Interoperability and ‘The temporal aspect’ - do ‘4d’ ontologies represent the next generation technology for AI development?
At the moment, and if you look back over the previous posts, you’ll be able to see that there is a lot of discussion around ‘ontologies’ in general. To summarise; we are increasingly seeing the benefits of applying them to the reasoning we apply to our Artificial Intelligence research and technologies.
For example, if you read this really helpful post from Makolab here - you can get an idea of how and where ontologies are important. Additionally, in my previous post you can see how those applications highlight the importance of enabling context to AI particularly in relation to data provenance.
Picking through the applications and use cases of ontologies in general shows their benefit and what they bring to helping our understanding, both philosophically and technically. In philosophical terms - they generally form the basis of most of our knowledge and understanding of the world. In technical terms - they form an essential step in structuring data, information and knowledge.
So knowing this, the next important thing to highlight is that our current conventional understanding of an ontology is generally based on a ‘3d’ model of thinking. Over the past 6 years and through my association with the great people and the AIM team at Elemendar, I’ve been connected to a programme of research that looks at a different form of ontology, known as a ‘4d Ontology’. I will unpack this in a moment, and to do that, it's probably worth understanding how that is different from conventional 3d ontologies.
Conventional Ontologies are ‘3 Dimensional’ (3D)
If you follow this blog here - you can see structured examples of how conventional 3d ontologies work in principle, and a quick summary of this is as follows-
‘Conventional’ Ontologies store data in a ‘3D’ model - this means, they model particular aspects of data which generally includes entities, types, relationships and individuals. These are fixed and static and are created specifically to model the corresponding real world data they are describing.
Today, in late 2024, nearly all orgnisations use such conventional ontologies (generally in the form of databases) to represent all of their data and information by default. This is has been tremendously beneficial for a long time as such 3D models are cheap, well defined and easy to use and access and we are highly efficient in supporting them.
But - here’s the rub…
As we continue to understand AI and the foundational technologies that support its development - particularly LLM’s and Graph Databases - we are increasingly encountering a real issue with how different 3d Models interact with each other - a challenge we call ‘Interoperability’.
The Interoperability issue
Generally to access the powerful benefits of AI (that come from the growth of larger, hungrier training technologies) more and more sources need to be bought together for processing. And, because of the interoperability issue, this is where 3D models are challenging, as complex reasoning and analysis means that many different sources of data need to be combined. At present, these different sources of data come from 3D sources - which means each source is generally formed from different, fixed schema from different source ontologies.
Smushing together different source 3D data stores into the same format is a considerable headache and a bottleneck in performance for technologies that offer huge promise of scaling (graph databases being a case in point).
So as we increase our understanding of how much data is held in 3D formats and as more cross-comparison and referencing between different databases and data stores is being undertaken for AI tools, the need to convert such sources into consistent formats is becoming more and more of a time consuming and inefficient step, which is where are today.
The ‘temporal aspect’ - Why 4D is different from conventional (3D) ontologies
A 4D ontological model goes beyond the modelling of particular aspects of data featured in 3D ontologies (lets refer back to the aspects mentioned earlier that a conventional ontology describes - entities, types, relationships and individuals). A 4D model also contains all of those aspects, but it also captures the temporal state of those ‘things’ it describes (hence the application of the term '4D').
For example with a 4D ontology, an entity can have a particular relationship with another entity (e.g. an employee within an organisation) and each of the entities and the relationship itself can have a ‘state’ that persists for a specific period of time. So a 4D ontology models all of those aspects including the state of the object (or associated relationship).
How does this address Interoperability
Clearly, the differences between 3D ontologies and 4D ontologies are quite technical. But the crucial point between them surrounds interoperability. The issue with 3d is that by design, all of the data contained within them is fixed to the point in time they were created (and the ontology they are part of was implemented) - this means that whenever data or more crucially our understanding of the data changes this needs to be adjusted. Likewise, when one 3d database needs to be compared with another 3d database, considerable effort is required to make the combined outputs consistent and accessible.
4D works differently to this and addresses interoperability by simply allowing the new instantiation of the same data aspects (entities, types, relationships and individuals) to a new instantiation of the model. So in a sense, a new timestamp. This provides a more ‘open’ schema that effectively grows and expands as more data and information is added to the model. In practice, this is known as a ‘top level ontology’ and gives a far more open, scalable structure that vastly reduces the issue of bespoke interconversion seen with 3D models.
This is probably enough for now…and it is good to remember that 3D is generally perfect for most information needs, but as our technologies increase in scale the issue of interoperability of different data models is becoming more crucial and this means ‘4D ontologies’ do provide a new, alternative approach.
Going back to the use cases in this article - it does show that currently, 4D is harder and more time consuming to implement than 3D, but the potential benefits the ‘temporal aspect’ brings to addressing next generation AI tools means it is a worthwhile investment to consider both in the long term, but also in how we plan our information management today.
Final note - (looking back at this - I appreciate this is quite technical so I will follow up with a visual use case to go through the use case a bit more in the next blog)