With a successful 200-year track record in developing extensive and diverse product lines by leveraging chemistry and science, this global innovator was embarking on a new era of discovery to shape a better world. 

Categorization of products and the relationships between those named entities were difficult to describe in traditional taxonomies and “systems of systems”. Over the course of  2 centuries massive amounts of complex information had been arrogated, however it didn’t give a full picture. The patterns of data intersection for decision-making were lost within the siloed systems. For greater predictable business insights based not only on structured data but unstructured documents the need for easier access and interoperability was a primary goal. 

A CoE (Center of Excellence) was formed to accelerate R&D development, analytics and  finding of data to advance innovation with greater speed and reliability. A secondary mission was to socialize this capability to the broader community. Ontology development and tools to harmonize information were a foundational part of this data enrichment strategy, but in-house skills and data-centric modeling expertise were insufficient. It was necessary to develop as a core competency. 

Semantic Arts consultants were engaged to bridge this critical ontology and semantic capabilities gap. Our strategic advisory service offering brought over 25 years of practical implementation learnings and educational workshops to deliver a series of focused topics that met broad expectations of a teaching library. Recorded videos are now available on  the company’s enterprise intranet to traverse the complexities of information silos by enabling knowledge graphs, ontologies, and data-centric thinking. 

Topics include – Introducing Semantic Technologies and Ontologies, Introduction to OWL,  Introduction and Hands-on with Protégé and Property Rules, Understanding Class Relationships,  Expressions, and Property Restrictions, Semantic Triple Visualizations, Ontology vs. Taxonomy,  Topic Extraction and Weak Signals, and UI Visualization with Knowledge Graphs 

In parallel Semantic Arts are engaged with a division focused more on enriching and unifying 19 different data repositories into an ontology. The goal is to model relationships between the concepts, substances, and components for describing the products in a disambiguous manner to eliminate data duplication, increase metadata clarity, and eventually incorporate ML and NLP capabilities. We’re collaborating with the client to structure a roadmap for implementation. A projected iterative, agile approach by using our  predictable (rinse and repeat), Think Big / Start Small methodology will be employed to  guide and instruct in this digital evolution.

This effort to interconnect information assets for discovery complements and aligns with the broader digital transformational for bringing “miracles of science” to realization.

On a previous project we worked with Sallie Mae to build an enterprise ontology for their loan business. After the ontology was complete, they decided to outsource a new line of loans to a third-party SaaS vendor. Shortly after making that decision, they realized that the new system would have completely different screens, and completely different messages and APIs from their existing systems. 

Their existing loan servicing systems had, collectively, about 50,000 attributes. The enterprise ontology we had previously designed had 1,500 concepts. They decided to use their ontology as a unifying principle to conform the old and new messages such that their customer-facing systems would not look schizophrenic. They had mature service-oriented  architecture but had not done much to unify or rationalize their messages. 

We helped them select the DXSI toolkit from Progress Software. We created a set of programs that converted the ontology into a form that DXSI could consume. (There were  many issues around translating multiple inheritance to single and converting many fully expressed notions from the ontology into flatter representations.) 

Much of our work for the remainder of this project involved discovering at a very specific level of detail: exactly what each of the fields in each of the new system’s messages actually meant. In many cases this required extensions to the original ontology, but for the most part the extensions were consistent with the original design. In the end we extended the enterprise ontology by only about 10%. 

The new system was implemented on time with a set of conformed messages that allowed a single presentation to the customer.

We were retained by the leading provider of Student Loans to build an enterprise ontology. 

We conducted over a dozen workshops and facilitated brainstorming sessions and many  dozen more one-on-one interviews, and reviewed reams of documentation. In the end we built an Ontology that represented the complexity of their business in just over 1,000 concepts, including classes and properties. This is a dramatic reduction in complexity from the data models of the systems being used to run their business which have far in excess of 50,000 tables and attributes. 

The value of this reduction in complexity is a great strategic asset. Going forward, it means that new systems built to conform to the shared model will automatically be in conformance with each other. Integrating existing systems to each other can be done through the lens of the shared ontology, which, besides being much simpler, has the benefit of not being tied to legacy concepts. This truly is building a data bridge to the future. 

