Design rationale and overview of the African Wildlife tutorial ontologies

(update 30-7-2020: more details are described in the journal article published in the Journal of Biomedical Semantics)

There are several tutorial ontologies, which typically focus on illustrating one or two aspects of ontology development, notably language features and automated reasoning. This may suffice for one’s aims, but for an ontology engineering course, one would need to be able to illustrate a myriad of development factors and devise exercises for a wider range of tasks of ontology development. For instance, to illustrate the use of ontology design patterns, competency questions, foundational ontologies, and science-based modelling practices, neither of which is addressed easily by the popular tutorial ontologies (notably: wine and pizza), perhaps because they predate most of the advances made in ontology engineering research. Also, I have noticed that my students replicate examples from the exercises they carry out and from inspecting popular and easy-to-find ontologies. Marking the practical assignments, I got to see sandwich and ice cream and burger ontologies with toppings and value partitions, and software and mobile phone ontologies where laptop models are instances rather than classes. Not providing good and versatile examples holistically, causes the propagation of sub-optimal ontology development at least in the exercises, which then also may affect negatively the development of an operational domain ontology that the graduates may have to develop later on.

I’ve been exploring alternatives and variants over the past 11 years in the ontology engineering courses that I have taught yearly to about 8-40 students/year. In an attempt to systematise and possibly generalise from that, I’ve identified 22 requirements that contribute to a good tutorial ontology, which concern the suitability of the subject domain (7 factors), the ease of demonstrating logics and reasoning tasks (7), and assistance with demonstrating engineering aspects (8). Its details are described in a technical report [1]. I don’t claim that it’s an exhaustive list, but that it is one that may help someone to develop their own tutorial ontology in a fun or interesting topic if they so wish—after all, not everyone is interested in pizzas, wines, African wildlife, pets, shirts, a small university, or Robert Stevens’ family.

I’ve tried out a variety of extant tutorial ontologies as well as a range of versions of the African Wildlife Ontology (AWO) over the years (early experiences), eventually settling for a set of 14 versions, all the way from the example from the Primer [2] to DOLCE- and BFO-aligned to translated in several languages, and some with possible answers to some of the exercises. A graphical rendering of the main classes and relations is shown in the following figure:

The versions of the AWO are summarised in the following table, which is also mentioned as annotation in the OWL files.


The AWO meets a majority of the 22 requirements, is mature by now, and it has been used yearly in an ontology engineering course or tutorial since 2010. Also, it is links up with my ontology engineering textbook with relevant examples and exercises. The AWO provides a wide range of options concerning examples and exercises for ontology engineering well beyond illustrating only logic features and automated reasoning. For instance, it assists in demonstrating tasks about ontology quality, such as alignment to a foundational ontology and satisfying competency questions, versioning, and multilingual ontologies. For instance, it is easier to demonstrate alignment of a class Animal to DOLCE’s (Non-Agentive) Physical Object than, say, debating what Algorithm aligns with or descend into political debates on the gender binary or what constitutes a family. One can use the height or the colours of the plants and animals to discuss how to model attributes as qualities or dependent entities cf. OWL’s data properties or an artificial ValuePartition. Declare, say, de individual lion simba as an instance of Lion, rather than the confusion regarding grape varieties. Use intuitively obvious disjointness between animals and plants, and subsequently easy catches on sensitising modellers to the far-reaching effects of declaring domain and range axioms by first asserting that animals eat animals, and then adding that carnivorous plants eat insects. In addition, it links up easily to topics for ontology integration activities, such as with biodiversity data, wildlife trade, and tourism to create, e.g., an OBDA system with freely available data (e.g., taken from here) or an ontology-enhanced website for an organisation that offers environmentally sustainable safaris. More examples of broad usage options are described in section 2.3 in the tech report.

The AWO is freely available under a CC-BY licence through the textbook’s webpage at in this folder. A more comprehensive description of the requirements, design, and content is described in a technical report [1] for the time being.



[1] Keet, CM. The African Wildlife Ontology tutorial ontologies: requirements, design, and content. Technical Report 1905.09519. 23 May 2019.

[2] Antoniou, G., van Harmelen, F. A Semantic Web Primer. MIT Press, USA. 2003.


MOOCs, computer-based teaching aids, and taking notes

This post initially started out to be directed toward the current COMP314 Theory of Computation students at UKZN, who, like last year, are coming to terms not only with the subject domain, but also with the fact that I write the lecture notes on the blackboard (which is green, btw). The post eneded up containing some general reflections on the use/non-use of computer-based teaching aids and, in extension, the Massive Open Online Courses (MOOCs), with illustrations taken mainly from the ToC course.

