What is a pandemic, ontologically?

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:

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. Infectiousness; it’s got to be infectious, and so excluding non-infectious things, like obesity and smoking. Clear yes/no.
7. 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).
8. 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 dependent continuant 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.

References

[1] Doshi, P. The elusive definition of pandemic influenza. Bulletin of the World Health Organization,  2011, 89:532–538

[2] Kelly, H. The classical definition of a pandemic is not elusive. Bulletin of the World Health Organization, 2011, 89 (‎7)‎, 540 – 541.

[3] Morens, DM, Folkers, GK, Fauci, AS. What Is a Pandemic? The Journal of Infectious Diseases, 2009, 200(7): 1018-1021.

[4] Babcock, S., Beverley, J., Cowell, L.G. et al. The Infectious Disease Ontology in the age of COVID-19. Journal of Biomedical Semantics, 2021, 12, 13.

[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.

[6] Keet, C.M., Berman, S. Determining the preferred representation of temporal constraints in conceptual models. 36th International Conference on Conceptual Modeling (ER’17). Springer LNCS 10650, 437-450. 6-9 Nov 2017, Valencia, Spain.

[7] Fleming DM, Zambon M, Bartelds AI, de Jong JC. The duration and magnitude of influenza epidemics: a study of surveillance data from sentinel general practices in England, Wales and the Netherlands. European Journal of Epidemiology, 1999, 15(5):467-73.

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On computer program being a whole

Who cares whether some computer program is a whole, how, and why? Turns out, more people than you may think—and so should you, since it can be costly depending on the answer. Consider the following two scenarios: 1) you download a ‘pirated’ version of MS Office or Adobe Photoshop (the most popular ones still) and 2) you take the source code of a popular open source program, such as Notepad++, add a little code for some additional function, and put it up for sale only as an executable app called ‘Notepad++ extreme (NEXT)’ so as to try to earn money quickly. Are these actions legal?

In both cases, you’d break the law, but how many infringements took place, of the one that you potentially could be fined for or face jail time? For the piracy case, is that once for the MS Office suite, or for each progam in the suite, or for each file created upon installing MS office, or for each source code file that went into making the suite during software development? For the open source case, was that violating its GNU GLP open source licence once for the zipped&downloaded or cloned source code or for each file in the source code, of which there are hundreds? It is possible to construct similar questions for trade secret violations and patent infringements for programs, as well as other software artefacts, like illegal downloads of TV series episodes (going strong during COVID-19 lockdowns indeed). Just in case you think this sort of issue is merely hypothetical: recently, Arista paid Cisco $400 million for copyright damages and just before that, Zenimax got $500 million from Oculus (yes, the VR software) for trade secret violations, and Google vs Oracle is ongoing with “billions of dollars at stake”.

Let’s consider some principles first. To be able to answer the number of infringements, we first need to know whether a computer program is a whole or not and why, and if so, what’s ‘in’ (i.e., a part of it) and what’s ‘out’ (i.e., definitely not part of it). Spoiler alert: a computer program is a functional whole.

To get to that conclusion, I had to combine insights from theories of parthood (mereology), granularity, modularity, unity, and function and add a little more into the mix. To provide less and more condensed versions of the argumentation, there is a longer technical report [1], of which I hope it is readable by a wider audience, and a condensed version for a specialist audience [2] that was published in the Proceedings of the 11th Conference on Formal Ontologies in Information Systems (FOIS’20) two weeks ago. Very briefly and informally, the state of affairs can be illustrated with the following picture:

(Source: adapted from [2])

This schematic representation shows, first, two levels of granularity: level 1 and level 2. At level 1, there’s some whole, like the a1 and a2 in the figure that could be referring to, say, a computer program, a module repository, an electorate, or a human body. At a more fine-grained level 2, there are different entities, which are in some way linked to the respective whole. This ‘link’ to the whole is indicated with the vertical dashed lines, and one can say that they are part of the whole. For the blue dots on the right residing at level 2, i.e., the parts of a1, there’s also a unifying relation among the parts, indicated with the solid lines with arrows, which makes a1 an integral whole. Moreover, for that sort of whole, it holds that if some object x (residing at level 2) is part of a1 then if there’s a y that is also part of a1, it participates in that unifying relation with x and vice versa (i.e., if y is in that unifying relation with x, then it must also be part of a1). For the computer program’s source code, that unifying relation can be the source tree graph.

There is some nitty gritty detail also involving the notion of function—a source code file contributes to doing something—and optional vs mandatory vs essential part that you can read about in the report or in the paper [1,2], covering the formalisation, more argumentation, and examples.

How would it pan out for the infringements? The Notepad++ exploitation scenario would simply be a case of one infringement in total for all the files needed to create the executable, not one for each source code file. This conclusion from the theory turns out remarkably in line with the GNU GPL’s explanation of their licence, albeit then providing a theoretical foundation for their intuition that there’s a difference between a mere aggregate where different things are bundled, loose coupling (e.g., sockets and pipes) and a single program (e.g., using function calls, being included in the same executable). The order of things perhaps should have been from there into the theory, but practically, I did the analysis and stumbled into a situation where I had to look up the GPL and its explanatory FAQ. On the bright side, in the other direction now then: just  in case someone wants to take on copyleft principles of open source software, here are some theoretical foundations to support that there’s probably much less money to be gained than you might think.

For the MS Office suite case mentioned at the start, I’d need a look under the hood to determine how it ties together and one may have to argue about the sameness of, or difference between, a suite and a program. The easier case for a self-standing app, like the 3^rd-place most pirated Windows app Internet Download Manager, is that it is one whole and so one infringement then.

It’s a pity that FOIS 2020 has been postponed to 2021, but at least I got to talk about some of this as expert witness for a litigation case and I managed to weave an exercise about the source tree with open source licences into the social issues and professional practice module I thought to some 750 students this past winter.

