# ChatGPT, deep learning and the like do not make ontologies (and the rest of AI) obsolete

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:

References

[Bertens22] Bertens, L. M. F. Modeling the art historical canon. Arts and Humanities in Higher Education, 2022, 21(3), 240-262.

[Pruvost22] Pruvost, Hervé and Olaf Enge-Rosenblatt. Using Ontologies for Knowledge-Based Monitoring of Building Energy Systems. Computing in Civil Engineering 2021. American Society of Civil Engineers, 2022, pp762-770.

[Roboanary22] Raboanary, T., Wang, S., Keet, C.M. Generating Answerable Questions from Ontologies for Educational Exercises. 15th Metadata and Semantics Research Conference (MTSR’21). Garoufallou, E., Ovalle-Perandones, M-A., Vlachidis, A (Eds.). 2022, Springer CCIS vol. 1537, 28-40.

[Reese23] Reese, J. et al. Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes. eBioMedicine, Volume 87, 104413, January 2023.

[Soergel22] Soergel, Dagobert, Olivia Helfer, Steven Lewis, Matthew Wysocki, David Mawer. Using Virtual Reality & Ontologies to Teach System Structure & Function: The Case of Introduction to Anatomy. 12th International conference on the Future of Education 2022. 2022/07/01

[Tan23] Tan, S.Z.K., Kir, H., Aevermann, B.D. et al. Brain Data Standards – A method for building data-driven cell-type ontologies. Scientific Data, 2023, 10, 50.

# Only answering competency questions is not enough to evaluate your ontology

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 physical object. 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.

References

[1] Keet, C.M. The African Wildlife Ontology tutorial ontologies. Journal of Biomedical Semantics, 2020, 11:4.

[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

[4] Poveda-Villalón, M., Gómez-Pérez, A., Suárez-Figueroa, M. C.. OOPS!(Ontology Pitfall Scanner!): An on-line tool for ontology evaluation. International Journal on Semantic Web and Information Systems, 2014, 10(2): 7-34.

[5] Guarino, N., Welty,C. An overview of OntoClean. In S. Staab and R. Studer (Eds.), Handbook on Ontologies, pp 201-220. Springer Verlag, 2009.

[6] Ferré, S., Rudolph, S. Advocatus diaboli exploratory enrichment of ontologies with negative constraints. In A ten Teije et al., editors, 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW’12), volume 7603 of LNAI, pages 42-56. Springer, 2012. Oct 8-12, Galway, Ireland.

[7] Keet, C.M. and Artale, A. Representing and Reasoning over a Taxonomy of Part-Whole Relations. Applied Ontology, 2008, 3(1-2): 91-110.

[8] Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A. WonderWeb Deliverable D18–Ontology library. WonderWeb. 2003.

[9] Smith, B., et al. Relations in biomedical ontologies. Genome biology, 2005, 6.5: 1-15.

[10] Keet, C.M., Fernández-Reyes, F.C., Morales-González, A. Representing mereotopological relations in OWL ontologies with OntoPartS. 9th Extended Semantic Web Conference (ESWC’12), Simperl et al. (eds.), 27-31 May 2012, Heraklion, Crete, Greece. Springer, LNCS 7295, 240-254.

# Progress on generating educational questions from ontologies

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 $impala \sqsubseteq \exists livesOn.land$ 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.

Initial explorations started in 2019 with an honours project to develop some basics and a baseline, which was then expanded upon. Meanwhile, we have some more designed, developed, and evaluated, which was written up in the paper “Generating Answerable Questions from Ontologies for Educational Exercises” [7] that has been accepted for publication and presentation at the 15th international conference on metadata and semantics research (MTSR’21) that will be held online next week.

In short:

• Different types of questions and the answer they have to provide put different prerequisites on 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.

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.

Be this as it may, overall, promising results were obtained and are being extended (more to follow). Some details can be found in the (CRC of the) paper and the algorithms and data are available from the GitHub repo. The first author of the paper, Toky Raboanary, recently made a short presentation video about the paper for the yearly Open Evening/Showcase, which was held virtually and that page is still online available.

