It was published last month by Springer: “The what and how of modelling information and knowledge: from mind maps to ontologies”. The book’s three character-limited unique selling points are that it “introduces models and modelling processes to improve analytical skills and precision; describes and compares five modelling approaches: mind maps, models in biology, conceptual data models, ontologies, and ontology; aims at readers looking for a digestible introduction to information modelling and knowledge representation”. The softcover hardcopy and the eBook are available from Springer, Springer professional, many national and international online retailers (e.g., Amazon), as well as university libraries, and hopefully soon in the ‘science’ section of select bookstores.
There’s also a back flap blurb with the book’s motivations and aims, and intended readership. The remainder of this post are informal comments on it.
From my side as author and having read many popular science books on a wide range of topics, I wanted to write a popular science book too, but then about modelling. Modelling for the masses, as it were, or at least something that is comparatively easily readable for professionals who don’t have a computing background and who haven’t had, or had very little, training in modelling, yet who can greatly benefit from doing so. And to some extent also for computing and IT professionals who’d like a refresher on information modelling or a concise introduction to ontologies but don’t want to (re-)open their textbook tomes from college. Modelling doesn’t lend itself well to juicy world-changing discoveries the same way that vaccines and fungi can be themes for page-turners, but a few tales and juicy details do exist.
Then next consideration was about which aspects of modelling to include and what sort of popular science book to aim for. I distinguished four types of popular science books based on my prior readings, ranging from ‘entertaining layperson’ level holiday reading to ‘advanced interested layperson’ level where having at least a Bachelors in that field or a Master’s degree in an adjacent field may be needed to make it through the tiny-font book. I have no experience writing humour, and modelling is a rather dry topic compared to laugh-out-loud musings and investigations into stupidity, drunkenness, or elephants on acid—that entertainment can be found here, here, and here—so that was easily excluded. I’ve already tried out advanced texts tailored to specialists, in the form of an award-winning postgraduate textbook on ontology engineering, and wasn’t in the mood for writing another such book at the time when I was exploring ideas, which was around late 2021 and early 2022. I think this modelling book ended up between the two extremes regarding the amount of content, difficulty, and readability.
And so, I chose the tone of writing to be in so-called ‘casual writing’ style to make it more readable, there are a few anecdotes to enliven the text as is customary for popular science books, and the first three chapters are relatively easy in content compared to later chapters. The difficulty level of the chapters’ contents is turned up a notch each chapter going from Chapters 2 to 6 when we’re moving onwards with the journey passing by the five types of models covered in the book. Each successive chapter solves modelling limitations from the preceding chapter, and so it gets more challenging at least up to Chapter 5 (ontologies). Whether a reader finds Chapter 6 on Ontology (philosophy) even harder, depends on their background, because in other ways it is easier than ontologies because we can set aside certain interfering practicalities.
Chapter 7 mixes easier use cases with theoretically more abstract sections when we’re putting things together, reflect on Chapters 2-6, and look ahead. There’s no avoiding a little challenge. But then, we read non-fiction/science/tech books to learn from it and learning requires some effort.
Aside from the reader learning from reading the book, an author is supposed to gain new insights from writing it. And so did I. Moreover, upfront when planning the book, I tried to make sure I likely would. I mention a few salient points in the preface and I’ll select two for this blog post: the cladograms (Section 3.2.1) and the task-based evaluation (Section 7.1.2.2).
Diagrams/models in biology are sometimes ridiculed as “cartoons” by non-biologists. Cladograms would be the xkcd version of it, visually. I already knew that there are common practices, recurring icons, and rules governing the biological models drawn as diagrams. Digging deeper to find more diagrams with rules governing their notation, cladograms came up. They visualise key aspects of the scientific theory of evolution. Conversely, drawing an evolutionary diagram that doesn’t adhere to those rules then amounts to misunderstanding evolution. I think the case deserves more attention, especially because a bunch of school textbooks have been shown to have errors, and there’s room for improvement designing cladogram drawing software. Maybe clarifying matters and being more precise with such models helps resolve some debates on the topic as well.
The motivation for the task-based evaluation is easy to argue for in theory — actually doing it offered a deeper understanding, and writing the book spurred me to do so. One of my claims in the beginning of the book is that with better modelling—better than mind maps, not better mind maps—one learns more. The task-based evaluation is precisely about that. We take one page from a textbook and try to create a model of it, one for each type of model covered in the book. It demonstrates in a clear and straightforward way — assisted by Bloom’s taxonomy if you so fancy — why developing an ontology is much harder than developing a mind map or a conceptual data model, and in what way designing a conceptual data model of that textbook page is better for learning the content than creating a mind map of it.
There were more joys of writing the book. Like that the running example—dance—was also good for some additional interesting paper reading beyond what I already had read and engaged with in various projects. (There are also other subject domains in the examples and illustrations, such as fermentation, peace, labour law, and stuff, and a separate post will be dedicated to more content of the book.)
To jump the gun on questions like “why didn’t you include my preferred type of model or my language, being [DSL x/KG y/BPM z/etc.]?”: the point I wanted to make with this book was made with these five types of models and this was the shortest coherent story arc with which I could do it. The DSLs/KGs/BPMs/etc are not less worthy, but they would have caused the number of pages to explode without adding to the argument. As consolation, perhaps: knowledge graphs (KGs) are likely to appear in a v2 of my ontology engineering textbook and BPM likely will be linked to the TREND temporal conceptual data modelling language, but that’s future music.
Last, I’ve created a web page for the book, which collates information about the book, such as direct links where to buy it, media coverage and links to recent related blog posts (e.g., this one is a spin-off [with an add-on] of an early draft of section 6.3 and that one of a draft of section 7.3), and has extra supplementary material, including a longer illustration of a conceptual model design procedure using a prospective dance school database as example. Feedback is welcome!
Countless articles have announced the death of symbolic AI, which includes, among others, ontology engineering, in favour of data-driven AI with deep learning, even more loudly so since large language model-based apps like ChatGPT have captured the public’s attention and imagination. There are those who don’t even realise there is more to AI than deep learning with neural networks. But there is; have a look at the ACM Computing Classification or scroll down to the screenshots at the end of this post if you’re unaware of that. With all the hype and narrow focus, doom and gloom is being predicted with a new AI winter on the cards. But is it? It’s not like we all ditched mathematics at school when portable calculators became cheap gadgets, so why would AI now with machine and deep learning and Large Language Models (LLMs) and an app that attracts attention? Let me touch upon a few examples to illustrate that ontologies have not become obsolete, nor will they.
How exactly do you think data integration is done? Maybe ChatGPT can tell you what’s involved, superficially, but it won’t actually do it for you. Consider, for instance, a paper published earlier this month, on finding clusters of long Covid patient symptoms [Reese23], described in a press release: they obtained data of 20,532 relevant patients from 38 (!!) data partners, where the authors mapped the clinical findings taken from the electronic health records “to computable terms contained in the Human Phenotype Ontology (HPO), a standard framework for describing human traits … This allowed the researchers to analyze the data across the entire cohort.” (italics are mine). Here’s an illustration of the idea:
Could reliable data integration possibly be done by LLMs? No, not even in the future. NLP with electronic health records is an option, true, but it won’t harmonise terminology for you, nor will it integrate different electronic health record systems.