References

[1] Keet, C.M. Why a computer program is a functional whole. Technical report 2008.07273, arXiv. 21 July 2020. 25 pages.

[2] Keet, C.M. The computer program as a functional whole. Proc. of FOIS 2020. Brodaric, B. and Neuhaus, F. (Eds.). IOS Press. FAIA vol. 330, 216-230.

FOIS’18 conference report

To some perhaps surprisingly, despite being local organizer, I could attend all sessions of the 10th International Conference Formal Ontology in Information Systems as participant (cf. running around for last-minute things). It just wasn’t as much of a trip as it usually is: only 15 minutes to town at the Atlantic Imbizo conference venue, which is situated between the Clock Tower and (award-winning) Zeitz MOCAA at Cape Town’s V&A Waterfront. This blog post has turned into a longer post than intended—yet, there’s still so much left out to talk about—and it is divided up into sections on keynotes, presentations, ontologies, and the (ontologically inappropriate basket of) other things.

 

Keynotes

The first keynote was presented by (emeritus) professor in philosophy Peter Simons from Trinity College Dublin and Universität Salzburg, on the ontology of aboutness (slides).

Peter Simon during his keynote talk

That may sound a bit abstract, but it is not unusual for some information system that it will have to record statements about something, such as different medical opinions, changes of policies, plans or expectations, and we need a way to represent that and deal with it. Simons discussed several earlier proposals before proposing his own, which includes as main entities a bearer, act, time, act-type, mental content, mental content type, intentional objects, referent, and referent type (slide 16), and then variants for pictorial and linguistic (speech and writing). And, in closing, his advice of “Don’t get involved in irrelevant philosophical disputes”.

The second keynote was presented by Alessandro Oltramari, who works at Bosch Research and Technology Centre in Pittsburgh, USA. He presented several of Bosch’s projects where ontologies are used in one way or another (slides) and that he was involved in. One of them was about knowledge-based intelligent IoT and another on an emergency assistant, or, in business sales parlance, a “personal guardian angel” mobile device that has location awareness, safety information of those locations, a decision support system for alternate route computation, and automatic escalation. The ontologies used include the foundational ontology DOLCE, the domain ontology of semantic sensor networks (SSN) from the W3C, and specific schemas developed in-house. Another project on a knowledge-based chatbot for healthcare policies links up DOLCE, schema.org, and some in-house schemas with Highmark-specific information (and is not ashamed of using SKOS). Om my question what methods and methodologies were used for the in-house ontology development, the (disappointing) answer was, unfortunately, only “DOLCE and OntoClean”, but the former is neither a method nor a methodology (it implies a top-down approach), and the latter is some 15 years old, as if nothing has happened in ontology engineering in the meantime (more about that further below). Regardless, it was good to see that ontologies are being used in industry.

The third keynote (slides) was by Riichiro Mizoguchi from the Japan Advanced Institute of Science and Technology (JAIST), on a state-centric methodology, which I’ll leave for a separate post.

Riichiro Mizoguchi during his keynote talk.

 

Presentations

The report on the presentations easily could take up several pages, but I’ll try to keep it short, lest otherwise this post never gets posted. The first session of the conference was on foundations. This included Antony Galton’s assessment of the treatment of time in upper ontologies [1]. It was mildly entertaining in that it turned out that BFO would need abstract things for its treatment of time (which it doesn’t have and doesn’t like) and adheres to Newtonian physics cf. the latest scientific theories. It is definitely on my list of papers to read in more detail. Another paper-for-printing to read is Torsten Hahmann’s work on mereotopology, which extends it to multidimensional space [2]. A nice bonus (though it ought not to be perceived as such) is that at least the theorems in the paper have been proved with Prover9 and Vampire (cf. having to double-check them manually). Laure Vieu presented a proposal for a graph-based approach to represent structure among the components of an entity [3], which is apparently different from the graph-based approach for representing molecules (within the Semantic Web context); I’ll have to look at that in more detail, for it sounds like it might be of some use for the parts aspects of part-whole relations.

Besides such theoretical contributions that are rather distant from applications, there were two of note that were motivated from praxis more clearly. One was about the ontological foundations of competition and the sort of competitive relations there are [4], which was presented by Tiago Prince Sales. The other one was presented by Pawel Garbacz, whose presentation conveyed more than the paper so as to get a real feel of the problem, being identity criteria for localities [5], with complicating use cases extracted from a Polish history project. He presented some examples of changes and a proposal for how to identify a locality/settlement. For instance, settlements can get moved altogether, have a population-only move, split into two, be merged, renamed and renamed again, deserted by a population and repopulated and renamed, and so on. When is it the same settlement and when is it another one? The paper [5] describes a first solution for identity criteria with an event-based approach to identity of localities.

My presentation on part-whole relations in Zulu language and culture [6] was scheduled in the ‘applications’ session, which had positive feedback and some pointers that may assist with future work.

 

