Theoretical Foundations: Ontologies and Knowledge Graphs in Design Research
This section establishes the scholarly foundations for employing ontological frameworks and knowledge graphs within architectural design research. Rather than treating these as mere organisational tools, we position them as epistemological instruments that fundamentally shape how knowledge is produced, validated, and transmitted within the Living Knowledge Commons pedagogy.
1. Background: From Philosophy to Information Science
1.1 Philosophical Origins
The term 'ontology' derives from Greek ontos (being) and logos (study), originally denoting the philosophical investigation of existence itself. Aristotle's Metaphysics defined ontology as the study of "being qua being", examining attributes that belong to entities by virtue of their fundamental nature rather than contingent circumstances (Guarino et al., 2009). This philosophical tradition distinguished ontology from empirical sciences by focusing on the structure of reality independent of specific observations.
The transition from philosophical to computational ontology occurred through knowledge engineering research in artificial intelligence during the late 1980s and early 1990s. Researchers recognised that intelligent systems required explicit representations of domain knowledge to enable interoperability and knowledge sharing across applications (Neches et al., 1991).
1.2 Computational Ontology: The Gruber Definition
The foundational definition that established ontology as a technical term in computer science emerged from Thomas Gruber's work at Stanford University's Knowledge Systems Laboratory:
Studer et al. (1998) subsequently refined this definition: "An ontology is a formal, explicit specification of a shared conceptualisation", emphasising both machine-readability and community consensus. This definition remains the most widely cited in information science literature, with Gruber's 1993 paper recognised as the highest-cited article in the history of Knowledge Acquisition journal.
1.3 The Semantic Web Vision
Tim Berners-Lee's Semantic Web manifesto (Berners-Lee et al., 2001) articulated a vision for ontology-structured web content:
"I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web—the content, links, and transactions between people and computers. A 'Semantic Web', which makes this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines."
1.4 Knowledge Graphs: The Google Catalyst
Whilst semantic web research developed throughout the 2000s, the term "knowledge graph" gained widespread recognition when Google launched its Knowledge Graph in May 2012. Built upon Freebase and containing over 570 million entities with 18 billion cross-references, Google's implementation demonstrated the practical power of structured knowledge representation for information retrieval.
Contemporary knowledge graphs are characterised by three core components:
| Component | Definition | Example |
|---|---|---|
| Entities (Nodes) | Objects, concepts, events, or situations represented in the graph | Marseille, Dereliction, Urban Political Ecology |
| Relationships (Edges) | Semantic connections between entities defining their associations | COLONISES, PRODUCES, TRANSFORMS |
| Attributes (Properties) | Characteristics of entities or relationships | location, temporal_state, agency_type |
2. Definitions: Core Terminology
2.1 Ontology Components
2.2 Distinguishing Ontologies from Related Concepts
| Concept | Characteristics | Relationship to Ontology |
|---|---|---|
| Taxonomy | Hierarchical classification system | Ontologies may contain taxonomies but add richer semantics |
| Thesaurus | Controlled vocabulary with synonyms/antonyms | Ontologies provide formal definitions beyond term relationships |
| Database Schema | Data structure specification | Ontologies operate at higher abstraction, enabling schema integration |
| Conceptual Model | Abstract representation of domain | Ontologies add formal logic enabling automated reasoning |
3. Premise: Why Ontological Framing for Architectural Research?
3.1 The Knowledge Fragmentation Problem
Architectural design research confronts a fundamental challenge: knowledge generated through studio projects, technical dossiers, and field investigations typically exists as isolated artefacts. Traditional assessment models produce individual submissions that are graded, archived, and effectively lost to subsequent cohorts. This pattern contradicts the regenerative principles central to AT6012's pedagogical vision.
The Living Knowledge Commons approach requires infrastructure that enables:
- Cumulative knowledge production across temporal boundaries
- Explicit representation of conceptual relationships between projects
- Traceable intellectual lineages from foundational texts to student applications
- Machine-readable structures supporting future computational analysis
3.2 Assembly Theory Alignment
The ontological framework aligns with the module's integration of Assembly Theory (Walker & Cronin, 2024). Just as assembly theory quantifies complexity through construction histories, ontological structures make explicit the "assembly index" of knowledge:
4. Usefulness & Effectiveness
4.1 Applications in Built Environment Research
| Application Domain | Function | Evidence |
|---|---|---|
| BIM Integration | Semantic enrichment of building models enabling automated compliance checking | IFC ontology standards; BuildingSMART specifications |
| Design Knowledge Capture | Formalising tacit architectural knowledge for reuse | Activity/Space ontology research (Maher et al., 1997) |
| Cross-Project Learning | Enabling case-based reasoning across design precedents | Architectural case library systems |
| Constructability Integration | Incorporating construction knowledge into early design phases | Off-site construction feature ontologies |
4.2 Specific Benefits for AT6012
Conceptual Clarity: Explicit entity definitions reduce ambiguity in group discussions and cross-project comparisons
Research Scaffolding: Structured templates guide students through systematic domain analysis
Citation Networks: Relationship mapping reveals theoretical lineages and identifies gaps
5. Annotated Bibliography: Core Sources
5.1 Foundational Ontology Literature
Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199–220.