LLMs aren’t good at playing with data in the myriad of ways where ontologies are used to power ‘intelligent’ applications. Data that’s generated in automation of scientific experiments, for instance, like that cell types in the brain need to be annotated and processed to try to find new cell types and then add annotations with those new types, which is used downstream in queries and further analysis [Tan23]. There is no new stuff in off-the-shelf LLMs, so they can’t help; ontologies can – and do. Ontologies are used and extended as needed to document the new ground truth, which won’t ever be replaced by LLMs, nor by the approximations that machine learning’s outputs are.
What about intelligent analysis of real-time data? Those LLMs won’t be of assistance there either. Take, e.g., energy-optimised building systems control: the system takes real-time data that is linked to an ontology and then it can automatically derive energy conservation measures for the building and its use [Pruvost22].
Much has been written on ChatGPT and education. It’s an application domain that permits for no mistakes on the teaching side of it and, in fact, demands for vetted quality. There are many tasks, from content presentation to assessment. ChatGPT can generate quiz questions, indeed, but only on general knowledge. It can generate a response as well, but whether that will be correct answer is another matter altogether. We also need other types of educational questions besides MCQs, in many disciplines, on specific texts and textbooks with its particular vocabulary, and have the answer computed for automated marking. Computing correct questions and answers can be done with ontologies and some basic automated reasoning services [Raboanary22]. One obtains precision with ontologies that cannot be had with probabilistic guessing. Or take the Foundational Model of Anatomy ontology as a concrete example, which is used to manage the topics in anatomy classes augmented with VR [Soergel22]. Ontologies can also be used as a method of teaching, in art history no less, to push students to dig into the details and be precise [Bertens22] – the opposite of bland, handwaivy, roughly, sort of, non-committal, and fickle responses ChatGPT provides, at times, to open questions.
They’re just a few application examples that I lazily came across in the timespan of a mere 15 minutes (including selecting them) – one via the LinkedIn timeline, a GS search on “ontologies” with a “since 2022” (17300 results this morning) and clicking a few links that sounded appealing, and one I’m involved in.
This post is not a cry of desperation before sinking, but, rather, mainly one of annoyance. Technology blinkers of any kind are no good and one better has more than just a hammer in one’s toolbox. Not everything can be solved by LLMs and deep learning, and Knowledge Representation (& Reasoning) is not dead. It may have been elbowed to the side by the new kids on the block. I suspect that those in the ‘symbolic AI is obsolete’ camp simply aren’t aware – or would like to pretend not to be aware – of the many different AI-driven computing tasks that need to be solved and implemented. Tasks for which there are no humongous amounts of text or non-text data to grab and learn from. Tasks that are not tolerant to outputs that are noisy or plain wrong. Tasks that require current data, not stale stuff from over a year old and longer ago. Tasks where past data are not a good predictor for the future. Tasks in specialised domains. Tasks that are quirky to a locale. And so on. The NLP community already has recognised LLM’s outputs need fixing, which I was pleasantly surprised with when I attended EMNLP’22 in December (see my EMNLP22 trip report for a few pointers).
Also, and casting the net a little wider, our academic year is about to start, where students need to choose projects and courses, including, among others, another installment of ontology engineering, of logic for AI, Computer Vision, and so on. Perhaps this might assist in choosing and in reflecting that computing as a whole is not going to be obsolete either. ChatGPT and CodePilot can probably pass our 1st-year practical assignments, but there’s so much more computing beyond that, that relies on students understanding the foundations and problem-solving methods. Why should the whole rest of AI, and even computing as a discipline, become obsolete the instant a tool can, at best, regurgitate the known coding solutions to common basic tasks. There are still mathematicians notwithstanding all the devices more powerful than a pocket calculator and there are linguists regardless the free availability of Google Translate’s services; so why would software engineers not remain when there’s a code-completion tool for basic tasks.
Perhaps you still do not care about ontologies and knowledge representation & reasoning. That’s fine; everyone has their interests – just don’t confound new interests for obsolescence of established topics. In case you do want to know more about ontologies and ontology engineering: you may like to have a look at my award-winning open textbook, with exercises, tools, and slides.
p.s.: here are those screenshots on the ACM classification and AI, annotated:
How do you know whether the ontology you developed or want to reuse is any good? It’s not a new question. It has been investigated quite a bit, and so the answer to that is not a short one. Based on a number of anecdotes, however, it seems ever more people are leaning toward a short answer along the line of “it’ll be fine if it can answer my competency questions”. That is most certainly not the right answer. Let me illustrate this.
Here’s a set of 5 competency questions and a bad ontology (with the OWL file), being a newly mutilated version of the African Wildlife Ontology [1] modified with a popular South African pastime: the braai, i.e., a barbecue.
CQ1: Which animals are served at a barbecue? (Sample answers: kudu, impala, warthog)
CQ2: What are the materials used for a barbecue? (Sample answers: tongs, skewers, poolbraai)
CQ3: What is the energy source for a braai device? (Sample answers: gas, coal)
CQ4: Which vegetables taste good with a braai? (Sample answers: tomatoes, onion, butternut)
CQ5: What food is eaten at a braai, or: what collection of edible things are offered?
The bad ontology does have answers to the competency questions, so a ‘CQs-only’ criterion for quality would suggest that the bad ontology is a good one. 100% good, even.
Why is it a bad one nonetheless?
That’s where years of methods, techniques, and tool development enter the stage (my textbook dedicates Section 5.2 to that), there are heuristics-based tips to prevent pitfalls [2] in general and for bio-ontologies with GoodOD, and there’s also a framework for ontology quality, OQuaRE [3], that all aim to approach this issue of quality systematically. Let’s have look at some of that.
Low-hanging fruit for a quick sanity check is to run the ontology through the Ontology Pitfall Scanner OOPS! [4]. Here’s the summary result, with two opened up that show what was flagged and why:
Mixing naming conventions is not neat. Examples of those in the badBBQ ontology are using CamelCase with PoolBraai but dash in tasty-plant and spaces converted to underscores in Food_Preparation_Material, and lower-case for some classes and upper case for others (PoolBraai and plant). An example of unconnected ontology element is Site: the idea is that if it isn’t really used anywhere in the ontology, then maybe it shouldn’t be in the ontology, or you forgot to add something there and OOPS! points you to that. Pitfall P11 may be contested, but if at all possible, one really should add domain and range to the object property so as to minimise unintended models and make the ontology closer to the reality (or understanding thereof) one aims to present. For instance, surely eats should not have any of the braai equipment on the left-hand side in the domain position, because equipment does not eat—only organisms do.
