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
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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
Taxonomies — Formal and Informal
We were working with a client recently who wanted to incorporate their existing taxonomies into their newly forming enterprise ontology. It was, as they say, a “teachable moment.” Not all taxonomies are created equal. At least not with regard to their being able to be integrated into an ontology. Most people start a taxonomy...Continue reading→
How can I perform quality assurance on an ontology?
First, you need to know what the original objectives were for having the ontology. The more specific the objectives, the easier it is to test whether they have been met. If you never had any objectives (or requirements), it might be easier to declare success, but harder to defend what that means. Whatever the specific...Continue reading→
How can I obtain confidence that an ontology is correct?
A good place to start is to run an inference engine to see if there an any logical inconsistencies or classes that cannot possibly have any members. The former is always an error; the latter usually leads to an error. However, this is only the beginning. Plenty of errors can remain even when the ontology...Continue reading→
How can I ensure that an ontology is complete?
This is a question of scope. An ontology is complete if it has all the concepts and axioms that are needed. Needed for what? This depends on the intended purpose of the ontology. If you have a local, one-time need to integrate a small number of databases, then you need to include all the concepts...Continue reading→
How can I ensure that an ontology is elegant?
This is related both to scope and to design. An ontology is elegant if it has the fewest possible concepts to cover the required scope with minimal redundancy and complexity. Whereas completeness is about making sure enough is in the ontology, elegance is about making sure that the ontology is not done until there is...Continue reading→
How can I ensure that an ontology is easy to understand and use?
This is a complex and multi-faceted issue. The answer depends on the audience, who have varying degrees of a) knowledge in the domain, b) technical background, c) awareness of what the ontology is for and d) need to directly work with the ontology. For everyone, and especially non-technical people, it is important for there to...Continue reading→
I’m trying to build an enterprise ontology for my organization. How can I avoid terminology wars?
Great question. This is an ongoing challenge because different parts of a company use terms differently. One rule of thumb is to avoid using terms that have many different meanings across the company – this will just cause confusion. Things can be set up so that local groups can see their own terms in UIs...Continue reading→
How do I track down and debug errors in my ontology?
I will answer this in the context of OWL ontologies where the errors are found using Hermit or Fact++ as the inference engine. I happen to use Protégé, but other ontology tools have similar functionality. There are a few cases. First, if the ontology is inconsistent, then one or more classes will be inferred to...Continue reading→
Are There Any Tools to Help Find Common Pitfalls in Ontologies?
Yes. Check out http://oeg-lia3.dia.fi.upm.es/oops/. The authors looked through the literature and discovered 29 common pitfalls that arise when building ontologies. Code has been written to automatically find 21 of the 29 pitfalls. Try it out and give the authors feedback. The whole community will benefit.
Oh brave new world… How semantic technology can improve your IT productivity while protecting your legacy IT investments
Workers, professionals and managers are all becoming acutely aware of the discrepancy between the performance of consumer based technology and their internal IT department. The gap between the information and services they can get for ‘free’ on the web vs. what they get from IT is creating a high level of frustration and dissatisfaction between IT...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.