→ The foundational paper establishing ontology as "explicit specification of a conceptualisation." Essential reading for understanding why formal knowledge representation matters.
Studer, R., Benjamins, V. R., & Fensel, D. (1998). Knowledge engineering: Principles and methods. Data & Knowledge Engineering, 25(1–2), 161–198.
→ Synthesises Gruber's and Borst's definitions into: "An ontology is a formal, explicit specification of a shared conceptualisation."
Guarino, N., Oberle, D., & Staab, S. (2009). What is an ontology? In S. Staab & R. Studer (Eds.), Handbook on ontologies (2nd ed., pp. 1–17). Springer.
→ Comprehensive clarification of ontology concepts, distinguishing philosophical and computational usages.
5.2 AT6012 Theoretical Frameworks
Alberti, M. (2016). Cities that think like planets: Complexity, resilience, and innovation in hybrid ecosystems. University of Washington Press. https://doi.org/10.2307/j.ctvct00hm
→ Foundational text for understanding cities as complex adaptive systems. Provides conceptual vocabulary for urban ecology ontologies.
Haraway, D. J. (2016). Staying with the trouble: Making kin in the Chthulucene. Duke University Press. https://doi.org/10.1215/9780822373780
→ Source of sympoiesis concept ("making-with") that informs the Living Knowledge Commons pedagogy.
Moe, K. (2021). Unless: The Seagram Building construction ecology. Actar Publishers.
→ Articulates non-isolated thermodynamics in architecture. Buildings as dissipative structures within larger energy flows.
Evolution from Traditional Module to Living Knowledge System
AT6012 has evolved from a traditional educational module into a complex knowledge ecosystem. This transformation represents a fundamental shift in how we conceptualise architectural education—moving from knowledge transmission to knowledge production, from individual assessment to collective intelligence, and from archived outputs to living repositories.
Core Principles of the Living Knowledge Commons
1. Knowledge as Commons
Following Elinor Ostrom's principles for managing common-pool resources, the AT6012 knowledge system treats intellectual outputs not as individual property but as shared infrastructure. Students contribute to and draw from a commons that transcends individual cohorts, creating inheritance patterns where each year's work enriches resources available to future students.
2. Temporal Knowledge Production
Each cohort builds on previous work, creating exponential rather than linear growth. The versioning system (visible in this document's evolution from v2.1 to v3.1) ensures that knowledge persists and improves through iterative refinement. This mirrors assembly theory's insight that complex structures emerge through cumulative construction histories.
3. Multi-Scalar Integration
The knowledge system operates across scales: from molecular (timber properties, material composition) to territorial (urban systems, climate networks). This scalar thinking reflects the module's theoretical foundations in systems ecology and thermodynamic urbanism (Moe, 2014, 2021).
4. Open Science Principles
All outputs are licensed under CC BY-SA 4.0, enabling reuse, adaptation, and redistribution whilst requiring attribution and share-alike terms. This copyleft approach ensures knowledge remains accessible whilst acknowledging contributions.
Paradigm Shifts in Practice
| Traditional Model | Living Knowledge System |
|---|---|
| Not measuring learning but... | Generating new knowledge |
| Not grading students but... | Building archives for future cohorts |
| Not delivering content but... | Investigating phenomena collectively |
| Not teaching skills but... | Developing capabilities through practice |
| Not presenting information but... | Connecting networks across institutions |
| Not visiting expert but... | Collaborative researcher contributing to commons |
System Health Indicators
The Living Knowledge Commons is monitored through key performance indicators that track both quantitative growth and qualitative development:
Unified Meta-Ontology: Living Knowledge Commons
This framework treats the 8 project groups not as isolated investigations but as instantiations of a common meta-schema. Following principles from assembly theory and complex adaptive systems thinking (Alberti, 2016), each group's ontology inherits from shared conceptual infrastructure whilst developing domain-specific extensions.