venue during a Q&A session

Ontologies

Besides presentations, there was a discussion session on “what constitutes a good ontology paper?” for the Applied Ontology journal. Seeing the ontology papers at FOIS now, they should have done such as session for FOIS as well. There are four papers in the proceedings describing OWL files: “Amnestic forgery” (AF, conceptual metaphors) [7] presented by Mehwish Alam, UNiCS for research and innovation policy [8] presented by Fernando Roda, SAREF4Health [9] presented by João Moreira, and religious and spiritual belief (ORSB) [10] presented by Stefan Schulz. Skimming through each paper, AF, UNiCS and ORSB do not use a methodology explicitly, none of them uses existing methods, but they all do use a foundational or top-level ontology or the WordNet material, and then it’s cool enough to get into FOIS, apparently. This is a bit disappointing. At least SAREF4Health presented a set of competency questions, a systematic approach and broader framework, and some evaluation, and ORSB reuses not only top-level and top-domain ontologies but also tests some patterns. AF and ORSB have some interest to it as they’re addressing relatively novel modeling issues to solve and the ORSB discussion could be used more broadly for any “terms of dubious reference”. UNiCS is not really an ontology but an information model or, at best, a conceptual data model (e.g. calling “SCOPUS subject” an ontology is pushing it a bit too far); it makes their OBDA scenario easier to realize, true, but that’s a separate discussion. Fig 1 of SAREF4Health doesn’t look any better either, which has all the hallmarks of a plain UML Class Diagram (attributes with data types and such), with object diagram components attached and coloured in and annotated with OntoUML. SAREF4Health’s other downsides are things like “implementing the ontology as RDF” that just hurts to read (it is left implicit for AF that is plugged into the LOD cloud), as is the download in Turtle format (cf. the required exchange syntax of OWL 2), which isn’t even available at the provided link when you click on it (copy-paste gets you in the right direction), but is [I think] in some github sub-directory that has a whole bunch of ttl files with neither head nor tail, but one of them is called saref4health.ttl. On first inspection, it has plenty of data properties and data type use, and the class-as-instance issue here and there (e.g., ‘Rechargeable Lithium Polymer battery’ as instance cf. class), and others (e.g., a ‘series’ of measurements is not a subclass of a measurement) and very many classes directly subsumed by top, though some are knock-on effects from imports.

And then ontologists at FOIS deplored that there are many domain ontologies that are of poor quality and artifacts presented as ontologies but aren’t. The FOIS reviewers themselves apparently can’t even get their act together in the reviewing process, where artifacts that are sold as domain ontologies but aren’t (UNiCS, SAREF4Health) make it not only through the reviewing process but, moreover, even get a best paper award from the PC chairs (SAREF4Health). The PC chairs wanted to make a political statement to communicate that FOIS accepts domain ontology papers. It is good that the FOIS topics are becoming less narrow and I’m not saying they are pointless papers or lousy artifacts per sé—they are useful reference papers and UNiCS and SAREF4Health perform the application tasks they’re supposed to be performing, which is a good thing. Maybe, collectively, ontology developers can’t do better or don’t need to do better w.r.t. applied ontology? Either way, once upon a time there were principles for what ontologies are; what happened to that? Also, there are multiple methodologies for domain ontology development, and there are a myriad of methods and tools, which have been mostly ignored. For instance, using one foundational ontology over another ‘just because I know x’ is neither a scientific nor a sound engineering approach. There are comparisons, requirements, and a mix of the two to help you figure out which one is the best to use; an early tool for that is ONSET, the ONtology Selection and Explanation Tool, developed by Zubeida Khan (more data). To name one example.

Coincidentally, ontology engineering papers with such a content do not, or very rarely, make it into FOIS; but just that they don’t (because they’re typically not philosophical enough), doesn’t mean they don’t exist. Just in case a FOIS ontologist would like to explore methods, methodologies and tools for ontology development: ESWC, EKAW, and K-CAP are good/top conferences covering such topics in whole or in part, and Chapter 5 of the ontology engineering textbook provides a sampling as well (as do some other sections in Block II). Considering my critical comments, one may ask whether my ontologies and ontology papers are any better, or anyone else’s for that matter. Perhaps, perhaps not. You can check for yourself some of my recent papers on domain ontologies that also have OWL files[1] that I was involved in developing; one paper was intended as a reference paper for the domain ontology [11], another paper was a bit of both domain ontology and some framework [12], and yet another turned into a core ontology [13] (v1, with the main categories; there’s an updated version for the relations).

Anyway, returning to the first sentence of this section: the open forum discussion did not make it any clearer as to what would be the characteristics of a good ontology paper for the Applied Ontology journal (or FOIS, for that matter). Mainly just Protégé screenshots certainly is not, but opinions varied as to what would be. Going by examples of the ontology papers that made it through: use of a top-level or foundational ontology and some modeling issues and solutions seems to be preferred, evaluation and usage & uptake as a nice-to-have. Is developing an (domain) ontology science? That question wasn’t answered unanimously; I think it was leaning towards a ‘mostly no’ w.r.t. applied ontology but it may be if it’s the first to solve a modeling issue. How to evaluate the ontology? Another question without a satisfactory answer. Overall, the criteria for an ontology paper—let alone for the ontology itself—are “TBD” and meanwhile one has to hope that one will get a supportive ‘reviewer 2’.

 

Other

In case you have clicked-though to one or more of the listed papers, you may have noticed that the FOIS’18 proceedings are Open Access—paid for by those who registered for the conference (it was calculated in the registration fee). I suppose the next FOIS organisers and the IAOA exec may like your opinion on that approach.

mentors of the early career symposium papers

Besides the best paper award for SAREF4Health [9], there were two “distinguished paper awards”, which went to aforementioned paper on the graph-based approach for structured universals by Laure Vieu and Claudio Masolo [3] and to the foundational ontologies for units of measure by Michael Grüninger and co-authors [14]. The early career symposium went well and from hearsay they had a good social activity, too. There were lots of interesting conversations, networking, good food, and so on, and lots more to write about. There are also more photos.

Some of the postgraduate students and a recent PhD graduate in the spotlight at the closing ceremony, being thanked for chairing the sessions.

Last, but not least: the next FOIS in 2020 will be in Bolzano, Italy, as part of a ‘Bolzano summer of knowledge’ with more co-located conferences, workshops, and summer schools.

 

References

[1] Antony Galton. The treatment of time in upper ontologies. Proc. of FOIS’18. IOS Press, 306: 33-46.

[2] Thorsten Hahmann. On Decomposition Operations in a Theory of Multidimensional Qualitative Space. Proc. of FOIS’18. IOS Press, 306: 173-186.