One of the open questions with something as broad as an enterprise ontology is: does it really cover the breadth of the organization and does it have sufficiently granular data to represent all the details that are involved with the many applications that it represents?  Our original test case was to be a document management system that was being implemented in parallel with our Ontology. The idea was that if the tags that were going to  be implemented in document management were aligned with the concepts in the ontology  that primarily described data in the structured systems, it would then be possible to  achieve one of the holy grails in this business: the integration of structured and  unstructured data. 

Unfortunately, the document management project was cancelled before we could test the  theory, but as we describe in another use case, another project came along and provided a  different use case: use the enterprise ontology as the basis for alignment of SOA messages  between legacy systems and a newly outsourced service. 

As we describe in the SOA case study, we were able to use the enterprise ontology to drive down to field-level detail for the SOA messages. It required about a 20% increase in the core ontology (mostly in creating a bit more detail for specific financial transaction codes and the like) and we added two other lower-level ontologies, one specifically for mapping to the legacy systems, and one to help describe concepts that only occur in the SOA layer  (message headers and the like).

Semantic Arts worked with PwC to improve Verizon’s data privacy capabilities. 

Prior to the engagement, as applications evolved many of the process steps required to stay in compliance with privacy regulations and policies were performed manually using  multiple data sources. Our goal was to provide a knowledge graph as a single source of privacy metadata (information about data classified as private). 

Early in the engagement, we identified the key components of the data privacy landscape,  summarized in this diagram: 

The data set loaded into the knowledge graph identifies which applications and third parties are involved in each kind of privacy data processing: data collection, analysis,  modification, transfer, storage, etc. 

We modeled most of the privacy concerns implied by the diagram, including: legal rights of  a data subject, agreements with third parties, details of data processing relevant to privacy  concerns, data lineage, data retention, data catalogs, impact assessments, privacy-related  processes and tasks 

We also created an extensive taxonomy that allowed key distinctions to be made while the core model remained simple and straight-forward. Finally, we created generic configurable queries to perform a range of data validations. 

The result is a consolidated view of data from multiple sources that allows greatly improved management of application compliance with privacy data policies and regulations.

International Monetary Fund

The IMF works to achieve sustainable growth for its approximately 200 member countries.  It carries out missions and loans funds to execute projects for the member countries. The countries’ financial situation is measured and tracked using a wide variety of economic indicators. 

The challenge: Information is stored in a wide variety of vocabulary managers, applications and databases. This makes it difficult to quickly get answers to questions required to carry out day-to-day work. For example: Who is likely to be an expert on customs; Find documents associated with countries similar to Afghanistan; What types of missions for what countries are addressing climate change? 

In each case, getting the answer requires retrieving and processing information from multiple sources. To represent the information required to answer the questions, we built an ontology that covers the core business of the IMF. The main things are: 

We created a knowledge graph composed of the ontology and RDF triples data that was created by converting taxonomies and datasets from a variety of data sources. We wrote  SPARQL queries that traverse the knowledge graph to answer the questions of interest.  This is to be the basis for an internal knowledge portal for integrating structured data (such  as GDP per country) with unstructured data (such as country-specific reports on commodity prices).

Investment Bank Risk and Controls

We worked with a large investment bank who are embarking on a series of projects to further automate their back office. One of their first tasks was to understand in greater detail what all the 5,000 people in the back office were doing. They built an “Economic Architecture” that was essentially the equivalent of a continually running Activity Based Costing project. They asked managers to estimate the percentage of time each of their reports spent on a standard list of activities. However, the activity list was not stabilizing, and many managers had difficulty deciding which of the many activities they should use. As this was slated to eventually become part of the reporting and perhaps eventually the charge back to the front office for the activities performed to settle some of these very complex instruments.

We were called in to create a rational basis for the activity taxonomy. We ended up decomposing the working list of 800 or so activities into a set of orthogonal facets. What was fascinating was that the facets were far, far simpler than the long complex list of activities. Once someone knew the facets (such as financial product, market, as well as a simple set of verbs and modifiers) they would know what all the activities were, as they were just concatenations of the facets. More interestingly we discovered as we performed this that the facets provided a level of categorization that it would be possible to instrument in the workflow and source systems. (The list of 800 activities were too arbitrary to allow for automation, but he facets were closely aligned with primitive concepts found in most systems).