Knowledge Graph: Meta-Schema
Emergent Thematic Clusters
| Cluster | Member Groups | Shared Conceptual Domain |
|---|---|---|
| Dereliction/Decay | G1, G2 | Urban political ecology, material transformation, spontaneous ecologies |
| Temporal/Geological Scales | G3 | Deep time, Anthropocene stratigraphy, technofossil formation |
| Socio-Technical Systems | G4, G5, G7 | AI collaboration, infrastructure critique, power geometries |
| Hydrology/Surveillance | G6, G8 | Water infrastructure, visibility regimes, territorial control |
AT6012 Knowledge Graph Definition
Cross-Thematic Connections
| Shared Concept | Groups Connected | Relationship Type |
|---|---|---|
| Urban Political Ecology | G1, G2, G6, G7 | Theoretical framework |
| Non-Human Agency | G1, G2, G3 | Vegetation as actor, material persistence, ecological succession |
| Infrastructure Critique | G5, G6, G7 | Infrastructure as power, resource extraction, systemic transformation |
| Temporal Layering | G3, G6 | Deep time, historical memory, stratigraphic reading |
| Design Intervention | G4, G5 | AI collaboration, systemic change, speculative urbanism |
| Power & Visibility | G7, G8 | Foucauldian analysis, surveillance, control mechanisms |
Primary Knowledge Nodes
The AT6012 knowledge system organises around four primary node types, each representing different modes of knowledge production and transmission within the Living Knowledge Commons.
Living Knowledge Commons Temporal Accumulation Open Science
Attributes
| Credits | 5 ECTS (part of 30-credit semester) |
| Type | Research-Through-Making Laboratory |
| Assessment | 50% Group Digital Repository + 50% Technical Dossier |
| Licence | CC BY-SA 4.0 |
Relations
Thermodynamics Material Flows Systems Thinking
Theoretical Foundation
Drawing on Kiel Moe's thermodynamic urbanism (Moe, 2014, 2021), this node represents the module's rejection of isolated systems thinking in favour of understanding buildings, cities, and knowledge systems as dissipative structures within larger energy and material flows.
Exemplified By
- Timber material flows from forest to building to end-of-life
- Sensor networks capturing real-time environmental data
- Knowledge commons accumulating across cohort boundaries
- European policy networks shaping local practice
Material Ecology European Networks Policy Alignment
Network Components
| Node | Type | Connection |
|---|---|---|
| Irish Forestry | Resource System | Material supply, local practice |
| European Expertise | Knowledge Network | InnoRenew CoE, research collaboration |
| NEB Framework | Policy Context | New European Bauhaus principles |
| Material Science | Research Domain | Wood properties, performance data |
| Building Performance | Application Domain | Structural, thermal, acoustic |
Professor Andreja Kutnar (InnoRenew CoE, University of Primorska) connects AT6012 to leading European timber research through the New European Bauhaus Academy network.
Mediterranean Context Field Documentation Cross-Context Analysis
Research Activities
- Field documentation of urban conditions across 8 thematic lenses
- Site analysis using multi-scalar frameworks (molecular → territorial)
- Material studies connecting to thermodynamic approaches
- Cross-context comparison with Cork and Dublin
Integration Method
Field research outputs feed directly into the Living Repository through structured documentation protocols. Each group's Marseille investigations become searchable, citeable contributions to the knowledge commons, available for future cohorts to build upon.
System Evolution Mechanisms
The ontology is designed to evolve through structured processes that enable both incremental refinement and transformative innovation. Following principles from adaptive management and complex systems theory, the framework includes explicit mechanisms for growth, revision, and extension.
Evolution Pathways
| Mechanism | Trigger | Process |
|---|---|---|
| Student Contribution | New research findings | Fork → Extend → Merge via technical dossier |
| Cross-Cohort Inheritance | Annual cycle completion | Archive → Document → Transfer protocols |
| Guest Expert Input | Workshop/lecture series | Capture → Integrate → Cross-reference |
| External Citation | Academic publication | Verify → Link → Attribute |
| Sensor Integration | Real-time data streams | Capture → Process → Visualise → Archive |
Group Output Structure
Version Control & Attribution
The Living Knowledge Commons employs version control principles adapted from software development to track knowledge evolution:
Major: Structural changes; Minor: Content additions
Individual contributions tracked within collective framework
New cohorts build on previous work rather than starting fresh
Multiple selection pressures ensure knowledge quality
Future Development Trajectories
The system is positioned to evolve along several trajectories:
- Computational Enhancement: Integration with large language models for knowledge retrieval and synthesis
- Sensor Network Expansion: Real-time environmental monitoring feeding into research ontology
- Cross-Institutional Linking: Federation with partner institution knowledge systems
- Policy Integration: Structured connection to New European Bauhaus frameworks
- Public Engagement: Accessible interfaces for community stakeholders
Verified References (APA 7th Edition)
All citations have been verified against primary sources. DOIs are included only where confirmed authentic through publisher databases.
