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Resources
Articles, books, and talks on data-centric architecture
The best of 25 years of Semantic Arts writing on knowledge graphs, ontologies, AI, and the data-centric approach, chosen by topic. Start with the four pieces below, or go to a topic. Every article is also in the blog.
Start here
Four pieces, in this order, for a first read.
1
What semantics and ontologies are, and how shared meaning helps people and systems understand each other.
2
Data-Centric: How Big Things Get Done (in IT)
How to avoid cost overruns with a predictable, modular, and low-risk approach to digital transformation.
3
Building an Ontology with LLMs
Where large language models help in ontology work, and why Semantic Arts doesn’t use them to design ontologies.
4
Property Graphs: Training Wheels on the Way to Knowledge Graphs
Property graphs are easier to start with and harder to live with. When each kind of graph is the right choice.
The data-centric approach
Put data and its model at the center, and applications conform to the data instead of the other way around.
Article
Data-Centric vs. Application-Centric
The core differences between building systems around applications and building them around data.
Data-Centric vs. Data-Driven →
Column · Data-centric and data-driven aren’t synonyms. The case for data-centric architecture, and what sets it apart.
How to Take Back 40–60% of Your IT Spend by Fixing Your Data →
Article · Much of an IT budget goes to integrations between data silos. A semantic graph foundation cuts that cost.
25
years of data-centric work
90
clients
16
industries
100+
projects
AI and your data
AI works when the data foundation is right.
These pieces show where the data work comes in.
A Knowledge Model for Explainable Military AI →
How do you bring objects that don’t move and objects that do move into the same information frame with a unified context?
Shadow AI vs Shadow AI IT: The Next Enterprise Risk →
Employees’ unsanctioned use of generative AI is a growing risk. How it differs from shadow IT, and how data-centric architecture helps govern it.
Knowledge graphs and ontologies
Ontology design, clear concepts, and the business case behind an enterprise knowledge graph.
Best Practices and Schools of Ontology Design
There’s no single right way to design an ontology. The main schools of ontology design, and where each is best applied.
The Importance of Distinguishing between Terms and Concepts
A term is a label. A concept is the thing the label points at. Confusing the two is one of the most expensive mistakes in enterprise data modeling.
The Case for Enterprise Ontology
Three kinds of value an enterprise ontology brings when all of a company’s data shares one core model.
Six Enterprise Knowledge Graph Anti-Patterns
Six ways enterprise knowledge graph projects go wrong.
Choosing a graph approach
The standards and databases behind enterprise graphs, and how to choose between them.
1
Article · A graph database’s origin shapes its strengths and weaknesses. The origin stories of today’s graph databases, compared.
2
Column · RDF is designed for integration, so data works across the enterprise. The objection, answered.
3
Article · Is OWL necessary, or can SHACL do it all? The answer depends on whether you’re unifying an enterprise’s data.
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