We completed the redefinition and got agreement on the new activities. The new activities are in production, and they are looking at applying this concept beyond the back office operations.

Email: [email protected]
Phone: (970) 490-2224

Investment Bank: Economic Architecture

We worked with a large investment bank who are embarking on a series of projects to further automate their back office. One of their first tasks was to understand in greater detail what all the 5,000 people in the back office were doing. They built an “Economic  Architecture” that was essentially the equivalent of a continually running Activity Based  Costing project.  

They asked managers to estimate the percentage of time each of their reports spent on a standard list of activities. However the activity list was not stabilizing, and many managers  had difficulty deciding which of the many activities they should use. As this was slated to  eventually become part of the reporting and perhaps eventually the charge back to the  front office for the activities performed to settle some of these very complex instruments. 

We were called in to create a rational basis for the activity taxonomy. We ended up  decomposing the working list of 800 or so activities into a set of orthogonal facets. What was fascinating was that the facets were far simpler than the long complex list of activities.  Once someone knew the facets (such as financial product, market, as well as a simple set of verbs and modifiers), they would know what all the activities were, as they were just concatenations of the facets.  

More interestingly we discovered as we performed this that the facets provided a level of categorization that it would be possible to instrument in the workflow and source systems.  The list of 800 activities was too arbitrary to allow for automation, but the facets were closely aligned with primitive concepts found in most systems. 

We completed the redefinition and got agreement on the new activities. The new activities are in production, and they are looking at applying this concept beyond the back-office operations.

gist: Buckets, Buckets Everywhere:  Who Knows What to Think

We humans are categorizing machines, which is to say, we like to create metaphorical buckets and put things inside. But there are different kinds of buckets, and different ways to model them in  OWL and gist. The most common bucket represents a kind of thing, such as Person or Building.  Things that go into those buckets are individuals of those kinds, e.g. Albert Einstein, or the particular office building you work in. We represent this kind of bucket as an owl:Class and we use rdf:type to put something into the bucket. 

Another kind of bucket is when you have a group of things, like a jury or a deck of cards that are functionally connected in some way. Those related things go into the bucket (12 members of a jury, or 52 cards). We have a special class in gist called Collection, for this kind of bucket. A specific bucket of this sort will be an instance of a subclass of gist:Collection. E.g. OJs_Jury is an instance of the class Jury, a subclass of gist: Collection. We use gist:memberOf to put things into the bucket.  Convince yourself that these buckets do not represent a kind of thing. A jury is a kind of thing, a particular jury is not. We would use rdf:type to connect OJ’s jury to the owl: ClassJury, and use gist:memberOf to connect the specific jurors to OJ’s jury.

A third kind of bucket is a tag which represents a topic and is used to categorize individual items for the purpose of indexing a body of content. For example, the tag “Winter” might be used to index photographs, books and/or YouTube videos. Any content item that depicts or relates to winter in some way should be categorized using this tag. In gist, we represent this in a way that is  structurally the same as how we represent buckets that are collections of functionally connected  items. The differences are 1) the bucket is an instance of a subclass of gist:Category, rather than of gist: Collection and 2) we put things into the bucket using gist:categorizedBy rather than gist:memberOf. The Winter tag is essentially a bucket containing all the things that have been indexed or categorized using that tag.

Below is a summary table showing these different kinds of buckets, and how we represent them in  OWL and gist.

Kind of Bucket	Example	Representing the Bucket	Putting something in the Bucket
Individual of a Kind	John Doe is a Person	Instance of owl:Class	rdf:type
A bucket with  functionally connected  things inside	Sheila Woods is a  member of OJ’s Jury	Instance of a subclass of  gist:Collection	gist:memberOf
An index term for  categorizing content	The book “Winter of  our Discontent” has  Winter as one of its  tags	Instance of a subclass of  gist:Category	gist:categorizedBy

Semantic Arts Enters Its’ 25th Year

According to the U.S. Bureau of Labor Statistics, 15,336 companies were founded in Colorado in the year 2000. By 2024, only 2,101 of those companies remained. While we can speculate endlessly about why just ~14% survived recessions, pandemics, and international conflicts, the impression is clear.

The organizations that endured… and ideally thrived deserve recognition for their adaptability and resourcefulness. Seeing how we are entering our 25th anniversary, this statistic is a big deal. Most companies do not make it that long.

