Conference report: SWAT4HCLS 2022

The things one can do when on sabbatical! For this week, it’s mainly attending the 13th Semantic Web Applications and tools for Health Care and Life Science (SWAT4HCLS) conference and even having some time to write a conference report again. (The last lost tagged with conference report was FOIS2018, at the end of my previous sabbatical.) The conference consisted of a tutorial day, two conference days with several keynotes and invited talks, paper presentations and poster sessions, and the last day a ‘hackathon’/unconference. This clearly has grown over the years from the early days of the event series (one day, workshop, life science).

A photo of the city where it was supposed to take place: Leiden (NL) (Source: here)

It’s been a while since I looked in more detail into the life sciences and healthcare semantics-driven software ecosystems. The problems are largely the same, or more complex, with more technologies and standards to choose from that promise that this time it will be solved once and for all but where practitioners know it isn’t that easy. And lots of tooling for SARS-CoV-2 and COVID-19, of course. I’ll summarise and comment on a few presentations in the remainder of this post.

Keynotes

The first keynote speaker was Karin Verspoor from RMIT in Melbourne, Australia, who focussed her talk on their COVID-SEE tool [1], a Scientific Evidence Explorer for COVID-19 information that relies on advanced NLP and some semantics to help finding information, notably taking open questions where the sentence is analysed by PICO (population, intervention, comparator, outcome) or part thereof, and using UMLS and MetaMap to help find more connections. In contrast to a well-known domain with well-known terminology to formulate very specific queries over academic literature, that was (and still is) not so for COVID-19. Their “NLP+” approach helped to get better search results.

The second keynote was by Martina Summer-Kutmon from Maastricht University, the Netherlands, who focussed on metabolic pathways and computation and is involved in WikiPathways. With pretty pictures, like the COVID-19 Disease map that culminated from a lot of effort by many research communities with lots of online data resources [2]; see also the WikiPathways one for covid, where the work had commenced in February 2020 already. She also came to the idea that there’s a lot of semantics embedded in the varied pathway diagrams. They collected 64643 diagrams from the literature of the past 25 years, analysed them with ML, OCR, and manual curation, and managed to find gaps between information in those diagrams and the databases [3]. It reminded me of my own observations and work on that with DiDOn, on how to get information from such diagrams into an ontology automatically [4]. There’s clearly still lots more work to do, but substantive advances surely have been made over the past 10 years since I looked into it.

Then there were Mirjam van Reisen from Leiden UMC, the Netherlands, and Francisca Oladipo from the Federal University of Lokoja, Nigeria, who presented the VODAN-Africa project that tries to get Africa to buy into FAIR data, especially for COVID-19 health monitoring within this particular project, but also more generally to try to get Africans to share data fairly. Their software architecture with tooling is open source. Apart from, perhaps, South Africa, the disease burden picture for, and due to, COVID-19, is not at all clear in Africa, but ideally would be. Let me illustrate this: the world-wide trackers say there are some 3.5mln infections and 90000+ COVID-19 deaths in South Africa to date, and from far away, you might take this at face value. But we know from SA’s data at the SAMRC that deaths are about three times as much; that only about 10% of the COVID-19-positives are detected by the diagnostics tests—the rest doesn’t get tested [asymptomatic, the hassle, cost, etc.]; and that about 70-80% of the population already had it at least once (that amounts to about 45mln infected, not the 3.5mln recorded), among other things that have been pieced together from multiple credible sources. There are lots of issues with ‘sharing’ data for free with The North, but then not getting the know-how with algorithms and outcomes etc back (a key search term for that debate has become digital colonialism), so there’s some increased hesitancy. The VODAN project tries to contribute to addressing the underlying issues, starting with FAIR and the GDPR as basis.

The last keynote at the end of the conference was by Amit Shet, with the University of South Carolina, USA, whose talk focussed on how to get to augmented personalised health care systems, with as one of the cases being asthma. Big Data augmented with Smart Data, mainly, combining multiple techniques. Ontologies, knowledge graphs, sensor data, clinical data, machine learning, Bayesian networks, chatbots and so on—you name it, somewhere it’s used in the systems.

Papers

Reporting on the papers isn’t as easy and reliable as it used to be. Once upon a time, the papers were available online beforehand, so I could come prepared. Now it was a case of ‘rock up and listen’ and there’s no access to the papers yet to look up more details to check my notes and pad them. I’m assuming the papers will be online accessible soon (CEUR-WS again presumably). So, aside from our own paper, described further below, all of the following is based on notes, presentation screenshots, and any Q&A on Discord.

Ruduan Plug elaborated on the FAIR & GDPR and querying over integrated data within that above-mentioned VODAN-Africa project [5]. He also noted that South Africa’s PoPIA is stricter than the GDPR. I’m suspecting that is due to the cross-border restrictions on the flow of data that the GDPR won’t have. (PoPIA is based on the GDPR principles, btw).

Deepak Sharma talked about FHIR with RDF and JSON-LD and ShEx and validation, which also related to the tutorial from the preceding day. The threesome Mercedes Arguello-Casteleiro, Chloe Henson, and Nava Maroto presented a comparison of MetaMap vs BERT in the context of covid [6], which I have to leave here with a cliff-hanger, because I didn’t manage to make a note of which one won because I had to go to a meeting that we were already starting later because of my conference attendance. My bet would be on the semantics (those deep learning models probably need more reliable data than there is available to date).

