Business Knowledge Blueprint

I ran across a term that I really like.  It is a very practical term and the term is necessary these days. That term is business knowledge blueprint. The notion of the business knowledge blueprint came from the same guy that was behind the Business Rules Manifesto which I really like.

The Business Rules Community seems to be the ones that came up with the notion of the business knowledge blueprint. They explain a business knowledge blueprint thus:

"A business knowledge blueprint is without peer as a pragmatic basis for developing a high-quality business vocabulary, as well as a multi-purpose blueprint to your company's business knowledge. This blueprint focuses on business concepts organized in the form of a concept model."

"We define a business knowledge blueprint as follows: business knowledge blueprint: a concept model along with everything that communicates its meaning, including vocabulary, definitions, definitional rules, diagrams, and related information such as examples, descriptions, notes, and references."

It appears that a business knowledge blueprint is intended to be written in natural language guided by some form of  formal control mechanisms such as RuleSpeak or OMG's Semantics of Business Vocabulary and Business Rules (SBVR).  Seems like SBVR enables the information to be put into machine interpretable form.

What problem does the notion of a business knowledge blueprint solve?  Two problems are solved.  First, the term "ontology" is deemed to be "scary".  Second, these days the term "ontology" is overloaded; there are many different meanings.  Personally, I try and stay away from the term ontology, preferring the term "theory" instead. But most business people give me a "deer in the headlights" look when I use the term theory.

It seems to me that there are at least four different "camps" or "tribes" that are doing pretty much the same thing but in different ways. There was also another group who created RuleLog that tried to cross these "silos", bridging the gap so to speak.  These "camps" or "tribes" or "groups" appear to be:

  • Business rules community: This group appears to be practical business people. This group seems to use the RETE algorithm to build rules engines which uses forward chaining. This group seems to be focused on business professionals being able to work with these tools. They also seem to be focused on relational databases.
  • Semantic web community: This is a more technical group focused on W3C standards like RDF,  OWL, SPARQL.  Seems like SPARQL is the rules engine so to speak, not totally sure. This group seems to be focused on standardization.
  • Labeled property graph community: This group built the graph database; I don't think that they typical graph database has a rules engine. As I understand it, graph databases use graph compute engines. However, this group seems to be realizing that they need a rules engine.
  • Logic programming community: This group built PROLOG which is a logic programming language. PROLOG is a query language that I think uses backward chaining. This group got started the earliest, back in 1978 or so.

All  these "camps" or "tribes" or "silos" or groups seem to be converging. Fundamentally, they are all trying to do the same thing. At their essence, these are all problem solving systems. They enable machines to interpret "business knowledge" using the "blueprint".

The Meaning of Meaning which first introduced the notion of the triangle of meaning or the triangle of reference and was explained in Ontology, Metadata, and Semiotics. A really good interpretation of this triangle of meaning exists on page 28 of OMG's Semantics of Business Vocabulary and Business Rules (SBVR). Paraphrasing and using the best ideas from each graphic, syncing to common terminology; I put all those examples together into the context of business knowledge, the triangle of meaning goes something like this:


Meaning is the "notion" or "idea" or "thought" of some thing. The meaning is the human readable description and explanation of the referent or real world "thing" being described.  The real world thing could be an abstract idea or a physical object.

Referent or "thing" is the actual real world object (a.k.a. thing) that is being referred to.

Symbol or expression or "token" is the actual physical machine readable representation that is used to denote the  thing in the real world that is being referred to.

For example, here is the machine readable symbol represented on Wikidata, https://www.wikidata.org/wiki/Q46737, that represents the meaning of the notion, idea, or thought of  "Assets" or "economic resources" which corresponds to the referent or thing which might be a stack of money or your checking account in your bank.

An example of a business knowledge blueprint is the accounting equation. The accounting equation is a small example of a business knowledge blueprint, but a good example and small enough to get your head around.  The accounting equation defines the following ideas or notions: Assets, Liabilities, Equity, Balance Sheet, and that Assets = Liabilities + Equity.

The blueprint forms a model. A model is a specimen that exemplifies the ideal qualities of something. A model tends to be descriptive. A blueprint tends to be a prescriptive, structured plan for how something should be built. A blueprint seems to be more fundamentally directive, exactly how to do something.

Whether you call it a model, a blueprint, an ontology, a theory, or something else; fundamentally what is going on is we are trying to enable humans to be able to communicate and work with other humans, humans to effectively communicate with and work with machines, machines to effectively communicate with and work with humans, and machines to communicate and work with other machines.

The systems that I need to work with have zero tolerance for error.  As such, while probability based systems can provide benefits in some areas, the core of the system needs to MAKE CERTAIN that the triangle of meaning is not only certain but the same for all those stakeholders using the system. And, of course, one needs to understand the authority of those that define something. And governance is necessary to manage epistemic risk.

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