First a note to the students: I am aware most of you don’t really like taking notes during the lectures and quite a few still do not do so—despite that you know that you’re served a summary of the bulky textbook, saving you to summarise it. But those who do make notes, or at least rewrite the notes from someone else or fill up the gaps in their own notes, go much more quickly through the exercises than those who do not. For instance, I occasionally gave as an exercise an example that was done in class on the board already. The diligent note-takers’ response is along the line of “yeah, that was trivial, and, by the way, we did that already in class, here’s the solution. Give me a real challenge!” compared to starting the ‘exercise’ from scratch by those who did not take notes, and who do not even recollect we did it in class. In the end (and by observation, not scientific rigour of the double blind experiment), the former group completes more, and more advanced, exercises in the same or less amount of time. Maybe I should shout that from the rooftops.

Taking notes means you listen, read, and write, therewith processing the knowledge that I’m trying to get from my brain into yours. Doing something actively, versus passively listening (or, worse, letting it go into one ear and immediately release it through the other, or dozing off), makes you think about the material at least once, which then saves time later on because you’ll remember some, if not all, of it. In addition, imagine how fast I could go through the material if I would not have to take the time to write it on the board, but just click the down arrow, and therewith having the opportunity to cover even more material than I already do whereas you would hardly have time to think about the matter at all, let alone ask questions. Besides, note taking is a good exercise in general, because there often won’t be any handouts prepared for you once you’re out there in industry, so you might as well practice for those situations now. (See also Mole’s column on why to chalk it upon the blackboard.)

Nonetheless, unlike, say, 15-20 years ago when we did not know any better than reading the material beforehand and taking notes during the lectures, in the present-day slides-era, quite a few students are not happy with the note-taking effort and ‘quality of the teaching aids provided’, as they are used to the slides in other courses. I distribute the slides, too, for a course like ontologies and knowledge bases, because there is no textbook (I even wrote a 150-pages long set of lecture notes), but for Theory of computation, we use a textbook (Hopcoft, Mottwani, and Ullman) that has the details at undergraduate level. Nor were last year’s students happy with the note-taking. Some students searched online back then, and I vividly remember one student sharing his opinion about that with me, unsolicited: “ma’am, we searched online for better material, but it’s all just as bad as yours! So we won’t hold it against you”. Well, thanks.

Closely related to that ‘searching around online’, however, are possible less pleasant side effects. I will mention two. First, last year some students looked up alternative explanations for the diagonalization language and some things being incomputable, having come across a version of the barbershop paradox. Strictly speaking, it is not an alternative explanation of the same thing; or: if you are going to use that, it is a wrong analogy. I’ll explain why in class in a few weeks when we’ll cover chapters 8 and 9 of HMU.

Second, this year I have seen some non-notes students watching a YouTube video on the pumping lemma and on CFGs during the exercises in the lab; such repeats take up extra time. The note-takers, on the other hand, flicked through their pages in a fraction of the time, hence, were ahead in the exercises simply because they had used the lecture time more effectively. (And I generously assume that what was presented in the YouTube video was correct, which need not be the case; see previous point).

This does not mean there is no room for improvement regarding teaching aids for Theory of Computing, which I’ve written about last year. This year I tried the AutoSim automata simulator and the CFG Grammar Editor again, which still does not have much uptake by the students, afaik, and I’m trying out Gradiance. The Gradiance system is an online MCQs question bank with explanations of the answers and hints in case the answer given was incorrect—i.e., turning the gained experience and knowledge about common conceptual mistakes into an educational feature)—and one can assign homework and grade assignments. The dedication to do the (optional) homework is dwindling since the 5th week into the semester, but the automatic grading and providing the option for self-study for the interested and/or determined-to-pass students are can be great (the system is almost cheat-proof, but not entirely).

To get to the point after some meandering: some types of computer-based teaching aids can be useful, just not all of them all of the time. True, computing looks at what can be automated, and what can be automated efficiently, and so I could apply that notion to everything—up to totally automated course offerings, which is the direction that the MOOCs are going. However, computing also concerns solving problems, and being able to recognise when a problem is best solved by computation, and when other solutions may be more appropriate. For instance, low pass rates may be considered to be a problem, but this does not imply that e-learning is the solution to that; non-determinism and epsilon-transitions are concepts that are apparently not easy to grasp, and the simulator is more illustrative than my coloured chalk trying to simulate a run on the blackboard; in the pre-CMS era, course admin was a chore and there were, perhaps, instances of ‘lost’ assignment or project submissions (though that did not happen when I was a student in the ‘90s), which Moodle alleviates and prevents, respectively. So, software can indeed solve some problems.