[3] Claudio Masolo, Laure Vieu. Graph-Based Approaches to Structural Universals and Complex States of Affairs. Proc. of FOIS’18. IOS Press, 306: 69-82.

[4] Tiago Prince Sales, Daniele Porello, Nicola Guarino, Giancarlo Guizzardi, John Mylopoulos. Ontological Foundations of Competition. Proc. of FOIS’18. IOS Press, 306: 96-112.

[5] Pawel Garbacz, Agnieszka Ławrynowicz, Bogumił Szady. Identity criteria for localities. Proc. of FOIS’18. IOS Press, 306: 47-56.

[6] C. Maria Keet, Langa Khumalo. On the Ontology of Part-Whole Relations in Zulu Language and Culture. Proc. of FOIS’18. IOS Press, 306: 225-238.

[7] Aldo Gangemi, Mehwish Alam, Valentina Presutti. Amnestic Forgery: An Ontology of Conceptual Metaphors. Proc. of FOIS’18. IOS Press, 306: 159-172.

[8] Alessandro Mosca, Fernando Roda, Guillem Rull. UNiCS – The Ontology for Research and Innovation Policy Making. Proc. of FOIS’18. IOS Press, 306: 200-210.

[9] João Moreira, Luís Ferreira Pires, Marten van Sinderen, Laura Daniele. SAREF4health: IoT Standard-Based Ontology-Driven Healthcare Systems. Proc. of FOIS’18. IOS Press, 306: 239-252.

[10] Stefan Schulz, Ludger Jansen. Towards an Ontology of Religious and Spiritual Belief. Proc. of FOIS’18. IOS Press, 306: 253-260.

[11] Keet, C.M., Lawrynowicz, A., d’Amato, C., Kalousis, A., Nguyen, P., Palma, R., Stevens, R., Hilario, M. The Data Mining OPtimization ontology. Web Semantics: Science, Services and Agents on the World Wide Web, 2015, 32:43-53.

[12] Chavula, C., Keet, C.M. An Orchestration Framework for Linguistic Task Ontologies. 9th Metadata and Semantics Research Conference (MTSR’15), Garoufallou, E. et al. (Eds.). Springer CCIS vol. 544, 3-14.

[13] Keet, C.M. A core ontology of macroscopic stuff. 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14). K. Janowicz et al. (Eds.). 24-28 Nov, 2014, Linkoping, Sweden. Springer LNAI vol. 8876, 209-224.

[14] Michael Grüninger, Bahar Aameri, Carmen Chui, Torsten Hahmann, Yi Ru. Foundational Ontologies for Units of Measure. Proc. of FOIS’18. IOS Press, 306: 211-224.

[1] I have others developed as part of methods & tools research

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:

• Mereology, location, and time by Prof. Maureen Donnelly (with the Department of Philosophy from the State University of New York at Buffalo, USA);
• Biomedical Ontology by Prof. Stefan Schulz (with the Medical University in Graz, Austria);
• Foundations of Description Logics by Prof. Arina Britz (with the Department of Information Science, Stellenbosch University);
• Semantics and conceptual modeling by myself;
• Ontology for robotics by Dr. Stefano Borgo (with the Laboratory for Applied Ontology, CNR, in Trento, Italy);
• Applied mereolgy by Prof. Peter Simons (emeritus, with Trinity College Dublin, Ireland).

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.

An Ontology Engineering textbook

My first textbook “An Introduction to Ontology Engineering” (pdf) is just released as an open textbook. I have revised, updated, and extended my earlier lecture notes on ontology engineering, amounting to about 1/3 more new content cf. its predecessor. Its main aim is to provide an introductory overview of ontology engineering and its secondary aim is to provide hands-on experience in ontology development that illustrate the theory.

The contents and narrative is aimed at advanced undergraduate and postgraduate level in computing (e.g., as a semester-long course), and the book is structured accordingly. After an introductory chapter, there are three blocks:

• Logic foundations for ontologies: languages (FOL, DLs, OWL species) and automated reasoning (principles and the basics of tableau);
• Developing good ontologies with methods and methodologies, the top-down approach with foundational ontologies, and the bottom-up approach to extract as much useful content as possible from legacy material;
• Advanced topics that has a selection of sub-topics: Ontology-Based Data Access, interactions between ontologies and natural languages, and advanced modelling with additional language features (fuzzy and temporal).

Each chapter has several review questions and exercises to explore one or more aspects of the theory, as well as descriptions of two assignments that require using several sub-topics at once. More information is available on the textbook’s page [also here] (including the links to the ontologies used in the exercises), or you can click here for the pdf (7MB).

Feedback is welcome, of course. Also, if you happen to use it in whole or in part for your course, I’d be grateful if you would let me know. Finally, if this textbook will be used half (or even a quarter) as much as the 2009/2010 blogposts have been visited (around 10K unique visitors since posting them), that would mean there are a lot of people learning about ontology engineering and then I’ll have achieved more than I hoped for.

UPDATE: meanwhile, it has been added to several open (text)book repositories, such as OpenUCT and the Open Textbook Archive, and it has been featured on unglue.it in the week of 13-8 (out of its 14K free ebooks).

Ontology pub quiz questions of ISAO 2016 and JOWO 2017

In 2016 when I was a PC chair of the International School for Applied Ontology (ISAO 2016), the idea of organising a contest for the participants turned into a pub quiz somehow. The lecturers provided one or more questions on the topics they’d be teaching and I added a few as well. This set of ISAO16 ontology pub quiz questions is now finally online. It comes with the warning that it is biased toward the topics covered at ISAO 2016, and it turned out that there were a few questions not well formulated and/or not everyone agreed with the answer.

