Ontology or Entropy? The strategic shift in the Information asset economy

Rethinking the Limits of Hierarchical Taxonomies
Taxonomies organise data into fixed, predefined categories, typically in a top-down structure. While this is helpful for clear-cut classification tasks, it falls short in handling complex, multi-dimensional information environments. These models presume that each item fits neatly into a single category or follows a linear developmental path, an assumption that often leads to misrepresenting the ‘real world’, it seeks to describe and provide a sematic abstraction layer for.

One of the core challenges with taxonomies is their rigidity. They tend to be static, lacking the agility to accommodate new data types, emerging relationships, or shifting organisational requirements. As a result, entities are frequently stripped of their contextual richness. An employee, for instance, may simultaneously act as a project manager and a subject matter expert across different teams. Taxonomical systems often struggle to reflect this duality.

In the archival context, a record of a public health initiative may intersect with legislation, funding models, regional service delivery, and cultural engagement. Traditional taxonomies find it difficult to capture and represent such overlapping contexts in any meaningful way, and completely fail to capture this intersectionality and complexity.

Unlocking the Power of Relational Ontologies
A relational ontology introduces a more flexible and scalable framework. Unlike taxonomies, which categorise items in isolation, ontologies describe entities in relation to one another across multiple dimensions, whether temporal, thematic, functional, or ethical.

This relational model offers several strategic advantages. First, it grows organically with an organisation’s evolving needs, reducing the need for major restructuring. Second, it retains and highlights contextual richness by accurately modelling real-world relationships. A policy document, for example, can be simultaneously recognised as a legal instrument and a communications artefact, linked to its creators, its legislative foundation, and its impacted communities.

This web-like structure resonates with both traditional information and data practices and AI-driven knowledge graph methodologies, in which the meaning of an entity is shaped by its context and relationships.

Responding to Complexity with Contextual Modelling
Modern organisations manage a blend of structured and unstructured data, from IoT sensor outputs to legislative records and citizen interactions. These datasets are not only varied in structure but also deeply interconnected. A relational ontology offers a more natural way to model such complexity, enabling richer discovery and cross-domain insights.

Beyond that, relational ontologies underpin semantic technologies and AI systems. By defining entities through their interconnections, they support context-aware search, inference, and recommendations. This aligns with many metadata principles, and frameworks, e.g. ISO 23081, which emphasise functional relationships over rigid categories.

Interoperability is another major benefit. In federated environments like government or large enterprises, data is often siloed. A shared ontological model provides a common conceptual language that enables systems to exchange information more effectively. This is particularly valuable in cross-agency collaborations and multi-stakeholder projects.

Additionally, relational ontologies allow for responsive data governance. When new regulations or risks arise, such as a privacy law affecting certain data types, the ontology can identify related entities and prompt the necessary updates or protections. This supports proactive control over information and allows us to meet our regulatory obligations and societal expectations much more effectively.

Moving Beyond Rigid Classifications
Taxonomies can be useful where environments are predictable and well-bounded. However, they struggle with change. New categories or relationships require restructuring, which can be time-consuming and disruptive. In AI applications, hierarchical models often obscure the complexity of data, reducing their usefulness in training intelligent systems.

They also tend to fragment data. Similar or related content may be stored in different places, leading to redundancy and inefficiency. In archival science, these issues are evident when attempting to describe records with multiple provenances or aggregate diverse content types within a coherent framework.

Strategic Advantages and Future Resilience
Embracing relational ontologies is not just a technical improvement; it’s a strategic necessity for future resilience.

Organisations can tailor ontologies to reflect their specific structures, processes, and vocabulary. This makes AI applications more accurate and aligned with organisational values and culture. Generative AI systems, for instance, need rich contextual data to produce meaningful, trustworthy outputs. Ontologies that encode cultural, ethical, and legal parameters, such as those from Indigenous data governance frameworks like CARE and Te Mana Raraunga, can guide AI toward respectful and relevant outcomes, and create a basis for partnership, and federation ownership models for data and information.

Relational ontologies also empower decision-makers. They enable querying across dimensions, visualising relationships, and drawing connections that inform policy, strategy, and service delivery. This supports a shift from reactive data use to proactive, evidence-based governance.

Bridging Ethical AI and Records Management
Records and information management (RIM) has long prioritised provenance, context, access, and lifecycle, concepts which are also central to ethical AI. This overlap is not coincidental. It signals a shift in how we understand and use data; not merely as static facts to be categorised, but as dynamic assets situated within complex relational networks.

Ethical AI governance calls for transparency, traceability, and fairness, qualities that cannot be ensured through siloed or oversimplified systems. Ontology-driven RIM practices provide the structure needed for explainable decisions, traceable data lineage, and demonstrable compliance. In doing so, they become essential to any ethical, accountable use of AI.

Conclusion
The era of rigid taxonomies is giving way to more nuanced, relational approaches that can keep pace with the complexities of modern data environments. A fully relational ontology enables organisations to understand and navigate their information landscapes more effectively. It accommodates change, supports AI, and aligns with ethical imperatives.

Far from being a technical afterthought, relational ontology is a strategic cornerstone. It helps organisations represent their knowledge faithfully, govern their data responsibly, and harness technology with clarity and confidence.

Now is the time to reinvest in the principles of records and information management, not as legacy operations, but as foundational practices for a future defined by ethics, intelligence, and relational understanding.

References
1. Archives New Zealand. (2023). Atamai hangahanga me ngā mauhanga tūmatanui
2. Artificial intelligence and public and local authority records. https://www.archives.govt.nz/manage-information/how-to-manage-your-information/implementation/artificial-intelligence-and-public-records
3. Floridi, L. (2010–2022). Various works on the ethics of information and the philosophy of the infosphere. [See e.g., The Ethics of Information (2013); The Logic of Information (2019).]
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8. International Organization for Standardization. (2017). ISO 23081-1:2017 — Information and documentation – Metadata for records – Part 1: Principles. https://www.iso.org/standard/67904.html
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11. Philip C Brooks, “What Records Shall We Preserve?”: in which he introduced the concept of the “life history” of records, a concept later generally called the “life cycle of records.” Brooks 1940, see also Brooks, Philip Coolidge. “The Selection of Records for Preservation.” American Archivist 3, no. 4 (October 1940): 221–234. https://doi.org/10.17723/aarc.3.4.u77415458gu22n65.”
12. Public Records Act 2005 https://www.legislation.govt.nz/act/public/2005/0040/latest/DLM345529.html
13. Research Data Alliance International Indigenous Data Sovereignty Interest Group. (2019). CARE Principles for Indigenous Data Governance. https://www.gida-global.org/care
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15. Te Mana Raraunga – Māori Data Sovereignty Network. (n.d.). Principles of Māori Data Sovereignty. https://www.temanararaunga.maori.nz
16. The AI Act (Regulation (EU) 2024/1689 https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai#:~:text=The%20AI%20Act%20(Regulation%20(EU,regarding%20specific%20uses%20of%20AI