Besides papers related to scientific research into all things covid, another recurring topic was FAIR data—whether it’s findable, accessible, interoperable, and reusable. Fuqi Xu  and collaborators assessed 11 features for FAIR vocabularies in practice, and how to use them properly. Some noteworthy observations were that comparing a FAIR level makes more sense before-and-after changing a single resource compared to pitting different vocabularies against each other, “FAIR enough” can be enough (cf. demanding 100% compliance) [7], and a FAIR vocabulary does not imply that it is also a good quality vocabulary. Arriving at the topic of quality, César Bernabé presented an analysis on the use of foundational ontologies in bioinformatics by means of a systematic literature mapping. It showed that they’re used in a range of activities of ontology engineering, there’s not enough empirical analysis of the pros and cons of using one, and, for the numbers game: 33 of the ontologies described in the selected literature used BFO, 16 DOLCE, 7 GFO, and 1 SUMO [8]. What to do next with these insights remains to be seen.

Last, but not least—to try to keep the blog post at a sort of just about readable length—our paper, among the 15 that were accepted. Frances Gillis-Webber, a PhD student I supervise, did most of the work surveying OWL Ontologies in BioPortal on whether, and if so how, they take into account the notion of multilingualism in some way. TL;DR: they barely do [9]. Even when they do, it’s just with labels rather than any of the language models, be they the ontolex-lemon from the W3C community group or another, and if so, mainly French and German.

Source: [9]

Does it matter? It depends on what your aims are. We use mainly the motivation of ontology verbalisation and electronic health records with SNOMED CT and patient discharge note generation, which ideally also would happen for ‘non-English’. Another use case scenario, indicated by one of the participants, Marco Roos, was that the bio-ontologies—not just health care ones—could use it as well, especially in the case of rare diseases, where the patients are more involved and up-to-date with the science, and thus where science communication plays a larger role. One could argue the same way for the science about SARS-CoV-2 and COVID-19, and thus that also the related bio-ontologies can do with coordinated multilingualism so that it may assist in better communication with the public. There are lots of opportunities for follow-up work here as well.

Other

There were also posters where we could hang out in gathertown, and more data and ontologies for a range of topics, such as protein sequences, patient data, pharmacovigilance, food and agriculture, bioschemas, and more covid stuff (like Wikidata on COVID-19, to name yet one more such resource). Put differently: the science can’t do without the semantic-driven tools, from sharing data, to searching data, to integrating data, and analysis to develop the theory figuring out all its workings.

The conference was supposed to be mainly in person, but then on 18 Dec, the Dutch government threw a curveball and imposed a relatively hard lockdown prohibiting all in-person events effective until, would you believe, 14 Jan—one day after the end of the event. This caused extra work with last-minute changes to the local organisation, but in the end it all worked out online. Hereby thanks to the organising committee to make it work under the difficult circumstances!

References

[1] Verspoor K. et al. Brief Description of COVID-SEE: The Scientific Evidence Explorer for COVID-19 Related Research. In: Hiemstra D., Moens MF., Mothe J., Perego R., Potthast M., Sebastiani F. (eds). Advances in Information Retrieval. ECIR 2021. Springer LNCS, vol 12657, 559-564.

[2] Ostaszewski M. et al. COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms. Molecular Systems Biology, 2021, 17:e10387.

[3] Hanspers, K., Riutta, A., Summer-Kutmon, M. et al. Pathway information extracted from 25 years of pathway figures. Genome Biology, 2020, 21,273.

[4] Keet, C.M. Transforming semi-structured life science diagrams into meaningful domain ontologies with DiDOn. Journal of Biomedical Informatics, 2012, 45(3): 482-494. DOI: dx.doi.org/10.1016/j.jbi.2012.01.004.

[5] Ruduan Plug, Yan Liang, Mariam Basajja, Aliya Aktau, Putu Jati, Samson Amare, Getu Taye, Mouhamad Mpezamihigo, Francisca Oladipo and Mirjam van Reisen: FAIR and GDPR Compliant Population Health Data Generation, Processing and Analytics. SWAT4HCLS 2022. online/Leiden, the Netherlands, 10-13 January 2022.

[6] Mercedes Arguello-Casteleiro, Chloe Henson, Nava Maroto, Saihong Li, Julio Des-Diz, Maria Jesus Fernandez-Prieto, Simon Peters, Timothy Furmston, Carlos Sevillano-Torrado, Diego Maseda-Fernandez, Manoj Kulshrestha, John Keane, Robert Stevens and Chris Wroe, MetaMap versus BERT models with explainable active learning: ontology-based experiments with prior knowledge for COVID-19. SWAT4HCLS 2022. online/Leiden, the Netherlands, 10-13 January 2022.

[7] Fuqi Xu, Nick Juty, Carole Goble, Simon Jupp, Helen Parkinson and Mélanie Courtot, Features of a FAIR vocabulary. SWAT4HCLS 2022. online/Leiden, the Netherlands, 10-13 January 2022.

[8] César Bernabé, Núria Queralt-Rosinach, Vitor Souza, Luiz Santos, Annika Jacobsen, Barend Mons and Marco Roos, The use of Foundational Ontologies in Bioinformatics. SWAT4HCLS 2022. online/Leiden, the Netherlands, 10-13 January 2022.

[9] Frances Gillis-Webber and C. Maria Keet, A Survey of Multilingual OWL Ontologies in BioPortal. SWAT4HCLS 2022. online/Leiden, the Netherlands, 10-13 January 2022.

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.

Table 1. Summary of typical possible biases in ontologies grouped by source, with an indication whether such biases would be explicit choices or whether they may creep in unintentionally and lead to implicit bias. (Source: [5])

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.

Table 2. Tentative presence of bias in the three COVID-19 ontologies, by cognitive bias; see paper for details.

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:

Figure 1. OBDI scenario with the CIDO, two database, and a query over the system that returns a logically correct but undesirable result due to some optimism that an experimental substance is already a drug.

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)