Notwithstanding, it was deemed sufficiently ok as idea in that the general chair of the Joint Ontology Workshops (JOWO 2017) wanted one for JOWO 2017 as well. Several questions were thrown out of the ISAO16 set for various reasons and more general Ontology questions made their way in, as well as a few ‘fun’ and trivia ones in the hope to add some more entertainment to the ontology pub quiz. The JOWO17 pub quiz question set with answers is now also online to play with, which, in my opinion, is a nicer set than the ISAO16 one. Here are a few questions to give you a taste of it:

• Where/when can a pointless theory be relevant?
• What is the goal of guerrilla ontology?
• No Italian pizza has fruit as topping. Which of the following is (an)/are Italian pizza(s)? Pizza Hawaii, Pizza margherita, Pizza bianca romana (‘white roman pizza’)
• When was the earliest published occurrence of the word “ontology”?

It turned out that it still was not entirely free of debate. If you disagree with one of the answers now, then let me paraphrase Stefano Borgo, who co-ran the JOWO17 pub quiz at the Irish pub in Bolzano on 23 September: maybe there’s something there to write up and submit a paper to FOIS 2018 :-). Or you can write it in the blog post comments section below, so that those questions will/should not be recycled and I can add longer answers to the questions.

Aligning different relations: the case of part-whole relations—LDK2017

Despite the best intentions, I did not get around to writing a post on the paper that I presented last week at the First International Conference on Language, Data and Knowledge 2017, 19-20 June, Galway, Ireland, and now Paul Groth also ‘beat’ me to it writing a nice conference report of it. On the bright side, it is an opportunity to say upfront I really enjoyed the conference and look forward to the next edition in 2019. The ESWC’17 organisers might be slightly disappointed that there was no special track on the multilingual semantic web after all, but I did get the distinct impression that the LDK17 authors might just all have gambled on LDK17—an opportunity to binge two days on all things natural language & Semantic Web—rather than on one track at an overpriced conference (despite the allure of it being A-rated).

So, what was my paper about that could have been submitted to either? I ended up struggling—and solving—an issue with aligning OWL object properties that were not simple 1:1 mappings, in a similar scope as our ESWC17 paper (introduced here) [4], but then with too many complications. Complications were due to the different conceptualisations of part-whole relations and that one of the requirements was to solve what to do with an object property (relation, relationship) that does not have a neat, single, label, and therewith neither fitting with the common OWL modelling paradigm nor with the recently agreed-upon ontolex-lemon model for linguistic annotations.

The start of all this sounded nice and doable: we need to generate natural language for healthcare, using, e.g., SNOMED CT, in local languages in South Africa, focussing on the largest one, being isiZulu. Medical terminologies are riddled with part-whole relations, so we sought to address that one (simple existentials already having been solved), availing of a standard list of part-whole relations (e.g. [1]). That turned out to be a non-trivial exercise, but doable eventually [2]. What wasn’t addressed in [2] was that some ‘common’ part-whole relations, such as membership and containment, weren’t like that in isiZulu, at all. Moreover, it wasn’t just a language issue, but ontological as well. The LDK17 paper “Representing and aligning similar relations: parts and wholes in isiZulu vs English” [3] describes this in some detail.

Here’s a (simplified) list of (assumed to be) common part-whole relations, which takes into account both transitivity differences and domain and range:

Now here’s the one based on the isiZulu language and some ontological analysis of that:

That is: there are both generalisations—some distinctions are not being made—and specialisations—some distinctions are made here but not elsewhere. For instance, ‘musician is part of some orchestra’ and ‘heart is part of some human’ (or vv.) is all done and described in the same way (ingxenye ‘part of’ and SC+CONJ for ‘has part’ [more about that below]). Yet, there is a difference between an individual (e.g., a voter) participating in some process and a collective (e.g., the electorate) participating in a process, or vv. The paper describes this more precisely, going into some detail regarding the differences in categories of domain and range and into the consequences on transitivity of mereological parthood.

The other ‘odd thing’—cf. current multilingual Semantic Web assumptions and technologies, that is—is that while the conceptualisation of ‘has part’ exists, it does not have a single label as in English (or in several other languages, such as heeft as deel), but it is dependent on the noun class of the noun of the class that play the part and play the whole in the relation. It combines the subject concord (~conjugation) of the noun class of the noun that plays the whole with a conjunction that is phonologically conditioned based on the first letter of the noun that plays the part; with verbalisation in the plural and three phonological cases, there are 18 possible strings all denoting ‘has part’. This still could be sorted with a language with inverses, provided the part-of direction has a name, like the ingxenye. This is not the case for containment, however. Instead of the relation (object property) having a name—be this a verb like ‘contained in’ or some noun phrase—it is the noun that plays the whole (the container, if you will) that gets modified. For instance, imvilophu ‘envelope’ and emvilophini denoting ‘contained in the envelope’, or, for individuals and locations, the city iTheku ‘Durban’ and eThekwini meaning ‘located in Durban’ (no typo—there’s some phonological conditioning I’m brushing over). While I have gotten used to such constructions, it generated some surprise among several attendees that one can have notions, concepts, views on or interpretations or descriptions of reality, that exist but do not have even one single string of text throughout to refer to regardless the context it is used.

The naming issue was solved by adding some arbitrary string as ‘name’ of the object property, and relating that to the function that verbalises that specific part-whole relation. The former issue, i.e., not all the same part-whole relations, required a bit more work, using ontology pattern alignments, by extending one correspondence pattern from the ODP catalogue and introducing a new one (see paper for the formal details), using the same broad framework of formalisation as proposed in [4].

All this was then implemented and aligned, and verified to not result in some unsatisfiable classes, object properties, or inconsistency (files). This also works in the isiZulu verbalisation tool we demo-ed at ESWC17 (described in the previous post) [5], all as part of the NRF-funded GeNI project.

