Knowledge Management: The Strategic Foundation of Research

• 1: From Trenches to Transformation: How Design Study Methodology Can Accelerate Knowledge Evolution in 2025
• 2: Ontological Semantics
• 2.1: Paper on Glass: Digital Process Data Capture with S88/S95 Ontology Alignment

@startuml
!theme plain
skinparam backgroundColor white
skinparam activity {
  BackgroundColor lightblue
  BorderColor darkblue
  FontColor black
}
skinparam arrow {
  Color darkblue
}

start
:Search Existing Knowledge;
note right
  - Literature review
  - Internal databases
  - Expert consultation
  - External repositories
end note

:Perform Gap Analysis;
note right
  - Identify knowledge gaps
  - Assess criticality
  - Prioritize needs
  - Resource evaluation
end note

if (Knowledge Gap Exists?) then (yes)
  :Design Knowledge Acquisition Strategy;
  note right
    - Experiment design
    - Collaboration planning
    - Resource allocation
    - Timeline establishment
  end note
  
  :Execute Knowledge Generation;
  note right
    - Run experiments
    - Collect data
    - Analyze results
    - Validate findings
  end note
  
  :Capture & Document New Knowledge;
  note right
    - Structure findings
    - Create documentation
    - Peer review
    - Quality assurance
  end note
  
else (no)
  :Apply Existing Knowledge;
  note right
    - Use established methods
    - Adapt to context
    - Document application
  end note
endif

:Integrate into Knowledge Base;
note right
  - Update repositories
  - Cross-reference
  - Tag and categorize
  - Version control
end note

:Share & Disseminate;
note right
  - Internal communication
  - External publication
  - Training materials
  - Best practices
end note

:Monitor & Evaluate Impact;
note right
  - Usage metrics
  - Feedback collection
  - Outcome assessment
  - ROI measurement
end note

:Continuous Improvement;
note right
  - Process refinement
  - System updates
  - Training enhancement
  - Tool optimization
end note

stop
@enduml

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

In 2012, Michael Sedlmair, Miriah Meyer, and Tamara Munzner published what would become a foundational paper in visualization research: “Design Study Methodology: Reflections from the Trenches and the Stacks.” More than a decade later, their systematic approach to problem-driven research offers profound insights that extend far beyond visualization into the broader challenge of knowledge evolution in our rapidly changing world.

The Original Vision: Nine Stages of Systematic Discovery#

The authors’ methodology emerged from reflecting on twenty-one design studies and extensive literature review. Their framework consists of nine carefully orchestrated stages: learn, winnow, cast, discover, design, implement, deploy, reflect, and write. What makes this approach revolutionary isn’t just its systematic nature, but its recognition that knowledge creation is inherently iterative and collaborative.

The “trenches” represent the messy, real-world challenges researchers face when working with domain experts. The “stacks” symbolize the accumulated knowledge in academic literature. The methodology bridges these two worlds, creating a structured pathway for transforming practical problems into generalizable knowledge.

The Knowledge Evolution Challenge#

Traditional research often follows a linear path: identify problem, review literature, develop solution, publish results. But this approach struggles with the complexity of modern challenges. Climate change, artificial intelligence ethics, healthcare disparities, and technological disruption require more agile, collaborative approaches to knowledge creation.

The design study methodology anticipated this need by emphasizing:

• Iterative learning cycles that allow researchers to adapt as understanding deepens
• Cross-domain collaboration that breaks down silos between disciplines
• Systematic reflection that extracts transferable insights from specific contexts
• Practical deployment that tests knowledge in real-world conditions

Reimagining the Framework for 2025#

As we navigate an era of unprecedented technological change, the core principles of design study methodology become even more relevant. Here’s how we can adapt this framework for 2025’s unique challenges:

1. Learn → Continuous Learning Networks#

The traditional “learn” phase focused on understanding a specific domain. In 2025, we need continuous learning networks that span multiple domains simultaneously. With AI assistance, researchers can maintain awareness of developments across fields, identifying unexpected connections and emerging patterns.

2025 Opportunity: Create AI-powered research assistants that continuously scan literature, identify emerging trends, and suggest novel cross-domain connections. These systems could flag when developments in one field might impact another, accelerating interdisciplinary insights.

2. Winnow → Adaptive Filtering#

The original “winnow” stage involved narrowing focus to manageable scope. Today’s challenges require adaptive filtering that can dynamically adjust scope based on emerging insights and changing conditions.

2025 Opportunity: Develop adaptive research frameworks that can pivot quickly when new information emerges. This might involve scenario planning approaches where multiple research threads are maintained simultaneously, allowing teams to shift focus as circumstances change.

