In my part one of this commentary, I wrote about the deep roots of ontology and data management. I was able to do so, as I have been fortunate to have a social anthropology and history at undergrad background, so coming into the post-grad world of information and data management, I already had the knowledge of tautology, epistemology and semantics/semiotics as an intellectual framework to pull together ontological conceptual design for information systems.
The key advantage of this route into the world of data management I’ve found is that most technology people haven’t done the arts and social sciences, so don’t really have a deep understanding of philology/language, human communication and encoding systems. Or the way in which all information is socio-political construct, transmitting meaning within a specific cultural context of time and place. Technology/STEM trained professionals tend to view data as an inert object to be encapsulated or held within technological systems, rather than an inherent human expression of meaning to be decoded.
So, again what I’m proposing is going back to the ontological system that has worked for thousands of years but reusing it in a modern context. As a data/information management specialist, my academic training and knowledge is forged in information and archives science and deep-rooted ontological design philosophy.
Being able to apply GPT and LLMs to specific cultures and organisational systems is a brilliant new use case for ancient art of ontology, and the majority of Technologists tend to underappreciate data and information as a cultural construct, only really comprehensible in its original linguistic and socio-political context and all the attendant semantic, ritual and kinship structures. Social scientists and information science scholars will be familiar with this aspect of ontologies to represent complex inter-relationships in both conceptual and visual representational modes of transmission.
The key to this approach is to establish your high-level meta-model:
By incorporating these concepts, an ontology for data and information modelling can create a rich, context-aware and logically sound representation of knowledge that facilitates better data management, reasoning and decision-making.
Many practitioners that come into the data from the technology route also assume that these data problems and solutions are novel. However, it isn’t new, and as stated above the Arab Golden Age genius mathematician Abu Abdallah Muhammad ibn Musa Al-Khwarizmi established algorithms as a new scientific endeavour in the 9th-century. Again, I like using tried and tested approaches, toolsets change across the centuries and now the decades, so there’s no point getting hung up on the ‘here today, gone tomorrow’, ephemeral technology we use to transmit data and information through time, space and cultures.
NB: The meta model is adapted from the U.S. Geological Survey Open-File Report 01-223, Figure 1. Core of the geologic map information meta-model https://pubs.usgs.gov/of/2001/of01-223/soller2.html .
My view of returning to the ancient fundamentals for metadata design and entity-modelling is based on my Information Management post grad study, where we were taught the full history of data management, over the last 5-7000 years. It really hit me, when I was learning Medieval Latin encoding schemes, that they worked in exactly the same way as computer coding systems (my original career was as an engineer, and I’d done some machine coding in the 80s). Then started noticing it everywhere from Sumerian Cuneiform (which AI is finally fully decoding) to Egyptian Hieroglyphs, to Morse code, to the Enigma machine. A good example is Ptolemy’s World Map from 150AD. No maps have survived 2000 years, but the data models and data tables did to render it, and we can therefore use the inherent models and data to recreate the map.
Source: University of Cantabria: https://www.reddit.com/r/MapPorn/comments/29dcg8/claudius_ptolemys_world_map_1400908/
Therefore, we do the same thing as Socrates and Ptolemy and use our grasp of conceptual arrangement to render the ‘real world’ into an entity model in order to deconstruct and disaggregate it, so that we may reimagine and recreate it, in data and information modelled forms.
From the conceptual metamodel we need to arrange the attributes of entities into relational models to render them into machine consumable structures.
Then model them into data systems:
Conclusion: The Case for a Fully Relational Ontology
In the modern era of information and data management, a fully relational ontology approach offers unparalleled benefits over traditional taxonomical models. It provides the flexibility, contextual depth and adaptability required to navigate today’s data complexity. By moving away from hierarchical structures, organisations can unlock richer insights, enhance interoperability, support AI and machine learning initiatives and future-proof their data strategies in an ever-changing landscape.
A relational ontology is not just a better way to organise data – it’s a strategic necessity for organisations seeking to maximise the value of their information assets in the age of AI and beyond.
NB: The images used here (unless a source is provided) are from my own slide decks and are originally derived from work I undertook on the Asset Management Data Standard and Technical Specifications for Land Transport, NZ Transport Agency, 2019.
Stephen is an Information Management (IM) and AI Consultant. A former Engineer, he has over twenty years’ experience as an IM specialist, as a Chief Data Officer, Chief Archivist and thought leader. His drive to establish a strong intellectual foundation for IM has been expressed through writing articles, and contributing to International Standards such as ISO15489, ISO13028, and ISO16175. As a social anthropologist he understands human systems, and as a technical expert he understands information systems, using technology to connect the two is his life mission. He has a disturbing obsession with ontology and is a recovering historian.
See Stephen’s profile here.