References

[1] Gilbert, N., Keet, C.M. Automating question generation and marking of language learning exercises for isiZulu. 6th International Workshop on Controlled Natural language (CNL’18). Davis, B., Keet, C.M., Wyner, A. (Eds.). IOS Press, FAIA vol. 304, 31-40. Co. Kildare, Ireland, 27-28 August 2018.

[2] Alsubait, T., Parsia, B., Sattler, U. Ontology-based multiple choice question generation. KI – Kuenstliche Intelligenz, 2016, 30(2), 183-188.

[3] Rodriguez Rocha, O., Faron Zucker, C. Automatic generation of quizzes from dbpedia according to educational standards. In: The Third Educational Knowledge Management Workshop. pp. 1035-1041 (2018), Lyon, France. April 23 – 27, 2018.

[4] Vega-Gorgojo, G. Clover Quiz: A trivia game powered by DBpedia. Semantic Web Journal, 2019, 10(4), 779-793.

[5] Chaudhri, V., Cheng, B., Overholtzer, A., Roschelle, J., Spaulding, A., Clark, P., Greaves, M., Gunning, D. Inquire biology: A textbook that answers questions. AI Magazine, 2013, 34(3), 55-72.

[6] Kurdi, G., Leo, J., Parsia, B., Sattler, U., Al-Emari, S. A systematic review of automatic question generation for educational purposes. Int. J. Artif. Intell. Edu, 2020, 30(1), 121-204.

[7] Raboanary, T., Wang, S., Keet, C.M. Generating Answerable Questions from Ontologies for Educational Exercises. 15th Metadata and Semantics Research Conference (MTSR’21). 29 Nov – 3 Dec, Madrid, Spain / online. Springer CCIS (in print).

# Bias in ontologies?

Bias in models in the area of Machine Learning and Deep Learning are well known. They feature in the news regularly with catchy headlines and there are longer, more in-depth, reports as well, such as the Excavating AI by Crawford and Paglen and the book Weapons of Math Destruction by O’Neil (with many positive reviews). What about other types of ‘models’, like those that are not built in a data-driven bottom-up way from datasets that happen to lie around for the taking, but that are built by humans? Within Artificial Intelligence still, there are, notably, ontologies. I searched for papers about bias in ontologies, but could find only one vision paper with an anecdote for knowledge graphs [1], one attempt toward a framework but looking at FOAF only [2], which is stretching it a little for what passes as an ontology, and then stretching it even further, there’s an old one of mine on bias in relation to conceptual data models for databases [3].

We simply don’t have bias in ontologies? That sounds a bit optimistic since it’s pervasive elsewhere, and at least worthy of examination whether there is such notion as bias in ontologies and if so, what the sources of that may be. And, if one wants to dig deeper, since Ontology: what is bias anyhow? The popular media is much more liberal in the use of the term ‘bias’ than scientific literature and I’m not going to answer that last question here now. What I did do, is try to identify sources of bias in the context of ontologies and I took a relevant selection of Dimara et al’s list of 154 biases [4] (just like only a subset is relevant to their scope) to see whether they would apply to a set of existing ontologies in roughly the same domain.

The outcome of that exploratory analysis [5], in short, is: yes, there is such notion as bias in ontologies as well. First, I’ve identified 8 types of sources, described them, and illustrated them with hand-picked examples from extant ontologies. Second, I examined the three COVID-19 ontologies (CIDO, CODO, COVoc) on possible bias, and they exhibited different subsets indeed.

The sources can be philosophical, by purpose (commonly known as encoding bias), and ‘subject domain’ source, such as scientific theory, granularity, linguistic, social-cultural, political or religious, and economic motivations, and they may be explicit choices or implicit.

An example of an economic motivation is to (try to) categorise some disorder as a type of disease: there latter gets more resources for medicines, research, treatments and is more costly for insurers who’s rather keep it out of the terminology altogether. Or modifying the properties of a disease or disorder in the classification in the medical ontology so that more people will be categorised as having the disorder even when they don’t. It has happened (see paper for details). Terrorism ontologies can provide ample material for political views to creep in.