3. Cast → Diverse Stakeholder Ecosystems#

“Cast” originally meant assembling the right team. In 2025, we need diverse stakeholder ecosystems that include not just domain experts and researchers, but also affected communities, policymakers, and implementation partners from the outset.

2025 Opportunity: Design participatory research platforms that enable broader stakeholder engagement throughout the research process. This could include citizen science components, community feedback loops, and policy maker integration sessions.

4. Discover → Accelerated Insight Generation#

The “discover” phase identified key insights and abstraction opportunities. Modern tools enable accelerated insight generation through AI-assisted pattern recognition, automated hypothesis generation, and real-time data analysis.

2025 Opportunity: Implement AI-human collaboration systems that can process vast amounts of data to identify patterns humans might miss, while maintaining human oversight for contextual interpretation and ethical considerations.

5. Design → Rapid Prototyping Ecosystems#

Traditional design phases often took months or years. 2025 demands rapid prototyping ecosystems that can quickly test multiple approaches simultaneously using simulation, digital twins, and AI-generated alternatives.

2025 Opportunity: Create collaborative design platforms where researchers worldwide can contribute to and iterate on solutions in real-time, using shared simulation environments and standardized testing protocols.

6. Implement → Agile Knowledge Deployment#

Implementation traditionally meant building specific tools or systems. Modern implementation requires agile knowledge deployment strategies that can adapt to different contexts and scale across diverse environments.

2025 Opportunity: Develop modular knowledge frameworks that can be rapidly adapted and deployed across different contexts, with built-in monitoring and adaptation mechanisms.

7. Deploy → Living Laboratory Networks#

Deployment once meant releasing a finished product. Today’s complex challenges require living laboratory networks where solutions continuously evolve based on real-world performance and changing conditions.

2025 Opportunity: Establish global networks of living laboratories that share data, insights, and adaptations in real-time, creating a collective learning system that accelerates knowledge evolution.

8. Reflect → Continuous Meta-Learning#

The reflection stage extracted lessons learned. Modern reflection requires continuous meta-learning that not only captures insights from individual projects but also identifies patterns across multiple studies and domains.

2025 Opportunity: Build AI-assisted reflection systems that can analyze patterns across thousands of research projects, identifying successful strategies, common pitfalls, and emergent best practices.

9. Write → Dynamic Knowledge Artifacts#

Traditional writing produced static papers. 2025 demands dynamic knowledge artifacts that can evolve as new insights emerge and adapt to different audiences and contexts.

2025 Opportunity: Create interactive knowledge platforms where research findings are presented as living documents that can incorporate new data, respond to questions, and adapt their presentation based on the reader’s background and needs.

The Compound Effect: Accelerating Knowledge Evolution#

When these enhanced stages work together, they create a compound effect that could dramatically accelerate knowledge evolution. Instead of isolated research projects producing incremental insights, we could have interconnected networks of adaptive research systems that:

• Share insights in real-time across disciplines
• Adapt quickly to emerging challenges
• Involve diverse stakeholders throughout the process
• Generate solutions that can be rapidly deployed and evolved
• Learn from each other’s successes and failures

Practical Steps for 2025#

Organizations and researchers can begin implementing these ideas immediately:

For Research Institutions:

• Invest in AI-powered research assistance tools
• Create interdisciplinary collaboration platforms
• Establish partnerships with implementation organizations
• Develop rapid prototyping capabilities

For Funding Agencies:

• Support longer-term, adaptive research programs
• Encourage cross-domain collaboration
• Fund living laboratory networks
• Prioritize projects with built-in reflection and learning mechanisms

For Individual Researchers:

• Develop skills in AI-assisted research methods
• Build diverse collaborative networks
• Practice rapid prototyping approaches
• Engage with implementation communities early and often

The Future of Knowledge Creation#

The design study methodology showed us that systematic approaches to knowledge creation can bridge the gap between theory and practice. As we face increasingly complex global challenges, these principles become even more crucial.

The future of knowledge evolution isn’t just about creating new information—it’s about creating adaptive systems that can learn, evolve, and respond to changing conditions. By building on the solid foundation that Sedlmair, Meyer, and Munzner established, we can create research methodologies that are worthy of the challenges we face.

The trenches of 2025 may be digital, global, and interconnected, but the fundamental need for systematic, collaborative, and reflective approaches to knowledge creation remains unchanged. What has changed is our capacity to implement these approaches at unprecedented scale and speed.

The question isn’t whether we can evolve our knowledge creation processes—it’s whether we can do it fast enough to keep pace with the challenges ahead. The design study methodology provides both the foundation and the inspiration for this vital transformation.

2 - Ontological Semantics

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.