Now, ideally, I already would have had the time to read the papers I flagged in my LDK17 conference notes with “check paper”. I haven’t yet due to end-of-semester tasks. So, on the basis of just a positive-seeming presentation, here are a few that are on the top of my list to check out first, for quite different reasons:

• Interaction between natural language reading capabilities and math education, focusing on language production (i.e., ‘can you talk about it?’) [6], mainly because math education in South Africa faces a lot of problems. It also generated a lively discussion in the Q&A session.
• The OnLiT ontology for linguistic [7] and LLODifying linguistic glosses [8] terminology (also: one of the two also won the best paper award).
• Deep text generation, for it was looking at trying to address skewed or limited data to learn from [9], which is an issue we face when trying to do some NLP with most South African languages.

 

References

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

[2] Keet, C.M., Khumalo, L. On the verbalization patterns of part-whole relations in isiZulu. 9th International Natural Language Generation conference (INLG’16), September 5-8, 2016, Edinburgh, UK. ACL.

[3] Keet, C.M. Representing and aligning similar relations: parts and wholes in isiZulu vs English. In: Gracia J., Bond F., McCrae J., Buitelaar P., Chiarcos C., Hellmann S. (eds) Language, Data, and Knowledge LDK 2017. Springer LNAI vol 10318, 58-73.

[4] Fillottrani, P.R., Keet, C.M. Patterns for Heterogeneous TBox Mappings to Bridge Different Modelling Decisions. 14th Extended Semantic Web Conference (ESWC’17). Springer LNCS. Portoroz, Slovenia, May 28 – June 2, 2017.

[5] Keet, C.M. Xakaza, M., Khumalo, L. Verbalising OWL ontologies in isiZulu with Python. 14th Extended Semantic Web Conference (ESWC’17). Springer LNCS. Portoroz, Slovenia, May 28 – June 2, 2017. (demo paper)

[6] Crossley, S., Kostyuk, V. Letting the genie out of the lamp: using natural language processing tools to predict math performance. In: Gracia J., Bond F., McCrae J., Buitelaar P., Chiarcos C., Hellmann S. (eds) Language, Data, and Knowledge LDK 2017. Springer LNAI vol 10318, 330-342.

[7] Klimek, B., McCrae, J.P., Lehmann, C., Chiarcos, C., Hellmann, S. OnLiT: and ontology for linguistic terminology. In: Gracia J., Bond F., McCrae J., Buitelaar P., Chiarcos C., Hellmann S. (eds) Language, Data, and Knowledge LDK 2017. Springer LNAI vol 10318, 42-57.

[8] Chiarcos, C., Ionov, M. Rind-Pawlowski, M., Fäth, C., Wichers Schreur, J., Nevskaya. I. LLODifying linguistic glosses. In: Gracia J., Bond F., McCrae J., Buitelaar P., Chiarcos C., Hellmann S. (eds) Language, Data, and Knowledge LDK 2017. Springer LNAI vol 10318, 89-103.

[9] Dethlefs N., Turner A. Deep Text Generation — Using Hierarchical Decomposition to Mitigate the Effect of Rare Data Points. In: Gracia J., Bond F., McCrae J., Buitelaar P., Chiarcos C., Hellmann S. (eds) Language, Data, and Knowledge LDK 2017. Springer LNAI vol 10318, 290-298.

On that “shared” conceptualization and other definitions of an ontology

It’s a topic that never failed to generate a discussion on all 10 instalments of the ontology engineering course I taught from BSc(hons) up to participants studying toward or already having a PhD: those pesky definitions of what an ontology is. To top it off, like I didn’t know, I also got a snarky reviewer’s comment about it on my Stuff ontology paper [1]:

A comment that might be superficial but I cannot help: since an ontology is usually (in Borst’s terms) assumed to be a ‘shared’ conceptualization, I find a little surprising for such a complex model to have been designed by a sole author. While I acknowledge the huge amount of literature carefully analyzed, it still seems that the concrete modeling decisions eventually relied on the background of a single ontologist

Is that bad? Does that make the Stuff Ontology a ‘nontology’? And, by the by, what about all those loner philosophers who write single-author papers on ontology; should that whole field be discarded because most of the ontology insights were “shared” only from paper submission and publication?

Anyway, let’s start from the beginning. There’s the much-criticized definition of an ontology from Gruber that, it seems, only novices seem to keep quoting (to my irritation, indeed):

An ontology is a specification of a conceptualization. [2]

If you wonder why quite a bit has been written about it: try to answer what “specification” really means and how it is specified, and what exactly a “conceptualization” is. The real fun starts with Borst et al.’s [3] and then Studer et al.’s [4] refinement of Gruber’s version, which the reviewer quoted above alluded to:

An ontology is a formal, explicit specification of a shared conceptualization. [4]

At least there’s the “formal” (be it in the sense of logic or formal ontology), and “explicit”, so something is being made explicit and precise. But “shared”? Shared with whom? How? Is a logical theory that not one, but two, people write down an ontology, then? Or one person develops an ontology and then emails it to a few colleagues or puts it online in, say, the open BioPortal ontology repository. Does that count as “shared” then? Or is it only “shared” if at least one other person agrees with it as is (all reviewers of the Stuff Ontology did, btw), or perhaps (most or all of) the ‘conceptualization’ of it but a few axioms would need a bit of tweaking and cleaning up? Do you need at least a group of people to develop an ontology, and if so, how large should that group be, and should that group consist of independent sub-groups that adopt the ontology (and if so, how many endorsers)? Is a lightweight low-hanging-fruit ontology that is used by a large company a real or successful ontology, but a highly axiomatised ontology with a high tangledness that is used by a specialist organization, not? And even if you canvass and get a large group and/or organization to buy into that formal explicit specification, what if they are all wrong on the reality is supposed to represent? Does it still count as an ontology no matter how wrong the conceptualization is, just because it’s formal, explicit, and shared? Is a tailor-made module of, say, the DOLCE ontology not also an ontology, even if the module was made by one person and made available in an online repository like ROMULUS?