Besides the hand-picked examples, I did assess the three COVID-19 ontologies in more detail. Not because I wanted to pick on them—I actually think it’s laudable they tried in trying times—but because they were developed in the same timeframe by three different groups in relative isolation from each other. I looked at both the sources, which can be argued to be present and identified some from a selection of Dimara et al’s list, such as the “mere exposure/familiarity” bias and “false consensus” bias (see table below). How they are present, is also described in that same paper, entitled “An exploration into cognitive bias in ontologies”, which has recently been accepted at the workshop on Cognition And OntologieS V (CAOS’21), which is part of the Joint Ontology Workshops Episode VII at the Bolzano Summer of Knowledge.

Will it matter for automated reasoning when the ontologies are deployed in various information systems? For reasoning over the TBox only, perhaps not so much, or, at least, any inconsistencies that it would have caused should have been detected and discussed during the ontology development stage, rather.

Will it matter for, say, annotating data or literature etc? Some of it yes, for sure. For instance, COVoc has only ‘male’ in the vocabulary, not female (in line with a well-known issue in evidence-based medicine), so when it is used for the “scientific literature triage” they want to, then it’s going to be even harder to retrieve COVID-19 research papers in relation to women specifically. Similarly, when ontologies are used with data, such as for ontology-based data access, bias may have negative effects. Take as example CIDO’s optimism bias, where a ‘COVID-19 experimental drug in a clinical trial’ is a subclass of ‘COVID-19 drug’, and this ontology would be used for OBDA and data integration, as illustrated in the following use case scenario with actual data from the ClinicalTrials database and the FDA approved drugs database:

The data together with the OBDA-enabled reasoner will return ‘hydroxychloroquine’, which is incorrect and the error is due to the biased and erroneous class subsumption declared in the ontology, not the data source itself.

Some peculiarities of content in an ontology may not be due to an underlying bias, but merely a case of ‘ran out of time’ rather than an act of omission due to a bias, for instance. Or it may not be an honest mistake due to bias but a mistake because of some other reason, such as due to having clicked erroneously on a wrong button in the tool’s interface, say, or having misunderstood the modelling language’s features. Disentangling the notion of bias from attendant ontology quality issues is one of the possible avenues of future work. One also can have a go at those lists and mini-taxonomies of cognitive biases and make a better or more comprehensive one, or to try to harmonise the multitude of definitions of what bias is exactly. Methods and supporting software may also assist ontology developers more concretely further down the line. Or: there seems to be enough to do yet.

Lastly, I still hope that I’ll be allowed to present the paper in person at the CAOS workshop, but it’s increasingly looking less and less likely, as our third wave doesn’t seem to want to quiet down and Italy is putting up more hurdles. If not, I’ll try to make a fancy video presentation.

References

[1] K. Janowicz, B. Yan, B. Regalia, R. Zhu, G. Mai, Debiasing knowledge graphs: Why female presidents are not like female popes, in: M. van Erp, M. Atre, V. Lopez, K. Srinivas, C. Fortuna (Eds.), Proceeding of ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks, volume 2180 of CEUR-WS, 2017.

[2] D. L. Gomes, T. H. Bragato Barros, The bias in ontologies: An analysis of the FOAF ontology, in: M. Lykke, T. Svarre, M. Skov, D. Martínez-Ávila (Eds.), Proceedings of the Sixteenth International ISKO Conference, Ergon-Verlag, 2020, pp. 236 – 244.

[3] Keet, C.M. Dirty wars, databases, and indices. Peace & Conflict Review, 2009, 4(1):75-78.

[4] E. Dimara, S. Franconeri, C. Plaisant, A. Bezerianos, P. Dragicevic, A task-based taxonomy of cognitive biases for information visualization, IEEE Transactions on Visualization and Computer Graphics 26 (2020) 1413–1432.

[5] Keet, C.M. An exploration into cognitive bias in ontologies. Cognition And OntologieS (CAOS’21), part of JOWO’21, part of BoSK’21. 13-16 September 2021, Bolzano, Italy. (in print)

# Automatically simplifying an ontology with NOMSA

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.

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:

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.

References

[1] Khan, Z.C., Keet, C.M. An empirically-based framework for ontology modularization. Applied Ontology, 2015, 10(3-4):171-195.