70% of companies fail in their first 10 years.

Even the venerable S&P 500 companies have an average lifespan of 21 years.

Resilience is in Our DNA

So here we are at 25, just getting warmed up.

To make things even more interesting, it is cool to be 25 years in an industry that many people think is only a few years old. Many people have only recently stumbled into semantics based on open standards, knowledge graphs, and data-centric thinking, and are surprised to find a company that has been specializing in this since before Facebook was founded.

It hasn’t always been easy or a smooth ride, but we like to think longevity is in our DNA.

Keep reading for a look at three of the most important lessons we’ve learned, a brief tour of our biggest achievements over the past 25 years, a glimpse of where we’re windsurfing to next, and as a bonus for reading through the entirety of our history, we’ll give you an inside scoop on Dave McComb’s origin story leading up to the founding of Semantic Arts.

3 Lessons Learned Surviving 25 Years

You learn a few things after surviving and eventually thriving for 25 years.

After you learn them and then state them, they often sound obvious and trivial. The reality is that we had to learn them to get to where we are today. We hope it serves you as much as it has served us.

LESSON 1:

Becoming data-centric is more of a program than a project. It is more of a journey and process than a destination or product.

We’ve observed a consistent pattern among our clients: once they discover the data-centric approach, they want it immediately. But meaningful transformation requires rethinking deeply held beliefs and shedding long-standing stigmas. This paradigm shift challenges cultural norms, restructures how information assets are organized, and redefines how knowledge is shared (in more meaningful and context-rich ways).

We’ve also seen what happens when organizations resist the data-centric shift. Despite initial interest, they cling to legacy mindsets, siloed systems, and entrenched hierarchies. The transformation stalls because cultural resistance outweighs technical readiness. Information remains fragmented, knowledge-sharing stays shallow, and AI initiatives struggle to produce meaningful results, often reinforcing the very inefficiencies the organization hoped to overcome.

LESSON 2:

Successful data-centric transformations require you to simultaneously look at the big picture and the fine-grain details.

Through decades of execution (and refinement of that execution), we employ a “think big” (Enterprise) / “start small” (Business Domain) approach to implementing data-centric

architecture. We advocate doing both the high-level and low-level models in tandem to ensure extendable and sustained success.

If you only start small (which every agile project advises), you end up recreating the very point solutions and silos you’re working to integrate. And only thinking big tends to build enterprise data models that do not get implemented (we know, because that’s where we started).

Doing both simultaneously affords two things that clients appreciate.

1. It demonstrates a solution to a choice problem set, by leveraging real production data, and in a way that a skeptic can understand

2. It performs in a way that ensures future proofing while avoiding vendor lock-in. After the first engagement with a client, each new project will fit into the broader data-centric architecture and will be pre-integrated. This work can later be re-used and leveraged to extend the ontological model.

LESSON 3:

To instill confidence, you need to prove value through a series of projects validating the utility of the data-centric paradigm.

Most of our clients re-engage us after the initial engagement to guide in the adoption. Generally, we extend the engagement by bringing our approach to more sub-domains. While in parallel, we help a client think through the implementation details of the architecture by modeling the business via an ontology and contextually connecting information with a semantic knowledge graph.

Part of the magic of our modular approach to extending a knowledge graph is that each newly integrated subdomain expands the limitless applications of clean, well-structured, and verified data. The serendipitous generation of use cases can’t be planned (as they are not always obvious),but it often creates opportunities that delight our clients and exceed their expectations.

Let’s take a text-guided tour of what led us to these conclusions, as well as the events that shaped our history.

A Historical Account of Semantic Arts

If we look at the official registration date with the Colorado Secretary of State, Semantic Arts was formed on August 14, 2000. However, reality is rarely as clear-cut as what’s captured on paper. In fact, we had already been operating loosely as Semantic Arts for several months prior.

Stick around, and we’ll take you through the journey, from August 2000 to the time of this writing, August 2025.