At the other end of the spectrum are the philosophy and Ontology-inspired methods. The most well-known one is OntoClean [5], which is summarised in the textbook and there’s a tutorial for it in Appendix A. The, perhaps, most straightforward (and simplified) rule within that package is that anti-rigid classes cannot subsume rigid classes, or, in layperson terminology: (physical) entities cannot be subclasses of things that are roles that entities play. Person cannot be a subclass of Employee, since not all persons are always employees. For the badBBQ: Food is a role that an organism or part thereof plays in a certain context, and animals and plants are not always food—they are organisms (or part thereof) irrespective of the roles they may play (or, worded differently: of the roles that they are the ‘bearer of’).
Then there are the methods and tools in-between these two extremes. Take, for instance, Advocatus Diaboli / PEW (Possible World Explorer) [6], which helps you find places where disjointness axioms ought to be added. This is in the same line of thinking as adding those domain and range axioms: it helps you to be more precise and find mistakes. For instance, Site and BraaiEquipment are definitely intended to be disjoint: some location cannot be a concrete physicalobject. Adding the disjointness axiom results in an error, however: the PoolBraai is unsatisfiable because it was declared to be both a subclass of Site and of BraaiEquipment. Pool braais do exist, as there are braais that can be placed in or next to a pool. What the issue is here, is that there are two different meanings of the same term: once that device for the barbecue and once the ‘braai area by the pool’. That is, they are two different entities, not one, and so they either have to appear as two different entities in the ontology, with different names, or the intended one chosen and one of the subsumption axioms removed.
I also put some ugly things in the description of Braai: both those two ways of the source of heating and the member. While one may say informally that a braai involves a collection of things (CQ5), ontologically, it won’t fly with ‘member’. Membership is not arbitrary. There are foundational (or top-level) ontologies whose developers already did the heavy-lifting of ontological analysis of key elements and membership is one of them (see, among others, [7-9]). Such relations can simply be reused in one’s own ontology (e.g., imported from here), with their widely-agreed upon meaning; there’s even a tool to assist you with that [10]. If what you want is something else than that, then that relation is not membership but indeed something else. In this case, there are two options to fix it: 1) a braai as an event (rather than the device) will have objects (such as food, the tongs) participating in the event, or 2) for the braai as a device, it has accessories (related with has Accessory, if you will), such as the tongs, and it is used for preparing (/barbecuing/cooking/frying) food (/meals/dinners).
Then the source of heating. The one-off construct (with the {…}) is relatively popular in conceptual data modelling when you know the set of values is ever only allowed to be that, like the days of the week. But in our open world of ontologies, more just might be added or removed. And, ontologically, coal, gas, and electricity are not individuals, so also that is incorrect. The other option, with heatedBy xsd:String, has its own set of problems, largely because data properties with their data types entail application implementation decisions that ought not to be in an ontology that is supposed to be usable across multiple applications (see Section 6.1 ‘attributions’ for a longer explanation). It can be addressed by granting them their rightful status as classes in the OWL file and relating that to the braai.
This is not an exhaustive analysis of the badBBQ ontology, nor even close to a full list of the latest methods and techniques for good ontology development, but I hope I’ve illustrated my point about not relying on just CQs as evaluation of your ontology. Sample changes made to the badBBQ are included in the improvedBBQ OWL file. Here’s snapshot of the differences in the basic metrics (on the left). There’s room for another round of improvements, but I’ll leave that for later.
All this was not to say that competency questions are useless. They are not. They can be very useful to demarcate the scope of the ontology’s content, to keep on track with that since it’s easy to go astray from the intended scope once you begin or be subjected to scope creep, and to check whether at least the minimum content is in there somehow (and if not, why not). It’s the easy thing to check compared to the methods, techniques, and theory about good, sub-optimal, and bad ways of representing something. But such relative ease with CQs, perhaps unfortunately, does not mean it suffices to obtain a ‘good quality’ stamp of approval. Why the plethora of methods, techniques, theories, and tools aren’t used as often as they should, is a question I’d like to know the answer to, and may be a topic for another time.
[2] Keet, C.M., Suárez-Figueroa, M.C., Poveda-Villalón, M. Pitfalls in Ontologies and TIPS to Prevent Them. Knowledge Discovery, Knowledge Engineering and Knowledge Management: IC3K 2013 Selected Papers. A. Fred et al. (Eds.). Springer CCIS vol. 454, pp. 115-131, 2015. preprint
[3] Duque-Ramos, A. et al. OQuaRE: A SQuaRE-based approach for evaluating the quality of ontologies. Journal of research and practice in information technology, 2011, 43(2): 159-176
At some point in time, this COVID-19 pandemic will be over. Each time that thought crossed my mind, there was that little homunculus in my head whispering: but do you know the criteria for when it can be declared ‘over’? I tried to push that idea away by deferring it to a ‘whenever the WHO says it’s over’, but the thought kept nagging. Surely there would be a clear set of criteria lying on the shelf awaiting to be ticked off? Now, with the omicron peak well past us here in South Africa, and with comparatively little harm done in that fourth wave, there’s more talk publicly of perhaps having that end in sight – and thus also needing to know what the decisive factors are for calling it an end.
Then there are the anti-vaxxers. I know a few of them as well. One raged on with the argument that ‘they’ (the baddies in the governments in multiple countries) count the death toll entirely unfairly: “flu deaths count per season in a year, but for covid they keep adding up to the same counter from 2020 to make the death toll look much worse!! Trying to exaggerate the severity!” My response? Duh, well, yes they do count from early 2020, because a pandemic is one event and you count per event! Since the COVID-19 pandemic is a pandemic that is an event, we count from the start until the end – whenever that end is. It hadn’t even crossed my mind that someone wouldn’t count per event but, rather, wanted to chop up an event to pretend it would be smaller than it actually is.
So I did a little digging after all. What is the definition of a pandemic? What are its characteristics? Ontologically, what is that notion of ‘pandemic’, be it according to the analytic philosophers, ontologists, or modellers, or how it may be aligned to some of the foundational ontologies used in ontology engineering? From that, we then should be able to determine when all this COVID-19 has become a ‘is not a pandemic’ (whatever it may be classified into after the pandemic is over).
I could not find any works from the philosophers and theory-focussed ontologists that would have done the work for me already. (If there is and I missed it, please let me know.) Then, to start: what about definitions? There are some, like the recently updated one from dictionary.com where they tried to explain it from a language perspective, and lots of debate and misunderstandings in the debate about defining and describing a pandemic [1]. The WHO has descriptions, but not a clear definition, and pandemic phases. Formulations of definitions elsewhere vary slightly as well, except for the lowest common denominator: it’s a large epidemic.