[2] Keet, C.M. Using abstractions to facilitate management of large ORM models and ontologies. International Workshop on Object-Role Modeling (ORM’05). Cyprus, 3-4 November 2005. In: OTM Workshops 2005. Halpin, T., Meersman, R. (eds.), LNCS 3762. Berlin: Springer-Verlag, 2005. pp603-612.

[3] Khan, Z.C., Keet, C.M. NOMSA: Automated modularisation for abstraction modules. Proceedings of the EKAW 2018 Posters and Demonstrations Session (EKAW’18). CEUR-WS vol. 2262, pp13-16. 12-16 Nov. 2018, Nancy, France.

[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

# A requirements catalogue for ontology languages

If you could ‘mail order’ a language for representing ontologies or fancy knowledge graphs, what features would you want it to have? Or, from an artefact development viewpoint: what requirements would it have to meet? Perhaps it may not be a ‘Christmas wish list’ in these days, but a COVID-19 lockdown ‘keep dreaming’ one instead, although perhaps it may even be feasible to realise if you don’t ask for too much. Either way, answering this on the spot may not be easy, and possibly incomplete. Therefore, I have created a sample catalogue, based on the published list of requirements and goals for OWL and CL, and I added a few more. The possible requirements to choose from currently are loosely structured into six groups: expressiveness/constructs/modelling features; features of the language as a whole; usability by a computer; usability for modelling by humans; interaction with ‘outside’, i.e., other languages and systems; and ontological decisions. If you think the current draft catalogue should be extended, please leave a comment on this post or contact the author, and I’ll update accordingly.

Expressiveness/constructs/modelling features

E-1 Equipped with basic language elements: predicates (1, 2, n-ary), classes, roles, properties, data-types, individuals, … [select or add as appropriate].

E-2 Equipped with language features/constraints/constructs: domain/range axioms, equality (for classes, for individuals), cardinality constraints, transitivity, … [select or add as appropriate].

E-3 Sufficiently expressive to express various commonly used ‘syntactic sugarings’ for logical forms or commonly used patterns of logical sentences.

E-4 Such that any assumptions about logical relationships between different expressions can be expressed in the logic directly.

Features of the language as a whole

F-1 It has to cater for meta-data; e.g., author, release notes, release date, copyright, … [select or add as appropriate].

F-2 An ontology represented in the language may change over time and it should be possible to track that.

F-3 Provide a general-purpose syntax for communicating logical expressions.

F-4 Unambiguous, i.e., not needed to have negotiation about syntactic roles of symbols, or translations between syntactic roles.

F-5 Such that every name has the same logical meaning at every node of the network.

F-6 Such that it is possible to refer to a local universe of discourse (roughly: a module).

F-7 Such that it is possible to relate the ontology to other such universes of discourse.

F-8 Specified with a particular semantics.

F-9 Should not make arbitrary assumptions about semantics.

F-10 Cater for internationalization (e.g., language tags, additional language model).

F-11 Extendable (e.g., regarding adding more axioms to same ontology, add more vocabulary, and/or in the sense of importing other ontologies).

F-12 Balance expressivity and complexity (e.g., for scalable applications, for decidable automated reasoning tasks).

F-13 Have a query language for the ontology.

F-14 Declared with Closed World Assumption.

F-15 Declared with Open World Assumption.

F-16 Use Unique Name Assumption.

F-17 Do not use Unique Name Assumption.

F-18 Ability to modify the language with the language features.

F-19 Ability to plug in language feature extensions; e.g., ‘loading’ a module for a temporal extension.

Usability by computer

UC-1 Be an (identifiable) object on the Web.

UC-2 Be usable on the Web.

UC-3 Using URIs and URI references that should be usable as names in the language.

UC-4 Using URIs to give names to expressions and sets of expressions, in order to facilitate Web operations such as retrieval, importation and cross reference.

UC-5 Have a serialisation in [XML/JSON/…] syntax.

UC-6 Have symbol support for the syntax in LaTeX/…

UC-7 Such that the same entailments are supported, everywhere on the network of ontologies.

UC-8 Able to be used by tools that can do subsumption reasoning/taxonomic classification.

UC-9 Able to be used by tools that can detect inconsistency.

UC-10 Possible to read and write in the document with simple tools, such as a text editor.

UC-11 Unabiguous and simple grammar to ensure parsing documents as simple as possible.