This brings me to the other end of the spectrum: the Massive Open Online Courses, or MOOCs; yes, there is even a MOOC for Theory of Computation, by Ullman himself. Which problems do they purport to solve, and do they? Despite reading a lot of pop-articles about it over the past year (see, e.g., the feature series from the Chronicle of Higher Education on MOOCs), it still is not clear to me with respect to the solutions for lecturing issues. One recurring argument in the flurry of news articles is that it is great [to/because it] give[s] the poor sods around the world some crumbs from the elite universities; well, the ‘poor’ with a good Internet connection and the money to pay for the data transfer, that is, not Joe and Joanne Soap in Chesterville, Soweto, etc., and the interested potential student has to have a good command of the English language as well.

Another recurring argument with the MOOCs is that one can learn from the best, and, en passant, implying, or even explicitly stating, that the MOOC lecturers are assumed to be better teachers by definition than anyone else. Sure, there are lecturers who teach stuff that is wrong, but how widespread is that, and what is the cause of that? If anyone has hard data about such claims, please let me know. For the sake of argument, let’s assume mistakes are widespread, and that it is because we, as non-elite university lecturers, are undereducated and incompetent teachers. Is a MOOC the solution? Or maybe giving lecturers the time to learn their material and prepare the lectures better, i.e., local capacity building? Not all of us are undereducated. People who have taught a course many times, do research in that field, and possibly even have written textbooks on the topic, tend to be better lecturers, because they generally have reflected more on the teaching and already have come across all possible conceptual mistakes the students make, can anticipate it, and therewith even prevent that from happening at least to a larger extent than a novice lecturer; those people are not all and only at Stanford, Harvard, and MIT.

Second, the MOOCs—for the time being, at least—are based on a push-mechanism of knowledge transfer, yet lecturing consists of much more than talking at the front of the classroom. There is interaction with questions and answers, there is context, and so on. For instance, regarding motivational context, to introduce the data mining in a database course with a story about the nearby Pick ‘n Pay at Westwood mall that the on-campus students go to, and, once signed up for their customer loyalty card, how PnP will find out you’re a student even if you did not say so on the application form. Or the problems with Johannesburg’s integrated services delivery management system as a real life example of data integration issues and the urgent need to have educated South African computer scientists to solve them. Or, given that UKZN’s CS student have had a lot of java programming in the first and second year, to show them the java language as a CFG in the theory of computation course. In addition, sometimes to the pleasant and sometimes frightened, surprise of my students, I actually do know about half of the roughly 70 registered students by name, and know who can be prodded into action only when an exercise is marked with ‘super hard’, who does not want to know the answer straight away but just a little hint to move on him/herself to find the solution, the determined to solve it on their own, the insecure who needs a bit of encouragement, and so on.

To have a MOOC suit you, you would have to have done the same prior courses—unless the MOOC is only an introduction to topic x—, you would not care about the absence of a few context/motivational stories, your learning style has to match with that of the MOOC, and you have to be very disciplined on your own. To name but a few potential hurdles in the positive light.

Further, there are the exercises and tests. There are some new tools for the automatic grading of exercises and assignments (Gradiance would fit here, I suppose, but not the regular textbook exercises), or semi-automatic with software+TA, and virtual ‘MOOC study groups’ are popping up in social media. If you don’t know any better, it is probably great. Like thinking pizza is tasty all around the world—until you experience how it tastes in Naples. Such online groups are not bad—I participated in it myself when I was studying at the Open University UK, and it is better than no contact with other students at all—but it does not compare with the face-to-face meetings with fellow students, where the lectures are discussed, notes compared and brushed up, exercises discussed and solved, peer-explanation happens, students motivating one another, and so on.

Overall, then, if MOOCs are going to become the standard, the world will be poorer for it. For sidelining competent lecturers, de-incentivising weaker lecturers from acting on their responsibility to brush up their knowledge and skills, de-contextualising and hamburgerising courses, impoverishing the academic learning environment by narrowing down education to a mere push-mechanism of knowledge transfer, and dehumanizing students into boring conformity (eenheidsworst in Dutch). Add to that mix the cultural imperialism, and we are well on our way to a ‘brave new world’.

In the meantime, participate in the lectures and process the information, and take notes. I don’t think MOOCs will kill the regular universities, but imagine if you really were the last generation to go to (or work at) a real university… Exploit the advantages that a face-to-face university offers you, and cherish it while it lasts!