Ontologically, that is an entirely unsatisfying answer. What is ‘large’? Some, like the CDC of the USA qualified it somewhat: it’s spread over the world or at least multiple regions and continents, and in those areas, it usually affects many people. The Australian Department of Health adds ‘new disease’ to it. Now we’re starting to get somewhere with inclusion of key properties of a pandemic. Kelly [2] adds another criterion to it, albeit focussed on influenza: besides worldwide/very wide area and affecting a large number of people, “almost simultaneous transmission takes place worldwide” and thus for a part of the world, there is an out-of-season influenza virus transmission.
Image credits: Miroslava Chrienova, taken from this page.
The best resource of all from an ontologists’ perspective, is a very clear, well-written, perspective article written by Morens, Folkers and Fauci – yes, that Fauci from the CDC – in the Journal of Infectious Diseases that, in their lack of wisdom, keeps the article paywalled (it somehow made it onto the webarchive with free access here anyhow). They’re experts and they trawled the literature to, if not define a pandemic, then at least describe it through trying to list the characteristics and the merits, or demerits, thereof. They are, in short, and with my annotation on what sort of attribute (/feature/characteristic, as loosely used term for now) it is:
Wide geographic extension; as aforementioned. That’s a scale or ‘fuzzy’ (imprecise in some way) feature, i.e., without a crisp cut-off point when ‘wide’ starts or ends.
Disease movement, i.e., there’s some transmission going on from place to place and that can be traced. That’s a yes/no characteristic.
High attack rates and explosiveness, i.e., lots of people affected in a short timespan. There’s no clear cut-off point on how fast the disease has to spread for counting as ‘fast spreading’, so a scale or fuzzy feature.
Minimal population immunity; while immunity is a “relative concept” (i.e., you have it to a degree), it’s a clear notion for a population when that exists or not; e.g., it certainly wasn’t there when SARS-CoV-2 started spreading. It is agnostic about how that population immunity is obtained. This may sound like a yes/no feature, perhaps, but is fuzzy, because practically we may not know and there’s for sure a grey area thanks to possible cross-immunity (natural or vaccine-induced) and due to the extent of immune-evasion of the infectious agent.
Novelty; the term speaks for itself, and clearly is a yes/no feature as well. It seems to me like ‘novel’ implies ‘minimal population immunity’, but that may not be the case.
Infectiousness; it’s got to be infectious, and so excluding non-infectious things, like obesity and smoking. Clear yes/no.
Contagiousness; this may be from person to person or through some other medium (like water for cholera). Perhaps as an attribute with categorical values; e.g., human-to-human, human-animal intermediary (e.g., fleas, rats), and human-environment (notably: water).
Severity; while the authors note that it’s not typically included, historically, the term ‘pandemic’ has been applied more often for diseases that are severe or with high fatality rates (e.g., HIV/AIDS) than for milder ones. Fuzzy concept for which a scale could be used.
And, at the end of their conclusions, “In summary, simply defining a pandemic as a large epidemic may make ultimate sense in terms of comprehensibility and consistency. We also suggest that use of the term is best reserved for infectious diseases that share many of the same epidemiologic features discussed above” (p1020), largely for simplifying it to the public, but where scientists and public health officials would maintain their more precise consensus understanding of the complex scientific concept.
Those imprecise/fuzzy properties and lack of clarity of cut-off points bug the epidemiologists, because they lead to different outcomes of their prediction models. From my ontologist viewpoint, however, we’re getting somewhere with these properties: SARS-CoV-2, at least early in 2020 when the pandemic was declared, ticked all those eight boxes and so any reasoner would classify the disease it causes, COVID-19, as a pandemic. Now, in early 2022 with/after the omicron variant of concern? Of those eight properties, numbers 4 and 8 much less so, and number 5 is the million-dollar-question two years into the pandemic. Either way, considering all those properties of a pandemic that have passed the revue here so far, calling an end to the pandemic is not as trivial is it initially may have sounded like. WHO’s “post pandemic period” phase refers to “levels seen for seasonal influenza in most countries with adequate surveillance”. That is a clear specification operationally.
Ontologically, if we were to take these eight properties at face value, the next question then is: are all eight of them combined the necessary and sufficient conditions, or are some of them ‘more essential’ for calling it a pandemic, and the other ones would then be optional features? Etymologically, the pan in pandemic means ‘all’, so then as long as it rages across the world, it would remain a pandemic?
Now that things get ontologically more interesting, the ontological status. Informally, an epidemic is an occurrence (read: instance/individual entity) of an infectious disease at a particular time (read: an unspecified duration of time, not an instant) and that affects some community (be that a community of humans, chicken, or whatever other organisms that live in a community), and pandemic, as a minimum, extends the region that it affects and amount of organisms infected, and then some of those other features listed above.
A pandemic is in the same subject domain as an infectious disease, and so we can consult the OBO Foundry and see what they did, or first start with just the main BFO categories for a general sense of what it would align to. With our BFO Classifier, I get as far as process:
As to the last (optional) question: could one argue that a pandemic is a collection of disjoint part-processes? Not if the part-processes all have to be instances of different types of processes. The other loose end is that BFO’s processes need not have an end, but pandemics do. For now, what’s the most relevant is that the pandemic is distinctly in the occurrent branch of BFO, and occurrents have temporal parts.
Digging further into the OBO Foundry, they indeed did quite some work on infectious diseases and COVID-19 already [4], and following the trail from their Figure 1 (see below): disposition is a realizable entity is a specifically dependentcontinuant is a continuant; infectious disease course is a disease course is a process is an occurrent; and “realizable entity comes to be realized in the course of the process”.
Source: Figure 1 of [4].
In that approach, COVID-19 is the infectious disease being realised in the pandemic we’re in at the moment, with multiple infectious disease courses in humans and a few other animals. But where does that leave us with pandemic? Inspecting the Infectious Disease Ontology (IDO) since the article does not give a definition, infectious disease epidemic and infectious disease pandemic are siblings of infectious disease course, where disease course is described as “Totality of all processes through which a given disease instance is realized.” (presumably the totality of all processes in one human where there’s an instance of, say, COVID-19). Infectious disease pandemic is an atomic class with no properties or formal definitions, but there’s an annotation with a definition. Nice try; won’t work.
What’s the problem? There are three. The first, and key, problem is that pandemic is stated to be a collection of epidemics, but i) collections of individual things (collectives, aggregates) are categorically different kind of entities than individual things, and ii) epidemic and pandemic are not categorically different things. Not just that, there’s a fiat boundary (along a continuum, really) between an epidemic evolving into becoming a pandemic and then subsiding into separate epidemics. A comparatively minor, or at least secondary, issue is how to determine the boundary of one epidemic from another to be able to construct a collective, since, more fundamentally: what are the respective identities of those co-occurring epidemics? One can’t get collections of things we can’t quite identify. For instance, is it one epidemic in two places that it jumped to, or do they count as two then, and what when two separate ones touch and presumably merge to become one large one? The third issue, and also minor for the current scope, is the definition for epidemic in the ontology’s annotation field, talking of “statistically significant increase in the infectious disease incidence” as determiner, but actually it’s based on a threshold.