Usability & modelling by humans

HU-1 Easy to use

HU-2 Have at least one compact, human-readable syntax defined which can be used to express the entire language

HU-3 Have at least one compact, human-readable syntax defined so that it can be easily typed up in emails

HU-4 Such that no agent should be able to limit the ability of another agent to refer to any entity or to make assertions about any entity

HU-5 Such that a modeller is free to invent new names and use them in published content.

HU-6 Have clearly definined syntactic sugar, such as a controlled natural language for authoring or rendering the ontology or an exhaustive diagramamtic notation

Interaction with outside

I-1 Shareable (e.g., on paper, on the computer, concurrent access)

I-2 Interoperable (with what?)

I-3 Compatible with existing standards (e.g., RDF, OWL, XML, URIs, Unicode)

I-4 Support an open networks of ontologies

I-5 Possible to import ontologies (theories, files)

I-6 Option ot declare inter-ontology assertions

Ontological decisions

O-1 3-Dimensionalist commitment, where entities are in space but one doesn’t care about time

O-2 3-Dimensionalist with a temporal extension

O-3 4-Dimensionalist commitment, where entities are in spacetime

O-4 Standard view of relations and relationships (there is an order in which the entities participare)

O-5 Positionalist relations and relationships (there’s no order, but entities play a role in the relation/relationship)

O-6 Have additional primitives, such as for subsumption, parthood, collective, stuff, sortal, anti-rigid entities, … [select or add as appropriate]

O-7 Statements are either true or false

O-8 Statements may vague or uncertain; e.g., fuzzy, rough, probabilistic [select as appropriate]

O-9 There should be a clear separation between natural language and ontology

O-10 Ontology and natural language are intertwined

That’s all, for now.

# 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 https://people.cs.uct.ac.za/~mkeet/OEbook/ in this folder. A more comprehensive description of the requirements, design, and content is described in a technical report [1] for the time being.

References

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

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

# DL notation plugin for Protégé 5.x

Once upon a time… the Protégé ontology development environment used Description Logic (DL) symbols and all was well—for some users at least. Then Manchester Syntax came along as the new kid on the block, using hearsay and opinion and some other authors’ preferences for an alternative rendering to the DL notation [1]. Subsequently, everyone who used Protégé was forced to deal with those new and untested keywords in the interface, like ‘some’ and ‘only’ and such, rather than the DL symbols. That had another unfortunate side-effect, being that it hampers internationalisation, for it jumbles up things rather awkwardly when your ontology vocabulary is not in English, like, say, “jirafa come only (oja or ramita)”. Even in the same English-as-first-language country, it turned out that under a controlled set-up, the DL axiom rendering in Protégé fared well in a fairly large sized experiment when compared to the Protégé interface with the sort of Manchester syntax with GUI [2], and also the OWL 2 RL rules rendering appear more positive in another (smaller) experiment [3]. Various HCI factors remain to be examined in more detail, though.

In the meantime, we didn’t fully reinstate the DL notation in Protégé in the way it was in Protégé v3.x from some 15 years ago, but with our new plugin, it will at least render the class expression in DL notation in the tool. This has the benefits that

1. the modeller will receive immediate feedback during the authoring stage regarding a notation that may be more familiar to at least a knowledge engineer or expert modeller;
2. it offers a natural language-independent rendering of the axioms with respect to the constructors, so that people may develop their ontology in their own language if they wish to do so, without being hampered by continuous code switching or the need for localisation; and
3. it also may ease the transition from theory (logics) to implementation for ontology engineering novices.

Whether it needs to be integrated further among more components of the tabs and views in Protégé or other ODEs, is also a question for HCI experts to answer. The code for the DL plugin is open source, so you could extend it if you wish to do so.

