Research on STEM to STEAM

Scientific Method in Digital Age

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

Scientific Method in the Digital Age#

The core principles of the scientific method—asking questions, forming hypotheses, designing experiments, collecting data, and sharing results—remain unchanged. However, in the digital era, how we document, analyze, and communicate our work must evolve to ensure research is transparent, reproducible, and collaborative.

Inspired by The Turing Way, this section explores methodologies and best practices for reproducible research in pharmaceutical science. The Turing Way emphasizes that reproducibility is not just a technical challenge, but a cultural one, requiring open tools, clear documentation, and inclusive collaboration.

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.

From Trenches to Transformation: How Design Study Methodology Can Accelerate Knowledge Evolution in 2025

Sun, 06 Jul 2025 00:00:00 +0000

How the systematic approach to problem-driven research from Sedlmair, Meyer, and Munzner’s 2012 paper can be enhanced with AI, global collaboration, and adaptive frameworks to tackle 2025’s complex challenges.

Design Researcher

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

A design researcher combines user insight, ethnography, and systems thinking to shape products and services that are meaningful and usable. This persona is key to connecting creative strategy with technical development and scientific discovery.

In the STEAM knowledge graph, the design researcher operates primarily in the Arts domain, but their methods — grounded in evidence and systematic reflection — connect back to Science (empirical rigor), Mathematics (measurement and analysis), and Technology (interaction design).

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.

@startuml
!theme plain
skinparam defaultFontSize 13
skinparam component {
 BackgroundColor #EBF5FB
 BorderColor #2980B9
 FontColor #1A252F
 RoundCorner 10
}
skinparam database {
 BackgroundColor #FEF9E7
 BorderColor #F39C12
}
skinparam rectangle {
 BackgroundColor #EAFAF1
 BorderColor #27AE60
 RoundCorner 8
}
skinparam arrow {
 Color #2C3E50
 FontColor #555
 FontSize 11
}

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.