Let’s try DOLCE as foundational ontology and see what we get there. With the DOLCE Decision Diagram [5], pandemic ends up as: Is [pandemic] something that is happening or occurring? Yes (perdurant – alike BFO’s occurrent). Are you able to be present or participate in [a pandemic]? Yes (event). Is [a pandemic] atomic, i.e., has no subdivisions of it and has a definite end point? No (accomplishment). Not the greatest word choice to say that a pandemic is an accomplishment – almost right up there with the DOLCE developers’ example that death is an achievement – but it sure is an accomplishment from the perspective of the infectious agent. The nice thing of dolce:accomplishment over bfo:process is that it entails there’s a limited duration to it (DOLCE also has process that also can go on and on and on).
The last question in both decision diagrams made me pause. The instances of COVID-19 going around could possibly be going around after the pandemic is over, uninterrupted in the sense that there is no time interval where no-one is infected with SARS-CoV-2, or it could be interrupted with later flare-ups if it’s still SARS-CoV-2 and not substantially different, but the latter is a grey area (is it a flare-up or a COVID-2xxx?). The latter is not our problem now. The former would not be in contradiction with pandemic as accomplishment, because COVID-19-the-pandemic and COVID-19-the-disease are two different things. (How those two relate can be a separate story.)
To recap, we have pandemic as an occurrent/perdurant entity unfolding in time and, depending on one’s foundational ontology, something along the line of accomplishment. For an epidemic to be classified as a pandemic, there are a varying number of features that aren’t all crisp and for which the fuzzy boundaries haven’t been set.
To sketch this diagrammatically (hence, informally), it would look something like this:
where the clocks and the DEX and DEV arrows are borrowed from the TREND temporal conceptual data modelling language [6]: Epidemic and Pandemic are temporal entities, DEX (+dashed arrow) verbalised is “An epidemic may also become a pandemic” and DEV (+solid arrow): “Each pandemic must evolve to epidemic ceasing to be a pandemic” (hiding the logic at the back-end).
It isn’t a full answer as to what a pandemic is ontologically – hence, the title of the blog post still has that question mark – but we can already clear up the two issues from the introduction of this post, as follows.
Consequences
We already saw that with any definition, description, and list of properties proposed, there is no unambiguous and certain definite endpoint to a pandemic that can be deterministically computed. Well, other than the extremes of either 100% population immunity or the affected species is extinct such that there is no single instance of a disease course (in casu, of COVID-19) either way. Several measured values of the scales for the fuzzy variables will go down and immunity increase (further) as the pandemic unfolds, and then the pandemic phase is over eventually. Since there are no thresholds defined, there likely will be people who are forever disagreeing on when it can be called over. That is inherent in the current state of defining what a pandemic is. Perhaps it now also makes you appreciate the somewhat weak operational statement of the WHO post-pandemic period phase – specifying anything better is fraught with difficulties to date and unlikely to ever make everybody happy.
There’s that flawed argument of the anti-vaxxer to deal with still. Flu epidemics last about 10 weeks, on average [7]. They happen in the winter and in the northern hemisphere that may cross a New Year (although I can’t remember that has ever happened in all the years I’ve lived in Europe). And yet, they also count per epidemic and not per calendar year. School years run from September to July, which provides a different sort of year, and the flu epidemics there are typically reported as ‘flu season 2014/2015’, indicating just that. Because those epidemics are short-lived, you typical get only one of those in a year, and in-season only.
Contrast this with COVID-19: it’s been going round and round and round since late December 2019, with waves and lulls for all countries, regions, and continents, but never did it stop for a season in whole regions or continents. Most countries come close to a stop during a lull at some point between the waves; for South Africa, according to worldometers, the lowest 7-day moving average since the first wave in 2020 was 265 recorded infections per day, on 7 November 2021. Any out-of-season waves? Oh yes – beta came along in summer last year and it was awful; at least for this year’s summer we got a relatively harmless omicron. And it’s not just South Africa that has been having out-of-season spikes. Point is, the COVID-19 pandemic ‘accomplishment’ wasn’t over within the year – neither a calendar year nor a northern hemisphere school year – and so we keep counting with the same counter for as long as the event takes until the pandemic as event is over. There’s no nefarious plot of evil controlling scaremongering governments, just a ‘demic that takes a while longer than we’ve been used to until 2019.
In closing, it is, perhaps, not the last word on the ontological status of pandemic, but I hope the walkthrough provided a little bit of clarity in the meantime already.
[5] Keet, C.M., Khan, M.T., Ghidini, C. Ontology Authoring with FORZA. 22nd International Conference on Information and Knowledge Management (CIKM’13). ACM proceedings, pp569-578. 2013.
A photo of the city where it was supposed to take place: Leiden (NL) (Source: here)
It’s been a while since I looked in more detail into the life sciences and healthcare semantics-driven software ecosystems. The problems are largely the same, or more complex, with more technologies and standards to choose from that promise that this time it will be solved once and for all but where practitioners know it isn’t that easy. And lots of tooling for SARS-CoV-2 and COVID-19, of course. I’ll summarise and comment on a few presentations in the remainder of this post.
Keynotes
The first keynote speaker was Karin Verspoor from RMIT in Melbourne, Australia, who focussed her talk on their COVID-SEE tool [1], a Scientific Evidence Explorer for COVID-19 information that relies on advanced NLP and some semantics to help finding information, notably taking open questions where the sentence is analysed by PICO (population, intervention, comparator, outcome) or part thereof, and using UMLS and MetaMap to help find more connections. In contrast to a well-known domain with well-known terminology to formulate very specific queries over academic literature, that was (and still is) not so for COVID-19. Their “NLP+” approach helped to get better search results.
The second keynote was by Martina Summer-Kutmon from Maastricht University, the Netherlands, who focussed on metabolic pathways and computation and is involved in WikiPathways. With pretty pictures, like the COVID-19 Disease map that culminated from a lot of effort by many research communities with lots of online data resources [2]; see also the WikiPathways one for covid, where the work had commenced in February 2020 already. She also came to the idea that there’s a lot of semantics embedded in the varied pathway diagrams. They collected 64643 diagrams from the literature of the past 25 years, analysed them with ML, OCR, and manual curation, and managed to find gaps between information in those diagrams and the databases [3]. It reminded me of my own observations and work on that with DiDOn, on how to get information from such diagrams into an ontology automatically [4]. There’s clearly still lots more work to do, but substantive advances surely have been made over the past 10 years since I looked into it.