The plugin itself is a jar file that can simply be dragged into the plugin folder of a Protégé installation (5.x); see the github repo for details. To illustrate it briefly, after dragging the jar file into the plugin folder, open Protégé, and add it as a view:

Then when you add some new axioms or load an ontology, select a class, and it will render all the axioms in DL notation, as shown in the following two screenshots form different ontologies:

For the sake of illustration, here’s the giraffe that eats only leaves or twigs, in the Spanish version of the African Wildlife Ontology:

The first version of the tool was developed by Michael Harrison and Larry Liu as part of their mini-project for the ontology engineering course in 2017, and it was brushed up for presentation beyond that just now by Michael Harrison (meanwhile an MSc student a CS@UCT), which was supported by a DOT4D grant to improve my textbook on ontology engineering and accompanying educational resources. We haven’t examined all possible ‘shapes’ that a class expression can take, but it definitely processes the commonly used features well. At the time of writing, we haven’t detected any errors.

p.s.: if you want your whole ontology exported at once in DL notation and to latex, for purposes of documentation generation, that is a different usage scenario and is already possible [4].

p.p.s.: if you want more DL notation, please let me know, and I’ll try to find more resources to make a v2 with more features.

References

[1] Matthew Horridge, Nicholas Drummond, John Goodwin, Alan Rector, Robert Stevens and Hai Wang (2006). The Manchester OWL syntax. OWL: Experiences and Directions (OWLED’06), Athens, Georgia, USA, 10-11 Nov 2016, CEUR-WS vol 216.

[2] E. Alharbi, J. Howse, G. Stapleton, A. Hamie and A. Touloumis. The efficacy of OWL and DL on user understanding of axioms and their entailments. The Semantic Web – ISWC 2017, C. d’Amato, M. Fernandez, V. Tamma, F. Lecue, P. Cudre-Mauroux, J. Sequeda, C. Lange and J. He (eds.). Springer 2017, pp20-36.

[3] M. K. Sarker, A. Krisnadhi, D. Carral and P. Hitzler, Rule-based OWL modeling with ROWLtab Protégé plugin. Proceedings of ESWC’17, E. Blomqvist, D. Maynard, A. Gangemi, R. Hoekstra, P. Hitzler and O. Hartig (eds.). Springer. 2017, pp 419-433.

[4] Cogan Shimizu, Pascal Hitzler, Matthew Horridge: Rendering OWL in Description Logic Syntax. ESWC (Satellite Events) 2017. Springer LNCS. pp109-113

# Some experiences on making a textbook available

I did make available a textbook on ontology engineering for free in July 2018. Meanwhile, I’ve had several “why did you do this and not a proper publisher??!?” I had tried to answer that already in the textbook’s FAQ. Turns out that that short answer may be a bit too short after all. So, here follows a bit more about that.

The main question I tried to answer in the book’s FAQ was “Would it not have been better with a ‘proper publisher’?” and the answer to that was:

Probably. The layout would have looked better, for sure. There are several reasons why it isn’t. First and foremost, I think knowledge should be free, open, and shared. I also have benefited from material that has been made openly available, and I think it is fair to continue contributing to such sharing. Also, my current employer pays me sufficient to live from and I don’t think it would sell thousands of copies (needed for making a decent amount of money from a textbook), so setting up such a barrier of high costs for its use does not seem like a good idea. A minor consideration is that it would have taken much more time to publish, both due to the logistics and the additional reviewing (previous multi-author general textbook efforts led to nothing due to conflicting interests and lack of time, so I unlikely would ever satisfy all reviewers, if they would get around reading it), yet I need the book for the next OE installment I will teach soon.

Ontology Engineering (OE) is listed as an elective in the ACM curriculum guidelines. Yet, it’s suited best for advanced undergrad/postgrad level because of the prerequisites (like knowing the basics of databases and conceptual modeling). This means there won’t be big 800-students size classes all over the world lining up for OE. I guess it would not go beyond some 500-1000/year throughout the world (50 classes of 10-20 computer science students), and surely not all classes would use the textbook. Let’s say, optimistically, that 100 students/year would be asked to use the book.

With that low volume in mind, I did look up the cost of similar books in the same and similar fields with the ‘regular’ academic publishers. It doesn’t look enticing for either the author or the student. For instance this one from Springer and that one from IGI Global are all still >100 euro. for. the. eBook., and they’re the cheap ones (not counting the 100-page ‘silver bullet’ book). Handbooks and similar on ontologies, e.g., this and that one are offered for >200 euro (eBook). Admitted there’s the odd topical book that’s cheaper and in the 50-70 euro range here and there (still just the eBook) or again >100 as well, for a, to me, inexplicable reason (not page numbers) for other books (like these and those). There’s an option to publish a textbook with Springer in open access format, but that would cost me a lot of money, and UCT only has a fund for OA journal papers, not books (nor for conference papers, btw).

