Seattle Method

A subject matter expert (SME) and a software application agree on a common representation of knowledge, a conceptualization , using global open standard technical formats such as XBRL, RDF+OWL+SHACL, or GQL. That means that rather than embedding subject matter logic within the code of a software application; that logic can be separated from the software application.  This makes both that common representation of knowledge more useful and modifications to the software easier. This distinction (i.e. separating knowledge and code) makes knowledge based systems very different from typical software applications. The architecture of a knowledge based system explicitly separates knowledge from software code. This is done by representing knowledge declaratively rather than as part of procedural code. This separation of knowledge and code and representing that knowledge declaratively also makes that knowledge more broadly usable. This types of systems have been referred to as "expert syst...

Conceptualization is the process of identifying, abstracting, and organizing a coherent set of concepts and relationships that represent the essential important elements, relationships, and possible states of affairs within a given subject domain (a.k.a. area of knowledge , field, universe of discourse). It transforms the complexity of reality into structured ideas that can be shared, discussed, analyzed, and modeled. A conceptualization is intended to be understandable by humans (the community of subject domain stakeholders) but can also be represented in machine interpretable form. Conceptualizations are social artifacts, sociotechnical tools. A conceptualization serves as a shared understanding among domain stakeholders, enabling them to articulate and reason about the subject domain in consistent, meaningful, and useful ways. It captures not only what exists, but also what could possibly exist by defining the admissible configurations, constraints, mechanics, and dynamics of that...

A model is an informative, intentional, well-defined, clear, complete, and correct representation of the set of things and relations between those things defined within an axiomatic system . A model enables a community of stakeholders trying to achieve a specific goal or objective or a range of goals/objectives to agree on important details related to capturing meaning or representing a shared understanding of and knowledge in some system of interest. In her book An Introduction to Ontology Engineering (PDF page 23), C. Maria Keet, PhD, provides discussion about what constitutes a good and perhaps a not-so-good ontology.  There are three categories of errors she discusses and these three ideas are applicable to models: Syntax errors : If the expression of the model is of some specific machine-readable language, having a syntax error is similar to computer code not being able to compile. Logic errors : If the expression of the model has an error in logic then the model does not r...

One of the original authors of the Scientific American article, The Semantic Web , Dr. Ora Lassila gave what amounts to an "after action report" regarding the vision of the Semantic Web and where we are in terms of realizing that vision. I find the video of that after action report quite helpful in understanding where the Semantic Web and artificial intelligence are going: Semantic Web and AI: Can we finally realize the original vision? That video is excellent including the question and answer session at the end. The vision has matured into the " enterprise knowledge graph ". Additional Information : The Semantic Web is Dead - Long Live the Semantic Web! Semantic Web Layer Cake Tweak Explained Problem Solving Systems The Case for Enterprise Ontology Cutting Through the Noise: An Introduction to RDF & LPG Graphs

Logical reasoning is about arriving at a conclusion in a rigorous way.  There two broad categories of logical reasoning: deductive reasoning   and non-deductive reasoning . Deductive reasoning provides a result that is guaranteed to be certain, therefore the result can be relied upon without doubt and humans need not be involved in a process because of the certainty of deductive reasoning.   Non-deductive reasoning, on the other hand, is not certain, meaning it could be correct but it could also be incorrect.  Non-deductive reasoning is based on probability.  Non-deductive reasoning is always fallible; there is always the possibility of error. And so non-deductive reasoning approaches must have a human in the loop to deal with that uncertainty.  There are three types of non-deductive reasoning: inductive reasoning , abductive reasoning , and analogy . A line of reasoning (a.k.a. chain of reasoning) is an explanation of an approach to solving a proble...

The comic strip below helps one understand the need for a shared set of goals and objectives for a system and the consequences for not agreeing on such shared goals and objectives.  Effective communication between stakeholder and by stakeholders to those creating the system is critically important.  Testing is a tools for verifying agreement and determining if a system is adequate to meet specified goals and objectives. For contrast, consider this project where everyone agrees with the project. If agreement is achieved it is relatively easier to move a project from one state of agreement to some other state of agreement. Additional Information: Overview