Then there were Mirjam van Reisen from Leiden UMC, the Netherlands, and Francisca Oladipo from the Federal University of Lokoja, Nigeria, who presented the VODAN-Africa project that tries to get Africa to buy into FAIR data, especially for COVID-19 health monitoring within this particular project, but also more generally to try to get Africans to share data fairly. Their software architecture with tooling is open source. Apart from, perhaps, South Africa, the disease burden picture for, and due to, COVID-19, is not at all clear in Africa, but ideally would be. Let me illustrate this: the world-wide trackers say there are some 3.5mln infections and 90000+ COVID-19 deaths in South Africa to date, and from far away, you might take this at face value. But we know from SA’s data at the SAMRC that deaths are about three times as much; that only about 10% of the COVID-19-positives are detected by the diagnostics tests—the rest doesn’t get tested [asymptomatic, the hassle, cost, etc.]; and that about 70-80% of the population already had it at least once (that amounts to about 45mln infected, not the 3.5mln recorded), among other things that have been pieced together from multiple credible sources. There are lots of issues with ‘sharing’ data for free with The North, but then not getting the know-how with algorithms and outcomes etc back (a key search term for that debate has become digital colonialism), so there’s some increased hesitancy. The VODAN project tries to contribute to addressing the underlying issues, starting with FAIR and the GDPR as basis.
The last keynote at the end of the conference was by Amit Shet, with the University of South Carolina, USA, whose talk focussed on how to get to augmented personalised health care systems, with as one of the cases being asthma. Big Data augmented with Smart Data, mainly, combining multiple techniques. Ontologies, knowledge graphs, sensor data, clinical data, machine learning, Bayesian networks, chatbots and so on—you name it, somewhere it’s used in the systems.
Papers
Reporting on the papers isn’t as easy and reliable as it used to be. Once upon a time, the papers were available online beforehand, so I could come prepared. Now it was a case of ‘rock up and listen’ and there’s no access to the papers yet to look up more details to check my notes and pad them. I’m assuming the papers will be online accessible soon (CEUR-WS again presumably). So, aside from our own paper, described further below, all of the following is based on notes, presentation screenshots, and any Q&A on Discord.
Ruduan Plug elaborated on the FAIR & GDPR and querying over integrated data within that above-mentioned VODAN-Africa project [5]. He also noted that South Africa’s PoPIA is stricter than the GDPR. I’m suspecting that is due to the cross-border restrictions on the flow of data that the GDPR won’t have. (PoPIA is based on the GDPR principles, btw).
Deepak Sharma talked about FHIR with RDF and JSON-LD and ShEx and validation, which also related to the tutorial from the preceding day. The threesome Mercedes Arguello-Casteleiro, Chloe Henson, and Nava Maroto presented a comparison of MetaMap vs BERT in the context of covid [6], which I have to leave here with a cliff-hanger, because I didn’t manage to make a note of which one won because I had to go to a meeting that we were already starting later because of my conference attendance. My bet would be on the semantics (those deep learning models probably need more reliable data than there is available to date).
Besides papers related to scientific research into all things covid, another recurring topic was FAIR data—whether it’s findable, accessible, interoperable, and reusable. Fuqi Xu and collaborators assessed 11 features for FAIR vocabularies in practice, and how to use them properly. Some noteworthy observations were that comparing a FAIR level makes more sense before-and-after changing a single resource compared to pitting different vocabularies against each other, “FAIR enough” can be enough (cf. demanding 100% compliance) [7], and a FAIR vocabulary does not imply that it is also a good quality vocabulary. Arriving at the topic of quality, César Bernabé presented an analysis on the use of foundational ontologies in bioinformatics by means of a systematic literature mapping. It showed that they’re used in a range of activities of ontology engineering, there’s not enough empirical analysis of the pros and cons of using one, and, for the numbers game: 33 of the ontologies described in the selected literature used BFO, 16 DOLCE, 7 GFO, and 1 SUMO [8]. What to do next with these insights remains to be seen.
Last, but not least—to try to keep the blog post at a sort of just about readable length—our paper, among the 15 that were accepted. Frances Gillis-Webber, a PhD student I supervise, did most of the work surveying OWL Ontologies in BioPortal on whether, and if so how, they take into account the notion of multilingualism in some way. TL;DR: they barely do [9]. Even when they do, it’s just with labels rather than any of the language models, be they the ontolex-lemon from the W3C community group or another, and if so, mainly French and German.
Source: [9]
Does it matter? It depends on what your aims are. We use mainly the motivation of ontology verbalisation and electronic health records with SNOMED CT and patient discharge note generation, which ideally also would happen for ‘non-English’. Another use case scenario, indicated by one of the participants, Marco Roos, was that the bio-ontologies—not just health care ones—could use it as well, especially in the case of rare diseases, where the patients are more involved and up-to-date with the science, and thus where science communication plays a larger role. One could argue the same way for the science about SARS-CoV-2 and COVID-19, and thus that also the related bio-ontologies can do with coordinated multilingualism so that it may assist in better communication with the public. There are lots of opportunities for follow-up work here as well.
Other
There were also posters where we could hang out in gathertown, and more data and ontologies for a range of topics, such as protein sequences, patient data, pharmacovigilance, food and agriculture, bioschemas, and more covid stuff (like Wikidata on COVID-19, to name yet one more such resource). Put differently: the science can’t do without the semantic-driven tools, from sharing data, to searching data, to integrating data, and analysis to develop the theory figuring out all its workings.
The conference was supposed to be mainly in person, but then on 18 Dec, the Dutch government threw a curveball and imposed a relatively hard lockdown prohibiting all in-person events effective until, would you believe, 14 Jan—one day after the end of the event. This caused extra work with last-minute changes to the local organisation, but in the end it all worked out online. Hereby thanks to the organising committee to make it work under the difficult circumstances!
[5] Ruduan Plug, Yan Liang, Mariam Basajja, Aliya Aktau, Putu Jati, Samson Amare, Getu Taye, Mouhamad Mpezamihigo, Francisca Oladipo and Mirjam van Reisen: FAIR and GDPR Compliant Population Health Data Generation, Processing and Analytics. SWAT4HCLS 2022. online/Leiden, the Netherlands, 10-13 January 2022.
[6] Mercedes Arguello-Casteleiro, Chloe Henson, Nava Maroto, Saihong Li, Julio Des-Diz, Maria Jesus Fernandez-Prieto, Simon Peters, Timothy Furmston, Carlos Sevillano-Torrado, Diego Maseda-Fernandez, Manoj Kulshrestha, John Keane, Robert Stevens and Chris Wroe, MetaMap versus BERT models with explainable active learning: ontology-based experiments with prior knowledge for COVID-19. SWAT4HCLS 2022. online/Leiden, the Netherlands, 10-13 January 2022.
[7] Fuqi Xu, Nick Juty, Carole Goble, Simon Jupp, Helen Parkinson and Mélanie Courtot, Features of a FAIR vocabulary. SWAT4HCLS 2022. online/Leiden, the Netherlands, 10-13 January 2022.
[8] César Bernabé, Núria Queralt-Rosinach, Vitor Souza, Luiz Santos, Annika Jacobsen, Barend Mons and Marco Roos, The use of Foundational Ontologies in Bioinformatics. SWAT4HCLS 2022. online/Leiden, the Netherlands, 10-13 January 2022.