Fact Repository#

Stores instantiated knowledge — specific, asserted facts about the world derived from texts, experiments, or expert input. Facts are the populated rows of the ontology’s schema.

Lexicon#

Maps surface word forms to their semantic meanings. A lexicon allows the system to recognize that “kinase inhibitor,” “enzyme blocker,” and “phosphorylation suppressor” may all refer to the same ontological concept, enabling robust natural language understanding across varied terminology.

Onomasticon#

A specialized lexicon for named entities: people, organizations, places, products, and standards (e.g., “ICH Q10,” “S88,” “FDA”). The onomasticon lets the system resolve ambiguous proper nouns to canonical knowledge graph nodes.

Knowledge Representation Language#

The Knowledge Representation Language (KRL) is the formal grammar that ties the four sources together. It allows a system to derive meaning from raw text by:

1. Parsing surface language through the Lexicon
2. Resolving named entities through the Onomasticon
3. Grounding concepts against the Ontology
4. Asserting new facts into the Fact Repository

This pipeline is what transforms unstructured text into structured, queryable knowledge graph entries.

Semantic Analyzer — Large Language Models#

The Semantic Analyzer uses the KRL-structured knowledge to drive user-facing interactions. Modern LLMs play this role, but a key distinction applies:

@startuml
!theme plain
skinparam defaultFontSize 13
skinparam rectangle {
  RoundCorner 10
  FontColor white
}

rectangle "**Black Box LLM**\n(no visible ontology)\n\nGenerates answers\nCannot explain reasoning\nKnowledge is opaque" as BB #C0392B
rectangle "**Glass Box (Ontological Semantics)**\n(explicit ontology + fact repository)\n\nGenerates answers\nCan cite sources\nKnowledge is auditable" as GB #27AE60

BB -right[hidden]-> GB
note bottom of BB : Hallucination risk\nLow traceability
note bottom of GB : Ontology-grounded\nHigh explainability
@enduml

The Glass Box advantage is critical in regulated domains like pharma, manufacturing, and clinical research — where every knowledge claim must be traceable to its source.

Supported Use Cases#

Applied Example: This Site’s Taxonomy as an Ontology#

The STEAM knowledge graph on this site is itself an instance of ontological semantics in practice:

• Ontology nodes: Science, Technology, Engineering, Arts, Mathematics
• Ontology edges: enables, implements, models, formalized by, grounded in
• Lexicon: tags and categories on each page
• Onomasticon: persona names, standard names (ICH, S88, S95)
• Fact Repository: each page’s related_concepts frontmatter

See the full graph: STEAM Knowledge Graph →

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

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.

The Paper on Glass Experience#

Visual Familiarity with Digital Power#

The paper on glass interface maintains the visual metaphors of traditional documentation:

• Form-based layouts that mirror paper worksheets
• Signature fields and approval workflows
• Checkbox lists and manual data entry points
• Familiar navigation patterns and information hierarchy

However, beneath this familiar surface lies a sophisticated data model that captures not just values, but semantic meaning, relationships, and process context.

Contextual Data Capture#

Unlike simple form digitization, true paper on glass systems capture:

• Temporal context: When actions occurred relative to process phases
• Causal relationships: How parameters influence outcomes
• Operational context: Who performed actions and under what conditions
• Equipment state: The configuration and status of process equipment
• Material genealogy: Traceability of inputs through transformation

S88 Ontology Integration#

Recipe Hierarchy Alignment#

The S88 standard defines a clear hierarchy for batch processes:

Recipe Level → Procedure → Unit Operation → Operation → Phase

Paper on glass systems aligned with S88 map traditional recipe cards to this structure:

• Master Recipe: The idealized process definition
• Control Recipe: Site-specific adaptation with equipment bindings
• Batch Record: Execution instance with actual parameters and results

Procedural Control Elements#

S88’s procedural control elements provide semantic structure for recipe steps:

• Phases become interactive workflow steps in the digital interface
• Operations group related phases with clear start/end conditions
• Unit Operations align with equipment modules and process cells
• Procedures represent complete processing sequences

Equipment Entity Modeling#

The S88 equipment model maps directly to paper on glass interfaces:

• Process Cells become top-level organizational units
• Units correspond to major equipment systems
• Equipment Modules represent controllable subsystems
• Control Modules map to individual instruments and actuators

S95 Enterprise Integration#

Functional Hierarchy#

S95 defines four levels of manufacturing operations:

• Level 4: Business planning and logistics
• Level 3: Manufacturing operations management
• Level 2: Supervisory control
• Level 1: Basic control

Paper on glass systems typically operate at Levels 2-3, bridging operator interfaces with enterprise systems.

