Knowledge on STEM to STEAM

Knowledge Management: The Strategic Foundation of Research

Mon, 01 Jan 0001 00:00:00 +0000

Knowledge Management is a key revenue source as it helps ensure you do not repeat what others may know and ensures you are spending the scarce resources you have in the most effective manner.

It does NOT happen automatically. Organizations need to take deliberate action to manage what they know and organize it in a manner that others in the areas of research can find and quickly learn and add their knowledge to this structured approach.

STEAM Knowledge Graph

Mon, 01 Jan 0001 00:00:00 +0000

The knowledge graph below is the living map of this site. Every section is a node; every arrow is a named edge that explains how two areas of knowledge relate. This is the structural backbone behind the STEAM mission: not just five siloed disciplines, but an interconnected network where insights travel across boundaries.

The STEAM Domain Graph#

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title STEAM Knowledge Graph — MadSciGuys

rectangle "**Science**\nBiology · Chemistry\nPhysics · Medicine" as SCI
rectangle "**Technology**\nBiotech · Energy\nManufacturing · IoT" as TECH
rectangle "**Engineering**\nChemical · Civil\nElectrical · Aerospace" as ENG
rectangle "**Arts**\nKnowledge Mgmt\nPharm · Industry 4.0" as ARTS
rectangle "**Mathematics**\nPredictive Modeling\nData Science · ML" as MATH

SCI -right-> TECH : "enables"
TECH -right-> ENG : "implements"
ENG -up-> ARTS : "designed by"
ARTS -left-> MATH : "formalized by"
MATH -down-> SCI : "models"

SCI -[#9B59B6,dashed]-> MATH : "measured by"
ARTS -[#27AE60,dashed]-> TECH : "shapes UX of"
MATH -[#E67E22,dashed]-> TECH : "optimizes"
ENG -[#E74C3C,dashed]-> SCI : "applies"
ARTS -[#2980B9,dashed]-> SCI : "frames questions for"

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Node Detail: What Each Domain Contains#

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package "1 · Science" {
 class Biology {
 Molecular Biology
 Cell Biology
 Biochemistry
 }
 class Chemistry {
 Organic · Inorganic
 Physical · Analytical
 }
 class Physics {
 Quantum Mechanics
 Thermodynamics
 Cosmology
 }
 class Medicine {
 Internal Medicine
 Medical Genetics
 Immunology
 }
}

package "2 · Technology" {
 class BioTech
 class Energy
 class Manufacturing
 class Transportation
}

package "3 · Engineering" {
 class ChemicalEng as "Chemical Eng"
 class ElectricalEng as "Electrical Eng"
 class AerospaceEng as "Aerospace Eng"
 class ComputerEng as "Computer Eng"
}

package "4 · Arts" {
 class KnowledgeMgmt as "Knowledge Mgmt" {
 Ontological Semantics
 Design Study Method
 Taxonomy
 }
 class Pharma {
 Regulations (ICH)
 Scientific Method
 QbD / Control Strategy
 }
 class Industry40 as "Industry 4.0" {
 IoT · Big Data
 Cyber-Physical Systems
 Smart Factory
 }
}

package "5 · Mathematics" {
 class PredictiveModeling as "Predictive Modeling" {
 Data Science Lifecycle
 Feature Engineering
 Non-linear Regression
 }
}

@enduml

Cross-Domain Relationship Map#

The edges below show the named semantic relationships used across this site. These are the types of connections that form the knowledge graph structure.

Knowledge Manager

Mon, 01 Jan 0001 00:00:00 +0000

A knowledge manager creates the systems, taxonomies, and processes that make knowledge discoverable and reusable. This persona is essential for connecting research, design, and implementation across STEAM projects.

In the STEAM knowledge graph, the knowledge manager is the persona most directly responsible for building the graph itself — defining the ontology, curating the fact repository, maintaining the lexicon, and ensuring the onomasticon stays current. Their work makes the graph navigable for every other persona.

Ontological Semantics

Mon, 01 Jan 0001 00:00:00 +0000

Architecture of Ontological Semantics#

The key components required to build a knowledge framework leveraging Ontological Semantics — the theoretical grounding that has enabled the current field of Large Language Models — which help us create knowledge out of existing sources in a systematic manner.

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title Architecture of Ontological Semantics

package "Static Knowledge Sources" {
 database "Ontology\n(World Model)" as ONT
 database "Fact Repository\n(Domain Facts)" as FACT
 database "Lexicon\n(Word Forms & Meanings)" as LEX
 database "Onomasticon\n(Named Entities)" as ONOM
}

component "Knowledge Representation\nLanguage" as KRL

rectangle "Semantic Analyzer\n(Large Language Model)" as LLM

rectangle "Use Cases" as UC {
 component "Text Summarization" as TS
 component "Question & Answering" as QA
 component "AI Agents" as AG
}

ONT --> KRL : "defines concepts"
FACT --> KRL : "instantiates"
LEX --> KRL : "maps words → meaning"
ONOM --> KRL : "resolves named entities"

KRL --> LLM : "structured meaning\nrepresentation"

LLM --> TS
LLM --> QA
LLM --> AG
@enduml

The Four Static Knowledge Sources#

Ontology — the World Model#

The ontology defines the concepts that exist in a domain and the relationships between them. It is the schema of the knowledge graph: what nodes (concepts) and edge types (relationships) are valid. Without an ontology, a knowledge base is just a bag of facts with no shared meaning.

Paper on Glass: Digital Process Data Capture with S88/S95 Ontology Alignment

Mon, 01 Jan 0001 00:00:00 +0000

Abstract#

The “paper on glass” paradigm represents a transformative approach to industrial process documentation, where traditional paper-based records are digitally replicated while maintaining familiar workflows. This paper explores how such systems can be enhanced through alignment with established manufacturing ontologies, specifically ISA-88 (S88) batch control and ISA-95 (S95) enterprise-control integration standards, creating a unified framework for recipe management and process data capture.

Introduction#

Manufacturing industries have long relied on paper-based documentation for recipe management, batch records, and process data collection. The “paper on glass” concept digitizes these familiar interfaces while preserving the cognitive patterns operators have developed over decades. However, true digital transformation requires more than visual replication—it demands semantic alignment with standardized ontologies that enable interoperability, data consistency, and automated processing.