How to align your domain ontology to a foundational ontology? It’s a well-known question, and one that I’ve looked into before as well. In some of that earlier work, we used DOLCE to align one’s ontology to. We devised the DOLCE decision diagram as part of the FORZA method to assist with the alignment process and implemented that in the MoKI ontology development tool [1]. MoKI is no more, but the theory and the algorithm’s design approach still stand. Instead of re-implementing it as a Protégé plugin and have it go defunct in a few years again (due to incompatible version upgrades, say), it sounded like more fun to design one for BFO and make a stand-alone tool out of it. And that design and the evaluation thereof is precisely what two of my ontology engineering course students—Chiadika Emeruem and Steve Wang—did for their mini-project of the course. That was then finalised and implemented in a tool for general use as part of the DOT4D project extension for my (award-winning) OE textbook afterward.
More precisely, as first part, there’s a diagram specifically for BFO – well, for one of its 2.0-ish versions in existence at least. Deciding on which version to use and what would be good questions was not as trivial as it may sound. While the questions seem to work (as evaluated with several ontologies), it might still be of use to set up an experiment to assess usability from a modeller’s viewpoint.
BFO ‘decision diagram’ to assist trying to align one’s class of a domain or core ontology to BFO (click to enlarge, or navigate to the user guide at https://bfo-classifier.github.io/)
Be this as it may, this decision diagram was incorporated into the tool that wraps around it with a nice interface with user guidance and feedback, and it has the option to load an ontology and save the alignment into the ontology (along with BFO). The decision tree itself is stored as a separate XML file so that it easily can be replaced with any update thereto, be it to reflect changes in question formulation or to adjust it to some later version of BFO. The stand-alone tool is a jar file that can be downloaded from the GitHub repo, and the repo also has the source code that may be used/adapted (i.e., has an open source licence). There’s also a user guide with explanations and screenshots. Here’s another screenshot of the tool in action:
Example of the BFO classifier in use, trying to align CODO’s ‘Disease’ to BFO, the trail of questions answered to get to ‘Disposition’, and the subsumption axiom that can be added to the ontology.
If you have any questions, please feel free to contact either of us.
References
[1] Keet, C.M., Khan, M.T., Ghidini, C. Ontology Authoring with FORZA. 22nd ACM International Conference on Information and Knowledge Management (CIKM’13). ACM proceedings, pp569-578. Oct. 27 – Nov. 1, 2013, San Francisco, USA.
With increasing student numbers, but not as much more funding for schools and universities, and the desire to automate certain tasks anyhow, there have been multiple efforts to generate and mark educational exercises automatically. There are a number of efforts for the relatively easy tasks, such as for learning a language, which range from the entry level with simple vocabulary exercises to advanced ones of automatically marking essays. I’ve dabbled in that area as well, mainly with 3rd-year capstone projects and 4th-year honours project student projects [1]. Then there’s one notch up with fact recall and concept meaning recall questions, and further steps up, such as generating multiple-choice questions (MCQs) with not just obviously wrong distractors but good distractors to make the question harder. There’s quite a bit of work done on generating those MCQs in theory and in tooling, notably [2,3,4,5]. As a recent review [6] also notes, however, there are still quite a few gaps. Among others, about generalisability of theory and systems – can you plug in any structured data or knowledge source to question templates – and the type of questions. Most of the research on ‘not-so-hard to generate and mark’ questions has been done for MCQs, but there are multiple of other types of questions that also should be doable to generate automatically, such as true/false, yes/no, and enumerations. For instance, with an axiom such as in a ontology or knowledge graph, a suitable question generation system may then generate “Does an impala live on land?” or “True or false: An impala lives on land.”, among other options.
We set out to make a start with tackling those sort of questions, for the type-level information from an ontology (cf. facts in the ABox or knowledge graph). The only work done there, when we started with it, was for the slick and fancy Inquire Biology [5], but which did not have their tech available for inspection and use, so we had to start from scratch. In particular, we wanted to find a way to be able to plug in any ontology into a system and generate those non-MCQ other types of educations questions (10 in total), where the questions generated are at least grammatically good and for which the answers also can be generated automatically, so that we get to automated marking as well.
Different types of questions and the answer they have to provide put different prerequisiteson the content of the ontology with certain types of axioms. We specified those for 10 types of educational questions.
Three strategies of question generation were devised, being ‘simple’ from the vocabulary and axioms and plug it into a template, guided by some more semantics in the ontology (a foundational ontology), and one that didn’t really care about either but rather took a natural language approach. Variants were added to cater for differences in naming and other variations, amounting to 75 question templates in total.
The human evaluation with questions generated from three ontologies showed that while the semantics-based one was slightly better than the baseline, the NLP-based one gave the best results on syntactic and semantic correctness of the sentences (according to the human evaluators).
It was tested with several ontologies in different domains, and the generalisability looks promising.
Graphical Abstract (made by Toky Raboanary)
To be honest to those getting their hopes up: there are some issues that cause it never to make it to the ‘100% fabulous!’ if one still wants to designs a system that should be able to take any ontology as input. A main culprit is naming of elements in the ontology, which varies widely across ontologies. There are several guidelines for how to name entities, such as using camel case or underscores, and those things easily can be coded into an algorithm, indeed, but developers don’t stick to them consistently or there’s an ontology import that uses another naming convention so that there likely will be a glitch in the generated sentences here or there. Or they name things within the context of the hierarchy where they put the class, but in the question it is out of that context and then looks weird or is even meaningless. I moaned about this before; e.g., ‘American’ as the name of the class that should have been named ‘American Pizza’ in the Pizza ontology. Or the word used for the name of the class can have different POS tags such that it makes the generated sentence hard to read; e.g., ‘stuff’ as a noun or a verb.
Ever wanted only to get the gist of the ontology rather than wading manually through thousands of axioms, or to extract only a section of an ontology for reuse? Then the NOMSA tool may provide the solution to your problem.
screenshot of NOMSA in action (deleting classes further than two levels down in the hierarchy in BFO)
There are quite a number of ways to create modules for a range of purposes [1]. We zoomed in on the notion of abstraction: how to remove all sorts of details and create a new ontology module of that. It’s a long-standing topic in computer science that returns every couple of years with another few tries. My first attempts date back to 2005 [2], which references modules & abstractions for conceptual models and logical theories to works published in the mid-1990s and, stretching the scope to granularity, to 1985, even. Those efforts, however, tend to halt at the theory stage or worked for one very specific scenario (e.g., clustering in ER diagrams). In this case, however, my former PhD student and now Senior Research at the CSIR, Zubeida Khan, went further and also devised the algorithms for five types of abstraction, implemented them for OWL ontologies, and evaluated them on various metrics.