Information Models#

S95 information models provide structure for:

• Work Orders: Linking recipes to production schedules
• Material Definitions: Standardizing ingredient and product specifications
• Equipment Information: Maintaining asset hierarchies and capabilities
• Production Performance: Capturing efficiency and quality metrics

Activity Models#

S95 activity models define standard operations:

• Production Scheduling: Translating demand into executable recipes
• Production Dispatching: Assigning resources to batch executions
• Production Execution: Real-time monitoring and control
• Production Tracking: Historical analysis and compliance reporting

Unified Recipe Management Framework#

Recipe as Executable Specification#

When aligned with S88/S95 ontologies, recipes become more than instructions—they become executable specifications that:

• Define precise control sequences
• Specify material requirements and constraints
• Establish quality checkpoints and acceptance criteria
• Enable automatic equipment configuration
• Support predictive quality modeling

Semantic Data Binding#

The ontological alignment enables semantic data binding where:

• Process parameters are linked to their physical measurements
• Material additions are tracked through inventory systems
• Quality attributes are connected to analytical results
• Deviations trigger defined response procedures
• Historical data supports continuous improvement

Cross-Recipe Learning#

Standardized ontologies enable learning across recipes:

• Common phases can share optimization insights
• Material properties influence multiple formulations
• Equipment performance patterns apply across products
• Quality correlations span recipe families

Implementation Architecture#

Ontology Layer#

The foundation consists of:

• S88 Process Model: Defining procedural hierarchy and equipment entities
• S95 Information Model: Structuring materials, personnel, and equipment data
• Domain Extensions: Industry-specific concepts and relationships
• Semantic Mappings: Linking legacy data to standardized concepts

Data Capture Layer#

This layer provides:

• Contextual Forms: Paper-like interfaces with semantic data binding
• Workflow Engine: S88-compliant procedure execution
• Real-time Integration: Connecting to process control systems
• Validation Rules: Ensuring data quality and compliance

Analytics Layer#

Advanced capabilities include:

• Process Mining: Discovering optimization opportunities from execution data
• Predictive Quality: Using historical patterns to predict outcomes
• Anomaly Detection: Identifying deviations from normal operation
• Continuous Improvement: Systematic recipe refinement

Benefits and Outcomes#

Operational Excellence#

• Reduced Training Time: Familiar interfaces with enhanced capabilities
• Improved Compliance: Automated validation and audit trails
• Faster Problem Resolution: Structured data enables root cause analysis
• Enhanced Flexibility: Rapid recipe modification and deployment

Strategic Advantages#

• Data Standardization: Consistent semantics across operations
• System Interoperability: Standards-based integration
• Knowledge Preservation: Structured capture of process expertise
• Innovation Acceleration: Data-driven recipe development

Challenges and Considerations#

Change Management#

• Balancing familiar interfaces with new capabilities
• Training operators on enhanced functionality
• Managing the transition from paper-based workflows
• Ensuring buy-in from production teams

Technical Implementation#

• Integrating with existing control systems
• Ensuring real-time performance requirements
• Managing data volume and storage requirements
• Maintaining system reliability and availability

Organizational Alignment#

• Coordinating IT and OT system integration
• Establishing data governance frameworks
• Managing intellectual property and security concerns
• Aligning with regulatory requirements

Future Directions#

AI-Enhanced Recipe Development#

Machine learning algorithms operating on S88/S95-structured data can:

• Automatically optimize process parameters
• Predict quality outcomes from input conditions
• Suggest recipe modifications based on historical performance
• Identify opportunities for process innovation

Digital Twin Integration#

Paper on glass interfaces can serve as control points for digital twins:

• Real-time process simulation during execution
• Predictive modeling for what-if scenarios
• Virtual commissioning of new recipes
• Continuous model refinement from execution data

Extended Reality Applications#

Augmented and virtual reality can enhance the paper on glass experience:

• 3D visualization of process equipment and flows
• Immersive training environments for complex procedures
• Remote collaboration on recipe development
• Enhanced troubleshooting with contextual information overlay

Conclusion#

The paper on glass paradigm, when properly aligned with S88 and S95 ontologies, represents more than interface modernization—it creates a foundation for intelligent manufacturing. By maintaining operator familiarity while introducing semantic rigor, these systems bridge the gap between traditional process knowledge and modern digital capabilities.

The key to success lies not in abandoning proven workflows, but in enhancing them with structured data capture, standardized semantics, and intelligent automation. This approach preserves the valuable tacit knowledge embedded in traditional practices while enabling the data-driven insights necessary for competitive manufacturing.

As industries continue their digital transformation journeys, the paper on glass concept provides a pathway that respects operational heritage while embracing technological possibility. The alignment with established ontologies ensures that today’s digitization efforts build toward tomorrow’s intelligent manufacturing systems.