’ve done it. We have worked through four different algorithms, using intuitive visuals and easy-to-understand metaphors. But we have finally hit a roadblock. Now we must discuss embeddings,… Read more →
Understand-Anything: Turn any code, or knowledge base (Karpathy LLM wiki), into an interactive knowledge graph you can explore, search, and ask questions about. Graphs that teach graphs that impress. Works with Claude Code, Codex, Cursor, Copilot, Gemini CLI, and more.
Turn any code, or knowledge base (Karpathy LLM wiki), into an interactive knowledge graph you can explore, search, and ask questions about. Graphs that teach > graphs that impress. Works with Cl...
Semantic Web Market Research Report 2025-2030: Semantic AI Convergence Unlocks Real-Time Analytics, Autonomous Systems and Intelligent Automation
The global semantic web market is set to expand from USD 2.71 billion in 2025 to USD 7.73 billion by 2030, at a CAGR of 23.3%. This growth is fueled by...
Supercharging LLM Wiki with Knowledge Graphs: Build a Self-Evolving Research System
Last week, Andrej Karpathy introduced a powerful concept called LLM Wiki — a framework designed to turn your notes, papers, and data into a structured, queryable knowledge base. It’s a big step for...
Rowboat: Open-source AI coworker that turns work into a knowledge graph and acts on it
Everyone Has an Ontology Now. Almost Nobody Has an Ontology. | LinkedIn
Microsoft just announced Fabric IQ. At its core: an "Ontology item.