FOUNDING & EARLY EXPLORATION (2000)

• In 2000, the idea of applying semantics to information systems was just beginning to gain traction, with emerging efforts like SHOE, DAML, and OIL.
• Leaning into this promising field, the company was aptly named Semantic Arts and served as a vessel through which contracts flowed through to the consultants, all of whom were subcontractors.
• There was virtually no demand for semantic consulting, largely due to a lack of understanding of what “semantic” even meant, so Semantic Arts focused on delivering traditional IT consulting projects (such as feasibility studies and SOA message modeling), often embedding semantic models behind the scenes to build internal capabilities.

THE 1ST SEMANTIC WAVE NEVER CAME (2001–2002)

• In 2001, the “Semantic Web” was formally introduced by Tim Berners-Lee, Jim Hendler, and  Ora Lassila in Scientific American, and given Berners-Lee’s legacy as the inventor of the  World Wide Web, excitement soared. 
• On surface, it appeared that Semantic Arts was poised to ride what seemed to be the next monster wave, but the wave never came. • Despite the hype, potential clients remained unaware or uninterested in semantics, and adoption stagnated.

BOOKS, CLIENTS, AND THE BIRTH OF gist (2002–2004) 

• From 2002 to 2003, while Dave McComb authored Semantics in Business Systems: The  Savvy Manager’s Guide, while Semantic Arts primarily sustained itself through contracts with the State of Washington. 
• Behind the scenes, Semantic Arts developed semantic models for departments such as  Labor & Industry and Transportation, and it was during the Department of Transportation project that gist, the open-source upper ontology, was born. 
• A small capital call in 2003 helped keep Semantic Arts viable, with Dave McComb becoming majority owner, and Simon Robe joining as the minority shareholder. 

EVANGELISM WITHOUT DEMAND (2005–2007) 

• From 2005–2012, Semantic Arts produced the Semantic Technology Conference and simultaneously began teaching how to design and build business ontologies.
• Despite the proactive outreach efforts, the market remained indifferent.
• During this time, an ontology for Child Support Enforcement in Colorado was created, but  clients were still largely unreceptive to semantic technologies.

THE FIRST WAVE OF REAL DEMAND (2008–2011) 

• In 2008, interest in semantics began to emerge with Sallie Mae being among the first to seek an ontology for a content management system.  
• Semantic Arts advised the team to build a Student Loan Ontology instead, a decision that proved critical when legacy systems could not support a new loan type, marking the first real demonstration of the serendipitous power of semantics. 
• Other clients soon followed: Lexis Nexis (their next generation Advantage platform),  Sentara (healthcare delivery), and Procter & Gamble (R&D and material safety). 

FROM DESIGN TO IMPLEMENTATION (2012–2016) 

• By 2012, Semantic Arts had matured into a premier ontology design firm; however,  increased efficiency meant projects became smaller, and few enterprises required more than one enterprise ontology. 
• A pivotal change occurred when an intern transformed the internal timecard system into a graph-based model, which became the prototype for Semantic Arts’ first implementation project, partnering with Goldman Sachs to solve a “living will” regulatory challenge. 
• This era saw deeper implementations, including a product catalog for Schneider Electric in partnership with Mphasis, and marked the period when Dave McComb eventually bought out Simon Robe to become the sole owner of Semantic Arts. 

SCALING THE DATA-CENTRIC MOVEMENT (2017–2019) 

• By 2017, implementation projects had overtaken design as Semantic Arts’ core business, and feedback from those projects helped rapidly evolve gist, with clients including Broadridge, Dun & Bradstreet, Capital One, Discourse.ai (now TalkMap), Euromonitor, Standard & Poor’s, and Morgan Stanley. 
• Dave McComb published Software Wasteland, followed by The Data-Centric Revolution,  both of which galvanized interest in reforming enterprise modeling. 
• Up to this point, Semantic Arts was primarily composed of highly experienced senior ontologists and architects, but with the growth of implementation work, they developed repeatable methodologies and began hiring junior ontologists and developers to support delivery at scale. 