IOS press does not fare much better. For instance, a softcover version in the studies on semantic web series, which is their cheapest range, would be about 70 euro due to number of pages, which is over R1100, and so again above budget for most students in South Africa, where the going rate is that a book would need to be below about R600 for students to buy it. A plain eBook or softcover IOS Press not in that series goes for about 100 euro again, i.e., around R1700 depending on the exchange rate—about three times the maximum acceptable price for a textbook.

The MIT press BFO eBook is only R425 on takealot, yet considering other MIT press textbooks there, with the size of the OE book, it then would be around the R600-700. Oxford University Press and its Cambridge counterpart—that, unlike MIT press, I had checked out when deciding—are more expensive and again approaching 80-100 euro.

One that made me digress for a bit of exploration was Macmillan HE, which had an “Ada Lovelace day 2018” listing books by female authors, but a logics for CS book was again at some 83 euros, although the softer area of knowledge management for information systems got a book down to 50 euros, and something more popular, like a book on linguistics published by its subsidiary “Red Globe Press”, was down to even ‘just’ 35 euros. Trying to understand it more, Macmillan HE’s “about us” revealed that “Macmillan International Higher Education is a division of Macmillan Education and part of the Springer Nature Group, publishers of Nature and Scientific American.” and it turns out Macmillan publishes through Red Globe Press. Or: it’s all the same company, with different profit margins, and mostly those profit margins are too high to result in affordable textbooks, whichever subsidiary construction is used.

So, I had given up on the ‘proper publisher route’ on financial grounds, given that:

• Any ontology engineering (OE) book will not sell large amounts of copies, so it will be expensive due to relatively low sales volume and I still will not make a substantial amount from royalties anyway.
• Most of the money spent when buying a textbook from an established publisher goes to the coffers of the publisher (production costs etc + about 30-40% pure profit [more info]). Also, scholarships ought not to be indirect subsidy schemes for large-profit-margin publishers.
• Most publishers would charge an amount of money for the book that would render the book too expensive for my own students. It’s bad enough when that happens with other textbooks when there’s no alternative, but here I do have direct and easy-to-realise agency to avoid such a situation.

Of course, there’s still the ‘knowledge should be free’ etc. argument, but this was to show that even if one were not to have that viewpoint, it’s still not a smart move to publish the textbook with the well-known academic publishers, even more so if the topic isn’t in the core undergraduate computer science curriculum.

Interestingly, after ‘publishing’ it on my website and listing it on OpenUCT and the Open Textbook Archive—I’m certainly not the only one who had done a market analysis or has certain political convictions—one colleague pointed me to the non-profit College Publications that aims to “break the monopoly that commercial publishers have” and another colleague pointed me to UCT press. I had contacted both, and the former responded. In the meantime, the book has been published by CP and is now also listed on Amazon for just \$18 (about 16 euro) or some R250 for the paperback version—whilst the original pdf file is still freely available—or: you pay for production costs of the paperback, which has a slightly nicer layout and the errata I knew of at the time have been corrected.

I have noticed that some people don’t take the informal self publishing seriously—even below the so-called ‘vanity publishers’ like Lulu—notwithstanding the archives to cater for it, the financial take on the matter, the knowledge sharing argument, and the ‘textbooks for development’ in emerging economies angle of it. So, I guess no brownie points from them then and, on top of that, my publication record did, and does, take a hit. Yet, writing a book, as an activity, is a nice and rewarding change from just churning out more and more papers like a paper production machine, and I hope it will contribute to keeping the OE research area alive and lead to better ontologies in ontology-driven information systems. The textbook got its first two citations already, the feedback is mostly very positive, readers have shared it elsewhere (reddit, ungule.it, Open Libra, Ebooks directory, and other platforms), and I recently got some funding from the DOT4D project to improve the resources further (for things like another chapter, new exercises, some tools development to illuminate the theory, a proofreading contest, updating the slides for sharing, and such). So, overall, if I had to make the choice again now, I’d still do it again the way I did. Also, I hope more textbook authors will start seeing self-publishing, or else non-profit, as a good option. Last, the notion of open textbooks is gaining momentum, so you even could become a trendsetter and be fashionable 😉