Perhaps one shouldn’t start top-down, but bottom-up: take some things and decide (who?) whether it is an ontology or not. Case one: the taxonomy of part-whole relations is a mini-ontology, and although at the start only ‘shared’ with my co-author and published in the Applied Ontology journal [5], it has been used by quite a few researchers for various (and unintended) purposes afterward, notably in NLP (e.g., [6]). An ontology? If so, since when? Case two: Noy et al. converted the representation of the NCI thesaurus into OWL DL [7]. Does changing the serialisation of a multi-authored thesaurus from one format into another make it an ontology? (more on that below.) Case three: a group of 5 people try to represent the subject domain of, say, breast cancer, but it is replete with mistakes both regarding the reality it ought to represent and unintended modelling errors (such as confusing is-a with part-of). Is it still an ontology, albeit a bad one?

It gets more muddled when the representation language is thrown in (as with case 2 above). What if the ontology turns out to be unsatisfiable? From a logic viewpoint, it’s not a theory then (a consistent set of sentences, is), but if it’s formal, explicit, and shared, is it acceptable that those people who developed the artefact simply have an inconsistent conceptualization and that it still counts as an ontology?

Horrocks et al. [8] simplify the whole thing by eliminating the ‘shared’ aspect:

an ontology being equivalent to a Description Logic knowledge base. [8]

However, this generates a set of questions and problems of its own that are practically also problematic. For instance: 1) whether transforming a UML Class Diagram into OWL ‘magically’ makes it an ontology (answer: no); 2) The NCI Thesaurus to OWL (answer: no); or 3) if you used, say, Common Logic to represent it, that then it could not be an ontology because it’s not formalised in Description Logics (answer: it sure can be one).

There are more attempts to give a definition or a description, notably by Nicola Guarino in [9] (a key paper in the field):

An ontology is a logical theory accounting for the intended meaning of a formal vocabulary, i.e. its ontological commitment to a particular conceptualization of the world. The intended models of a logical language using such a vocabulary are constrained by its ontological commitment. An ontology indirectly reflects this commitment (and the underlying conceptualization) by approximating these intended models. [9]

That’s a mouthful, but at least no “shared” in there, either. And, finally, among the many definitions in [10], here’s Barry Smith and cs.’s take on it:

An ONTOLOGY is a representational artifact, comprising a taxonomy as proper part, whose representational units are intended to designate some combination of universals, defined classes, and certain relations between them. [10]

And again, no “shared” either in this definition. Of course, also with Smith’s definition, there are things one can debate about and pose it against Guarino’s definition, like the “universals” vs. “conceptualization” etc., but that’s a story for another time.

So, to sum up: there is that problem on how to interpret “shared”, which is untenable, and one just as well can pick a definition of an ontology from a widely cited paper that doesn’t include that in the definition.

That said, all this doesn’t help my students to grapple with the notion of ‘an ontology’. Examples help, and it would be good if someone, or, say, the International Association for Ontology and its Applications (IAOA) would have a list of “exemplar ontologies” sooner rather than later. (Yes, I have a list, but it still needs to be annotated better). Another aspect that helps explaining it comes is from Guarino’s slides on going “from logical to ontological level” and on good and bad ontologies. This first screenshot (taken from my slides—easier to find) shows there’s “something more” to an ontology than just the logic, with a hint to reasons why (note to my students: more about that later in the course). The second screenshot shows that, yes, we can have the good, bad, and ugly: the yellow oval denotes the intended models (what it should be), and the other ovals denote the various approximations that one may have tried to represent in an ontology. For instance, representing ‘each human has exactly one brain’ is more precise (“good”) than stating ‘each human has at least one brain’ (“less good”) or not saying anything at all about it an ontology of human anatomy (“bad”), and even “worse” it would be if that ontology ware to state ‘each human has exactly two tails’.

Maybe we can’t do better than ‘intuition’ or ‘very wieldy explanation’. If this were a local installation of WordPress, I’d have added a poll on definitions and the subjectivity on the shared-ness factor (though knowing well that science isn’t governed as a democracy). In lieu of that: comments, preferences for one definition or the other, or any better suggestions for definitions are most welcome! (The next instalment of my Ontology Engineering course will start in a few week’s time.)

 

References

[1] Keet, C.M. A core ontology of macroscopic stuff. 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14). K. Janowicz et al. (Eds.). 24-28 Nov, 2014, Linkoping, Sweden. Springer LNAI vol. 8876, 209-224.

[2] Gruber, T. R. A translation approach to portable ontology specifications. Knowledge Acquisition, 1993, 5(2):199-220.

[3] Borst, W.N., Akkermans, J.M. Engineering Ontologies. International Journal of Human-Computer Studies, 1997, 46(2-3):365-406.

[4] Studer, R., Benjamins, R., and Fensel, D. Knowledge engineering: Principles and methods. Data & Knowledge Engineering, 1998, 25(1-2):161-198.

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

[6] Tandon, N., Hariman, C., Urbani, J., Rohrbach, A., Rohrbach, M., Weikum, G.: Commonsense in parts: Mining part-whole relations from the web and image tags. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI’16). pp. 243-250. AAAI Press (2016)

[7] Noy, N.F., de Coronado, S., Solbrig, H., Fragoso, G., Hartel, F.W., Musen, M. Representing the NCI Thesaurus in OWL DL: Modeling tools help modeling languages. Applied Ontology, 2008, 3(3):173-190.

[8] Horrocks, I., Patel-Schneider, P. F., and van Harmelen, F. From SHIQ and RDF to OWL: The making of a web ontology language. Journal of Web Semantics, 2003, 1(1):7.