INSTITUTIONALIZING THE VISION (2020–2024) 

• Around 2020, Semantic Arts realized that version 1.0 of the model driven system was not going to satisfy the increasing demands, so work began on a more ambitious version 2.0  (code named Spark) to begin development of a low-code, next-generation model-driven system.
• In parallel, implementation work toward data-centric transformations continued at pace  with clients including Morgan Stanley, Standard & Poor’s, Amgen, the Center for Internet  Security, PricewaterhouseCoopers, Electronic Arts, PCCW (Hong Kong Telecom), Payzer,  Juniper Networks, Wolters Kluwer, and the Institute for Defense Analyses. 
• At some point, Semantic Arts decided that the industry needed some companies that could become fully data-centric in a finite amount of time, which led to further self experimentation, and in an unplanned way yielded towards data-centric accounting, and the book promoting it, Real-Time Financial Accounting: The Data-Centric Way, by Dave  McComb and Cheryl Dunn to be published in late 2025. 

THE NEW SELF-GOVERNANCE OPERATING MODEL (2025) 

• In 2025, Semantic Arts entered a new era of self-governance as ownership transferred to the Semantic Arts Trust, secured by a royalty agreement that ensures independence from market acquisition. 
• The firm is now guided by a five-person Governance Committee, responsible for key deliberative functions such as budgeting, staffing levels, and strategic direction, alongside  a new President (Mark Wallace), who leads day-to-day strategic execution. 
• One of the first key initiatives in transitioning to this self-governance model is to improve the discipline and repeatability of the marketing and sales functions, making the pipeline of new work more predictable. 

If you’re interested in learning more about why we transitioned into an employee-governed company, we’ll leave you in suspense just a little while longer. We’re currently writing a companion article to this one, where we’ll share more about the company’s secret sauce, cultural  DNA, and what makes Semantic Arts as unique and bespoke as the work we do for our clients. 

You can find more information on our about us page here:  https://www.semanticarts.com/about-us/

Looking towards the Future 

As we reflect and prose on the last 25 years, we adjust our sails to ride the wind of our lessons into the next 25 years. We have a plan. It is not set in stone, but it is surprising how many things have remained constant over these last few decades, and we anticipate them staying constant into the future. 

Most software companies operate hockey-stick business plans that forecast explosive growth over the next few years. If you’re a software firm, that pace is both possible and desirable. But as a professional services firm, there is a natural limit to how fast we can, and should grow. We’ve seen that natural growth limit in other professional services firms, and we’ve experienced it ourselves.  We think that the limit is around 25% per year. Under that number, culture and quality can still be maintained, even as a firm grows.  

We’ve chosen the slightly more ambitious 26% per year as our target. 26% yearly growth is the number that results in a firm doubling in size every three years. We won’t always hit this exact target, but it is what we are aiming for. Afterall, the vast backlog of legacy applications, combined with the continuing accumulation of new legacy systems, suggests that we will have meaningful,  high-impact work for far longer than 25 years. 

If you’re a history buff, you might appreciate learning a thing or two about Dave McComb’s origin story. His professional background deeply shaped the DNA of Semantic Arts and continues to  influence how it functions today. 

Dave McComb’s Origin Story 

Since we’re reviewing Semantic Arts’ history in 25-year increments, we’ll do the same with Dave,  starting in 1975 and leading up to the founding of Semantic Arts. Like a skyscraper, an organization can only rise as high as its foundation is strong, and thanks to Dave’s remarkable background and expertise, Semantic Arts has been built into a truly exceptional organization. 

BREAKING INTO THE REAL WORLD (1975 – 1979) 

• Dave started his career in software in 1975, teaching the class “The Computer in Business”  at Portland State University while getting his MBA.  
• The same year, he got his first paid consulting gig, for an architectural firm (maybe that’s the source of his fascination with architectural firms); to computerize the results of some  sort of survey they had issued for a whopping $200 fixed price bid.  
• He joined Arthur Andersen (the accounting firm) in their “Administrative Division,” which would become Andersen Consulting and eventually Accenture. 
• Five years of building and implementing systems for inventory management, construction management, and payroll, he was made a manager and shipped off (in a plane) to  Singapore.
• After rescuing a plantation management system project that was going badly, he ended up in Papua New Guinea (no good deed goes unpunished). 

BUILDING AN ERP SYSTEM FROM SCRATCH (1980 – 1989) 

• On the island of Bougainville, Papa New Guinea was home to what was, at the time, the world’s largest copper and gold mine.  
• Their systems were pathetic, and so, Dave launched a project to build an ERP system from the ground up (SAP R/2 did exist at the time but was not available on the ICL mainframes that ran the mine).  
• The plan was fairly audacious: to build out a multi-currency production planning, materials management, purchasing and payables system of some 600 screens and 600 reports with  25 people in two years. 
• The success of that project was mostly due to partially automating the implementation of use cases.  