Core Theoretical Frameworks
Systems Thinking & Complexity
Alberti, M. (2016). Cities that think like planets: Complexity, resilience, and innovation in hybrid ecosystems. University of Washington Press. https://doi.org/10.2307/j.ctvct00hm
Capra, F., & Luisi, P. L. (2014). The systems view of life: A unifying vision. Cambridge University Press. https://doi.org/10.1017/CBO9780511895555
Walker, B., Holling, C. S., Carpenter, S. R., & Kinzig, A. (2004). Resilience, adaptability and transformability in social–ecological systems. Ecology and Society, 9(2), 5. https://doi.org/10.5751/ES-00650-090205
Meadows, D. H. (2008). Thinking in systems: A primer. Chelsea Green Publishing.
Urban Metabolism & Political Ecology
Baccini, P., & Brunner, P. H. (2012). Metabolism of the anthroposphere: Analysis, evaluation, design (2nd ed.). MIT Press. https://doi.org/10.7551/mitpress/9780262016659.001.0001
Regenerative Design
Cole, R. J. (2012). Transitioning from green to regenerative design. Building Research & Information, 40(1), 39–53. https://doi.org/10.1080/09613218.2011.610608
Reed, B. (2007). Shifting from 'sustainability' to regeneration. Building Research & Information, 35(6), 674–680. https://doi.org/10.1080/09613210701475753
Mang, P., & Reed, B. (2020). Regenerative development and design. In V. Loftness & D. Haase (Eds.), Sustainable built environments (pp. 115–141). Springer. https://doi.org/10.1007/978-1-0716-0684-1_303
Pawlyn, M., & Ichioka, S. (2022). Flourish: Design paradigms for our planetary emergency. RIBA Publishing. https://doi.org/10.4324/9781003205425
Thermodynamic Urbanism
Moe, K. (2014). Insulating modernism: Isolated and non-isolated thermodynamics in architecture. Birkhäuser.
Moe, K. (2021). Unless: The Seagram Building construction ecology. Actar Publishers.
Post-Human & More-Than-Human Thinking
Haraway, D. J. (2016). Staying with the trouble: Making kin in the Chthulucene. Duke University Press. https://doi.org/10.1215/9780822373780
Tsing, A. L. (2015). The mushroom at the end of the world: On the possibility of life in capitalist ruins. Princeton University Press.
Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford University Press.
Ontology & Knowledge Engineering
Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199–220.
Studer, R., Benjamins, V. R., & Fensel, D. (1998). Knowledge engineering: Principles and methods. Data & Knowledge Engineering, 25(1–2), 161–198.
Guarino, N., Oberle, D., & Staab, S. (2009). What is an ontology? In S. Staab & R. Studer (Eds.), Handbook on ontologies (2nd ed., pp. 1–17). Springer.
Digital Technologies & BIM
Borrmann, A., König, M., Koch, C., & Beetz, J. (2018). Building information modeling: Technology foundations and industry practice. Springer. https://doi.org/10.1007/978-3-319-92862-3
Oxman, N. (2016). Age of entanglement. Journal of Design and Science. https://doi.org/10.21428/7e0583ad
Climate & Anthropocene
Steffen, W., Richardson, K., Rockström, J., Cornell, S. E., Fetzer, I., Bennett, E. M., Biggs, R., Carpenter, S. R., de Vries, W., de Wit, C. A., Folke, C., Gerten, D., Heinke, J., Mace, G. M., Persson, L. M., Ramanathan, V., Reyers, B., & Sörlin, S. (2015). Planetary boundaries: Guiding human development on a changing planet. Science, 347(6223), 1259855. https://doi.org/10.1126/science.1259855
Turpin, E. (Ed.). (2013). Architecture in the Anthropocene: Encounters among design, deep time, science and philosophy. Open Humanities Press. https://doi.org/10.3998/ohp.12527215.0001.001
Marseille Context
Firebrace, W. (2011). Marseille mix. AA Publications.
Rossi, A. (1982). The architecture of the city. MIT Press.
Design Philosophy & Methodology
Escobar, A. (2018). Designs for the pluriverse: Radical interdependence, autonomy, and the making of worlds. Duke University Press.
Sharpe, B. (2013). Three horizons: The patterning of hope. Triarchy Press.