# ISAO 2018, Cape Town, ‘trip’ report

The Fourth Interdisciplinary School on Applied Ontology has just come to an end, after five days of lectures, mini-projects, a poster session, exercises, and social activities spread over six days from 10 to 15 September in Cape Town on the UCT campus. It’s not exactly fair to call this a ‘trip report’, as I was the local organizer and one of the lecturers, but it’s a brief recap ‘trip report kind of blog post’ nonetheless.

The scientific programme consisted of lectures and tutorials on:

The linked slides (titles of the lectures, above) reveal only part of the contents covered, though. There were useful group exercises and plenary discussion with the ontological analysis of medical terms such as what a headache is, a tooth extraction, blood, or aspirin, an exercises on putting into practice the design process of a conceptual modelling language of one’s liking (e.g.: how to formalize flowcharts, including an ontological analysis of what those elements are and ontological commitments embedded in a language), and trying to prove some theorems of parthood theories.

There was also a session with 2-minute ‘blitztalks’ by participants interested in briefly describing their ongoing research, which was followed by an interactive poster session.

It was the first time that an ISAO had mini-projects, which turned out to have had better outcomes than I expected, considering the limited time available for it. Each group had to pick a term and investigate what it meant in the various disciplines (task description); e.g.: what does ‘concept’ or ‘category’ mean in psychology, ontology, data science, and linguistics, and ‘function’ in manufacturing, society, medicine, and anatomy? The presentations at the end of the week by each group were interesting and most of the material presented there easily could be added to the IAOA Education wiki’s term list (an activity in progress).

What was not a first-time activity, was the Ontology Pub Quiz, which is a bit of a merger of scientific programme and social activity. We created a new version based on questions from several ISAO’18 lecturers and a few relevant questions created earlier (questions and answers; we did only questions 1-3,6-7). We tried a new format compared to the ISAO’16 quiz and JOWO’17 quiz: each team had 5 minutes to answer a set of 5 questions, and another team marked the answers. This set-up was not as hectic as the other format, and resulted in more within-team interaction cf. among all participants interaction. As in prior editions, some questions and answers were debatable (and there’s still the plan to make note of that and fix it—or you could write an article about it, perhaps :)). The students of the winning team received 2 years free IAOA membership (and chocolate for all team members) and the students of the other two teams received one year free IAOA membership.

Impression of part of the poster session area, moving into the welcome reception

As with the three previous ISAO editions, there was also a social programme, which aimed to facilitate getting to know one another, networking, and have time for scientific conversations. On the first day, the poster session eased into a welcome reception (after a brief wine lapse in the coffee break before the blitztalks). The second day had an activity to stretch the legs after the lectures and before the mini-project work, which was a Bachata dance lesson by Angus Prince from Evolution Dance. Not everyone was eager at the start, but it turned out an enjoyable and entertaining hour. Wednesday was supposed to be a hike up the iconic Table Mountain, but of all the dry days we’ve had here in Cape Town, on that day it was cloudy and rainy, so an alternative plan of indoor chocolate tasting in the Biscuit Mill was devised and executed. Thursday evening was an evening off (from scheduled activities, at least), and Friday early evening we had the pub quiz in the UCT club (the campus pub). Although there was no official planning for Saturday afternoon after the morning lectures, there was again an attempt at Table Mountain, concluding the week.

The participants came from all over the world, including relatively many from Southern Africa with participants coming also from Botswana and Mauritius, besides several universities in South Africa (UCT, SUN, CUT). I hope everyone has learned something from the programme that is or will be of use, enjoyed the social programme, and made some useful new contacts and/or solidified existing ones. I look forward to seeing you all at the next ISAO or, better, FOIS, in 2020 in Bolzano, Italy.

Finally, as a non-trip-report comment from my local chairing viewpoint: special thanks go to the volunteers Zubeida Khan for the ISAO website, Zola Mahlaza and Michael Harrison for on-site assistance, and Sam Chetty for the IT admin.