The tool itself, NOMSA, was presented very briefly at the EKAW 2018 Posters & Demos session [3] and has supplementary material, such as the definitions and algorithms, a very short screencast and the source code. Five different ways of abstraction to generate ontology modules were implemented: i) removing participation constraints between classes (e.g., the ‘each X R at least one Y’ type of axioms), ii) removing vocabulary (e.g., remove all object properties to yield a bare taxonomy of classes), iii) keeping only a small number of levels in the hierarchy, iv) weightings based on how much some element is used (removing less-connected elements), and v) removing specific language profile features (e.g., qualified cardinality, object property characteristics).
In the meantime, we have added a categorisation of different ways of abstracting conceptual models and ontologies, a larger use case illustrating those five types of abstractions that were chosen for specification and implementation, and an evaluation to see how well the abstraction algorithms work on a set of published ontologies. It was all written up and polished in 2018. Then it took a while in the publication pipeline mixed with pandemic delays, but eventually it has emerged as a book chapter entitled Structuring abstraction to achieve ontology modularisation [4] in the book “Advanced Concepts, methods, and Applications in Semantic Computing” that was edited by Olawande Daramola and Thomas Moser, in January 2021.
Since I bought new video editing software for the ‘physically distanced learning’ that we’re in now at UCT, I decided to play a bit with the software’s features and record a more comprehensive screencast demo video. In the nearly 13 minutes, I illustrate NOMSA with four real ontologies, being the AWO tutorial ontology, BioTop top-domain ontology, BFO top-level ontology, and the Stuff core ontology. Here’s a screengrab from somewhere in the middle of the presentation, where I just automatically removed all 76 object properties from BioTop, with just one click of a button:
screengrab of the demo video
The embedded video (below) might keep it perhaps still readable with really good eyesight; else you can view it here in a separate tab.
The source code is available from Zubeida’s website (and I have a local copy as well). If you have any questions or suggestions, please feel free to contact either of us. Under the fair use clause, we also can share the book chapter that contains the details.
[4] Khan, Z.C., Keet, C.M. Structuring abstraction to achieve ontology modularisation. Advanced Concepts, methods, and Applications in Semantic Computing. Daramola O, Moser T (Eds.). IGI Global. 2021, 296p. DOI: 10.4018/978-1-7998-6697-8.ch004
As any modeller will know, there are pieces of information or knowledge that can be represented in different ways. For instance, representing ‘marriage’ as class or as a ‘married to’ relationship, adding ‘address’ as an attribute or a class in one’s model, and whether ‘employee’ will be positioned as a subclass of ‘person’ or as a role that ‘person’ plays. In some cases, there a good ontological arguments to represent it in one way or the other, in other cases, that’s less clear, and in yet other cases, efficiency is king so that the most compact way of representing it is favoured. This leads to different design decisions in ontologies, which hampers ontology reuse and alignment and affects other tasks, such as evaluating competency questions over the ontology and verbalising ontologies.
When such choices are made consistently throughout the ontology, one may consider this to be a modelling style or representation style. If one then knows which style an ontology is in, it would simplify use and reuse of the ontology. But what exactly is a representation style?
While examples are easy to come by, shedding light on that intuitive notion turned out to be harder than it looked like. My co-author Pablo Fillottrani and I tried to disentangle it nonetheless, by characterising the inherent features and the dimensions by which a style may differ. This resulted in 28 different traits for the 10 identified dimensions. For instance, the dimension “modular vs. monolithic” has three possible options: 1) ‘Monolithic’, where the ontology is stored in one file (no imports or mergers); 2) ‘Modular, external’, where at least one ontology is imported or merged, and it kept its URI (e.g., importing DOLCE into one’s domain ontology, not re-creating it there); 3) ‘Modular, internal’, where there’s at least one ontology import that’s based on having carved up the domain in the sense of decomposition of the domain (e.g., dividing up a domain into pizzas and drinks at pizzerias). Other dimensions include, among others, the granularity of relations (many of few), how the hierarchy looks like, and attributes/data properties.
We tried to “eat our own dogfood” and applied the dimensions and traits to a set of 30 ontologies. This showed that it is feasible to do, although we needed two rounds to get to that stage—after the first round of parallel annotation, it turned out we had interpreted a few traits differently, and needed to refine the number of traits and be more precise in their descriptions (which we did). Perhaps unsurprising, some tendencies were observed, and we could identify three easily recognisable types of ontologies because most ontologies had clearly one or the other trait and similar values for sets of trait. Of course, there were also ontologies that were inherently “mixed” in the sense of having applied different and conflicting design decisions within the same ontology, or even included two choices. Coding up the results, we generated two spider diagrams that visualise that difference. Here’s one:
Last week I participated in the Workshop on Logic, Algebra, and Category theory (LAC2018) (and their applications in computer science), which was held 12-16 February at La Trobe University in Melbourne, Australia. It’s not fully in my research area, so there was lots of funstuff to learn. There were tutorials in the morning and talks in the afternoon, and, of course, networking and collaborations over lunch and in the evenings.
I finally learned some (hardcore) foundations of institutions that underpins the OMG-standardisedDistributed Ontology, Model, and Specification Language DOL, whose standard we used in the (award-winning) KCAP17 paper. It concerns the mathematical foundations to handle different languages in one overarching framework. That framework takes care of the ‘repetitive stuff’—like all languages dealing with sentences, signatures, models, satisfaction etc.—in one fell swoop instead of repeating that for each language (logic). The 5-day tutorial was given by Andrzej Tarlecki from the University of Warsaw (slides).
Oliver Kutz, from the Free University of Bozen-Bolzano, presented our K-CAP paper as part of his DOL tutorial (slides), as well as some more practical motivations for and requirements that went into DOL, or: why ontology engineers need DOL to solve some of the problems.
The afternoons had two presentations each. The ones of most interest to me included, among others, CSP by Michael Jackson; José Fiadeiro’s fun flexible modal logic for specifying actor networks for, e.g., robots and security breaches (that looks hopeless for implementations, but that as an aside); Ionuț Țuțu’s presentation on model transformations focusing on the maths foundations (cf the boxes-and-lines in, say, Eclipse); and Adrian Rodriguez’s program analysis with Maude (slides). My own presentation was about ontological and logical foundations for interoperability among the main conceptual data modelling languages (slides). They covered some of the outcomes from the bilateral project with Pablo Fillottrani and some new results obtained afterward.
Last, but not least, emeritus Prof Jennifer Seberry gave a presentation about a topic we probably all should have known about: Hadamard matrices and transformations, which appear to be used widely in, among others, error correction, cryptography, spectroscopy and NMR, data encryption, and compression algorithms such as MPEG-4.
Lots of thanks go to Daniel Găină for taking care of most of the organization of the successful event. (and thanks to the generous funders, which made it possible for all of us to fly over to Australia and stay for the week 🙂 ). My many pages of notes will keep me occupied for a while!
in a ontology or knowledge graph, a suitable question generation system may then generate “Does an impala live on land?” or “True or false: An impala lives on land.”, among other options.
Keet blog 