AI BEFORE IT WAS COOL (1990 – 1994) 

• Around 1990, Dave returned to the U.S. and was tasked with delivering another custom ERP  system, this time for a diatomaceous earth mine of similar size and scope as the previous mine in Papa New Guinea.  
• In this project, there was even more automation leveraged, in this case 98% of the several million lines of code were generated (using artificial intelligence in 1991). 
• Around this time, Dave started the consulting firm First Principles, Inc.  
• One of the anchor clients was BSW, the architectural firm that designed all the Walmarts in  North America, and it was on this project, in 1992, that First Principles decided to apply semantics to the design of database systems. 

TURNING A CORNER AT THE END OF THE CENTURY (1995-1999) 

• First Principles, was rolled into Velocity Healthcare Informatics, a dot com era healthcare software company.  • Velocity Healthcare Informatics built and patented the first fully model-driven application environment, where code was not generated, but behavior was expressed based on information in the model.
• Alongside this new model-driven application, the nascent semantic methodology evolved and was grafted onto an Object-Oriented Database.  
• Velocity Healthcare Informatics created a semantic model of healthcare that, in 1999, the medical director of WebMD said, after a multi-hour interrogation of his team, “I wish we had that when we started.”  
• Velocity Healthcare Informatics built several systems in this environment, including Patient  Centered Outcomes, Case Management, Clinical Trial Recruiting and Urology Office  Management.  
• Towards the turn of the century, Velocity Healthcare Informatics was preparing for the road show to go public in March of 2000 when the dot com bubble burst.  
• Velocity Healthcare Informatics imploded in a way that intellectual property could not be salvaged, and as a result, several of the employees jointly formed a new company in the late spring of 2000.

Semantic Arts, Inc. Celebrates its 25th Anniversary

Pioneering Data-Centric Transformations to Modernize IT Architecture, Advance Knowledge Systems, and Enable Foundational AI

Fort Collins, Colorado – August 14, 2025:

According to the U.S. Bureau of Labor Statistics, 15,336 companies were founded in Colorado in the year 2000. By 2024, only 2,101 of those companies remained. While we can speculate endlessly about why just ~14% survived through recessions, pandemics, and international conflicts, the impression is clear. Organizations that endured, and ideally thrived, deserve recognition for their adaptability and resourcefulness.

On August 14, Semantic Arts is celebrating its 25th year of leading the data-centric revolution. In those 25 years, we have undergone a long and treacherous journey from a one-person consultancy to a globally respected firm.

Throughout that time, Semantic Arts has guided organizations to unlock the power of semantic knowledge graphs to structure, manage, and scale their data. With over 100 successfully completed projects, we have refined our “Think Big, Start Small” approach, aligning strategic priorities with high-impact use cases where knowledge graphs create measurable value. And as a result, we have come to specialize in end-to-end, design and implementation of enterprise semantic knowledge graphs.

Company CEO, Dave McComb remarked: “Our 25-year journey has proven that while technologies evolve, the core challenges persist. The vast backlog of legacy applications, and the continuing addition to the legacy backlog suggest that it will be far more than 25 years before we run out of work. Lucky for us, it also means we’re just getting started in bringing meaningful, semantic transformations.

To put things into perspective, we have supported multinational organizations like Amgen, Broadridge, and Morgan Stanley to undergo their semantic transformation, through the adoption of taxonomies, ontologies, and semantic knowledge graphs. We’ve developed and continuously evolved gist, a foundational ontology built for practical enterprise use.

We are proud to faithfully serve organizations undergoing their data-centric transformations, in the same fashion that sherpas support and guide high-altitude climbers in the mountaineering world.

As a matter of fact, we’d like to extend an invitation for you, our dear reader, to sample our guidance in a 30-minute, no-strings-attached consultation. During this session, we’ll share how to avoid common pitfalls and reduce ongoing project risks. We guarantee it will improve your chances of launching successful pilot projects using taxonomies, ontologies, and knowledge graphs.

If you are interested in having a friendly chat, email us at [email protected] with a summary of your goals.

We’ll set up a time that works for you.