[9] Guarino, N. (1998). Formal ontology and information systems. In Guarino, N., editor, Proceedings of Formal Ontology in Information Systems (FOIS’98), Frontiers in Artificial intelligence and Applications, pages 3-15. Amsterdam: IOS Press.

[10] Smith, B., Kusnierczyk, W., Schober, D., Ceusters, W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. KR-MED 2006 “Biomedical Ontology in Action”. November 8, 2006, Baltimore, Maryland, USA.

More stuff: relating stuffs and amounts of stuff to their parts and portions

With all the protests going on in South Africa, writing this post is going to be a moment of detachment of it (well, I’m trying), for it concerns foundational aspects of ontologies with respect to “stuff”. Stuff is the philosophers’ funny term for those kind of things that cannot be counted, or only counted in quantities, and are in natural language generally referred to by mass nouns. For instance, water, gold, mayonnaise, oil, and wine as kinds of things, yet one can talk of individual objects of them only in quantities, like a glass of wine, a spoonful of mayonnaise, and a litre of oil. It is one thing to be able to say which types of stuff there are [1], it is another matter how they relate to each other. The latter is described in the paper recently accepted at the 20th International Conference on Knowledge Engineering and Knowledge management (EKAW’16), entitled “Relating some stuff to other stuff” [2].

Is something like that even relevant, when students are protesting for free education, among other demands? Yes. At the end of the day, it is part and parcel of a healthy environment to live in. For instance, one should be able to realise traceability in food and medicine supply chains, to foster practices, and check compliance, of good production processes and supply chains, so that you will not buy food that makes you ill or take medicines that are fake [3,4]. Such production processes and product logistics deal with ‘stuffs’ and their portions and parts that get separated and put together to make the final product. Current implementations have only underspecified ‘links’ (if at all) that doesn’t let one infer automatically what (or who) the culprit is. Existing theoretical accounts from philosophy and in domain ontologies are incomplete, so they wouldn’t help you further either. The research described in the paper solves this issue.

Seven relations for portions and stuff-parts were identified, which have a temporal dimension where needed. For instance, the upper-half of the wine in your wine glass is a portion of the whole amount of wine in the glass, yet that amount of wine was a portion of the amount of wine in the bottle when you opened it, and yet it has as part some amount of alcohol. (Some reader may not find this example nice, for it being with alcohol, but Western Cape, where Cape Town is situated, is the wine region of the country.) The relations are structured in a little hierarchy, as informally depicted in the figure below.

Section of the basic taxonomy of part-whole relations of [5] (less and irrelevant sections in grey or suppressed), extended with the stuff relations and their position in the hierarchy.

Their formal definitions are included in the paper.

Another aspect of the solution is that it distinguishes between 1) the extensional and intensional level—like, between ‘an amount of wine’ and ‘wine’—because different constraints apply (following from that latter can be instantiated the former cannot), and 2) the amount of stuff and the (repeatable) quantity, as one can have 1kg of many things.

Just theory isn’t good enough, though, for one would want to use it in some way to indeed get those benefits of traceability in the supply chains. After considering the implementation options (see paper for details), I settled for an extension to the Stuff Ontology core ontology that now also imports a special purpose module OMmini of the Ontology of Units of Measure (see also the Stuff Ontology page). The latter sounds easier than that it worked in praxis, but that’s a topic of a different post. The module is there, and the links between the OMmin.owl and stuff.owl have been declared.

Although the implementation is atemporal in the end, it is still possible to do some automated reasoning for traceability. This is mainly thought availing of property chains to approximate the relevant temporal aspects. For instance, with then one can infer that a scattered portion in my glass of wine that was a portion of bottle #1234 of organic Pinotage wine of an amount of wine, contained in cask #3, with wine from wine farm X of Stellar Winery from the 2015 harvest is a scattered portion of that amount of matter (that cask). Or take the (high-level) pharmaceutical supply chain from [4]: a portion (that is on a ‘pallet’) of the quantity of medicine produced by the manufacturer goes to the warehouse, of which a portion (in a ‘case’) goes to the distribution centre. From there, a portion ends up on the dispensing shelf, and someone buys it. Then tracing any customer’s portion of medicine—i.e., regardless the actual instance—can be inferred with the following chain:

Sure, the research presented hasn’t solved everything yet, but at least software developers now have a (better) way to automate traceability in supply chains. It also allows one to be more fine-grained in the analysis where a culprit may be, so that there are fewer cases of needless scares. For instance, we know that when there’s an outbreak of Salmonella, then we only have to trace where the batch of egg yolk went (typically in the tiramisu served in homes for the elderly), where it came from (which farm), and got mixed with in the production process, while the amounts of egg white on your lemon merengue still would be safe to eat even when it came from the same batch that had at least one infected egg.

I’ll be presenting the paper at EKAW’16 in November in Bologna, Italy, and hope to see you there! It’s not a good time of the year w.r.t. weather, but that’s counterbalanced by the beauty of the buildings and art works, and the actual venue room is in one of the historical buildings of the oldest university of Europe.

 

References

[1] Keet, C.M. A core ontology of macroscopic stuff. 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14). K. Janowicz et al. (Eds.). 24-28 Nov, 2014, Linkoping, Sweden. Springer LNAI vol. 8876, 209-224.

[2] Keet, C.M. Relating some stuff to other stuff. 20th International Conference on Knowledge Engineering and Knowledge Management EKAW’16). Springer LNAI, 19-23 November 2016, Bologna, Italy. (accepted)

[3] Donnelly, K.A.M. A short communication – meta data and semantics the industry interface: what does the food industry think are necessary elements for exchange? In: Proc. of Metadata and Semantics Research (MTSR’10). Springer CCIS vol. 108, 131-136.

[4] Solanki, M., Brewster, C. OntoPedigree: Modelling pedigrees for traceability in supply chains. Semantic Web Journal, 2016, 7(5), 483-491.

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