Semantic Arts exists to shepherd organizations on their Data-Centric journey.
Our core capabilities include:
• Semantic Knowledge Graph Development and Implementation
• Legacy Avoidance, Erosion, and Replacement
We can help your organization to fix the tangled mess of information in your enterprise systems while discovering ways to dissolve data silos and reduce integration debt.
What is Data-Centric?

Data-Centric is about reversing the priority of data and applications.
Right now, applications rule. Applications own “their” data (it’s really your data, but good luck with that). When you have 1,000 applications (which most large firms do) you have 1,000 incompatible data silos. This serves to further the entrenchment of legacy systems, with no real motivation for change.
Data-Centric says data and their models come first. Applications conform to the data, not the other way around. Almost everyone is surprised at the fundamental simplicity, once it’s been articulated.
It sounds simple, but fifty years of “application-centricity” is a hard habit to break. We specialize in helping firms make this transition. We recognize that in addition to new technology and design skills, a major part of most projects is helping shepherd the social change that this involves.
If you’re fed up with application-centricity and the IT-fad-of-the-month club, contact us.
Read More: What is Data-Centric?
What about those legacy systems?
The move to a more data-centric architecture requires thoughtful planning. Early phases look more like a surgical process of dealing with legacy applications in a way that realizes quick wins and begins to reduce costs, helping to fund future phases. Usually, it looks something like this:

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Legacy avoidance: The recognition that a firm has slowed down or stopped launching new application systems projects, and instead relies on the data that is in the shared knowledge graph.
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Legacy erosion: Occurs when firms take use cases that were being performed in a legacy system and instead implement them directly on the graph. Rather than wholesale legacy elimination (which is hard), this approach allows the functionality of the legacy system to be gradually decommissioned.
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Legacy replacement: Once enough of the data, functionality, and especially integration points have been shifted to the graph, legacy systems can be replaced. Not with “legacy modernization” systems, but with lightweight standalone use cases on the graph.
Read more: Incremental Stealth Legacy Modernization
ABOUT US
THOUGHT LEADERSHIP
PROBLEMS WE SOLVE
Taking a different path STARTS NOW. Become Data-Centric to simplify and enhance your enterprise information landscape:
5 Business Reasons for Implementing a Knowledge Graph Solution
1. Comprehensive data integration
2. Contextualized knowledge discovery
3. Agile knowledge sharing and collaboration
4. Intelligent search and recommendation
5. Future-proof data strategy
Integrating semantic capabilities into enterprise business processes has been the foundational shift that organizations such as Google, Amazon, and countless others have leveraged. The results are tangible: increased market share and revenue, lower costs, better customer experiences, reduced risks, and the promotion of innovation.
Semantic Arts’ professional services deliver true solutions (not gimmicks) for current and future information management challenges.
FROM OUR BLOG
Six Enterprise Knowledge Graph Anti-Patterns
The truth is that despite the outsider’s perception that the world of technology just keeps getting better, faster and less expensive, over 70% of enterprise digital transformations fail to achieve their objectives (McKinsey, 2019). Most commentary on the subject points the finger at executive priorities, strategic goal-setting and organizational management. But the seldom discussed root cause of...Continue reading→
Semantic Messages
Interfaces and Interactions Too Much Specificity And Not Enough Play I recently saw this tweet and it reminded me about something I’ve wanted to think and talk about. Satnam continues configuration management has not had the attention enjoyed by academic research for languages and networking, as well as language and networking innovations in industry. I don’t think...Continue reading→
The Data-Centric Revolution: OWL as a Discipline
Many developers pooh-pooh OWL (the dyslexic acronym for the Web Ontology Language). Many decry it as “too hard,” which seems bizarre, given that most developers I know pride themselves on their cleverness (and, as anyone who takes the time to learn OWL knows, it isn’t very hard at all). It does require you to think...Continue reading→
The Greatest Sin of Tabular Data
We recently came across this great article titled “The greatest sin of tabular data”. It is an excellent summary of the kind of work we do for our clients and how they benefit. You can read it at The greatest sin of tabular data · A blog @ nonodename.com The journey of capturing the meaning...Continue reading→
Software Development process expressed in a Knowledge Graph
More times than not we receive pushback on the use of RDF (the standard model for data exchange on the web) being difficult. However, the simplicity of triplestores (Subject – Predicate – Object) and logically composing queries with the written language make this form ideal for technical business users, capability owners, and data scientists alike....Continue reading→
Resisting the Temptation of Fused Edges
Fused Edges If you are doing domain modeling and using a graph database you might be tempted to use fused edges. You see them around the semantic web. But you should resist the temptation. What In a graph database a fused edge occurs when a domain modeler uses a single edge where a node and...Continue reading→
The Data-Centric Revolution: Headless BI and the Metrics Layer
Read more from Dave McComb in his recent article on The Data Administration Newsletter. “The data-centric approach to metrics puts the definition of the metrics in the shared data. Not in the BI tool, not in code in an API. It’s in the data, right along with the measurement itself.” Link: The Data-Centric Revolution: Headless...Continue reading→
How to SPARQL with tarql
To load existing data into a knowledge graph without writing code, try using the tarql program. Tarql takes comma-separated values (csv) as input, so if you have a way to put your existing data in csv format, you can then use tarql to convert the data to semantic triples ready to load into a knowledge...Continue reading→
Incremental Stealth Legacy Modernization
I’m reading the book Kill it with Fire by Marianne Bellotti. It is a delightful book. Plenty of pragmatic advice, both on the architectural side (how to think through whether and when to break up that monolith) and the organizational side (how to get and maintain momentum for what are often long, drawn-out projects). So...Continue reading→
gist: 12.x
gist: is our minimalist upper ontology. It is designed to have the maximum coverage of typical business ontology concepts with the fewest number of primitives and the least amount of ambiguity. Our gist: ontology is free (as in free speech and free beer–it is covered under the Creative Commons 3.0 attribution share-alike license). You can use as you see fit for any purpose, just give us attribution.