# Toward a framework for resolving conflicts in ontologies (with COVID-19 examples)

Among the many tasks involved in developing an ontologies, are deciding what part of the subject domain to include, and how. This may involve selecting a foundational ontology, reuse of related domain ontologies, and more detailed decisions for ontology authoring for specific axioms and design patterns. A recent example of reuse is that of the Infectious Diseases Ontology for schistosomiasis knowledge [1], but even before reuse, one may have to assess differences among ontologies, as Haendel et al did for disease ontologies [2]. Put differently, even before throwing alignment tools at them or selecting one with an import statement and hope for the best, issues may arise. For instance, two relevant domain ontologies may have been aligned to different foundational ontologies, a partOf relation could be set to be transitive in one ontology but is also used in a qualified cardinality constraint in the other (so then one cannot use an OWL 2 DL reasoner anymore when the ontologies are combined), something like Infection may be represented as a class in one ontology but as a property infectedby in another, or the ontologies differ on the science, like whether Virus is an organism or an inanimate object.

What to do then?

Upfront, it helps to be cognizant of the different types of conflict that may arise, and understand what their causes are. Then one would want to be able to find those automatically. And, most importantly, get some assistance in how to resolve them; if possible, also even preventing conflicts from happening in the first place. This is what Rolf Grütter, from the Swiss Federal Research Institute WSL, and I have been working since he visited UCT last year. The first results have been accepted for the International Conference on Biomedical Ontologies (ICBO) 2020, which are described in a paper entitled “Towards a Framework for Meaning Negotiation and Conflict Resolution in Ontology Authoring” [3]. A sample scenario of the process is illustrated informally in the following figure.

Summary of a sample scenario of detecting and resolving conflicts, illustrated with an ontology reuse scenario where Onto2 will be imported into Onto1. (source: [3])

The paper first defines and illustrates the notions of meaning negotiation and conflict resolution and summarises their main causes, to then go into some detail of the various categories of conflicts and ways how to resolve them. The detection and resolution is assisted by the notion of a conflict set, which is a data structure that stores the details for further processing.

It was tested with a use case of an epizootic disease outbreak in the Lemanic Arc in Switzerland in 2006, due to H5N1 (avian influenza): an administrative ontology had to be merged with one about the epidemiology for infected birds and surveillance zones. With that use case in place already well before the spread of SARS-CoV-2 that caused the current pandemic, it was a small step to add a few examples to the paper about COVID-19. This was made possible thanks to recently developed relevant ontologies that were made available, including for COVID-19 specifically. Let’s highlight the examples here, also so that I can write a bit more about it than the terse text in the paper, since there are no page limits for a blog post.

Example 1: OWL profile violations

Medical terminologies tend to veer toward being represented in an ontology language that is less or equal to OWL 2 EL: this permits scalability, compatibility with typical OBO Foundry ontologies, as well as fitting with the popular SNOMED CT. As one may expect, there have been efforts in ontology development with content relevant for the current pandemic; e.g., the Coronavirus Infectious Disease Ontology (CIDO) [4]. The CIDO is not in OWL 2 EL, however: it has a class expressions with a universal quantifier (ObjectAllValuesFrom) on the right-hand side; specifically (in DL notation): ‘Yale New Haven Hospital SARS-CoV-2 assay’ $\sqsubseteq \forall$ ‘EUA-authorized use at’.’FDA EUA-authorized organization’ or, in the Protégé interface:

(codes: CIDO_0000020, CIDO_0000024, and CIDO_0000031, respectively). It also imported many ontologies and either used them to cause some profile violations or the violations came with them, such as by having used the union operator (‘or’) in the following axiom for therapeutic vaccine function (VO_0000562):

How did I find that? Most certainly NOT by manually browsing through the more than 70000 axioms of the CIDO (including imports) to find the needle in the haystack. Instead, I burned the proverbial haystack to easily get the needles. In this case, the burning was done with the OWL Classifier, which automatically computes which axioms violate any of the OWL species, and lists them accordingly. Here are two examples, illustrating an OWL 2 EL violation (that aforementioned universal quantification) and an OWL 2 QL violation (a property chain with entities from BFO and RO); you can do likewise for OWL 2 RL violations.

Following the scenario with the assumption that the CIDO would have to stay in the OWL 2 EL profile, then it is easy to find the conflicting axioms and act accordingly, i.e., remove them. (It also indicates something did not go well with importing the NDF-RT.owl into the cido-base.owl, but that as an aside for this example.)

Example 2: Modelling issues: same idea, different elements

Let’s take the CIDO again and now also the COviD Ontology for cases and patient information (CODO), which have some overlapping and complementary information, so perhaps could be merged. A not unimportant thing is the test for SARS-CoV-2 and its outcome. CODO has a ‘laboratory test finding’ $\equiv$ {positive, pending, negative}, i.e., the possible outcomes of the test are individuals made into a class using the ObjectOneOf constructor. Consulting CIDO for the test outcomes, it has a class ‘COVID-19 diagnosis’ with three subclasses: Negative, Positive, and Presumptive positive. Aside from the inexact matches of the test status that won’t simplify data integration efforts, this is an example of class vs. instance modeling of what is ontologically the same thing. Resolving this in any merging attempt means that either

1. the CODO has to change and bump up the test results from individuals to classes, or
2. the CIDO has to change the subclasses to individuals in the ABox, or
3. take an ‘outside option’ and represent it in yet a different way where both the CODO and the CIDO have to modify the ontology (e.g., take a conceptual data modeling approach by making the test outcome an attribute with a few possible values).

The paper provides an attempt to systematize such type of conflicts toward a library of common types of conflict, so that it should become easier to find them, and offers steps toward a proper framework to manage all that, which assisted with devising generic approaches to resolution of conflicts. We already have done more to realize all that (which could not all be squeezed into the 12 pages), but more is still to be done, so stay tuned.

Since COVID-19 is still doing the rounds and the international borders of South Africa are still closed (with a lockdown for some 5 months already), I can’t end the blog post with the usual ‘I hope to see you at ICBO 2020 in Bolzano in September’—well, not in the common sense understanding at least. Hopefully next year then.

References

[1] Cisse PA, Camara G, Dembele JM, Lo M. An Ontological Model for the Annotation of Infectious Disease Simulation Models. In: Bassioni G, Kebe CMF, Gueye A, Ndiaye A, editors. Innovations and Interdisciplinary Solutions for Underserved Areas. Springer LNICST, vol. 296, 82–91. 2019.

[2] Haendel MA, McMurry JA, Relevo R, Mungall CJ, Robinson PN, Chute CG. A Census of Disease Ontologies. Annual Review of Biomedical Data Science, 2018, 1:305–331.

[3] Grütter R, Keet CM. Towards a Framework for Meaning Negotiation and Conflict Resolution in Ontology Authoring. 11th International Conference on Biomedical Ontologies (ICBO’20), 16-19 Sept 2020, Bolzano, Italy. CEUR-WS (in print).

[4] He Y, Yu H, Ong E, Wang Y, Liu Y, Huffman A, Huang H, Beverley J, Hur J, Yang X, Chen L, Omenn GS, Athey B, Smith B. CIDO, a community-based ontology for coronavirus disease knowledge and data integration, sharing, and analysis. Scientific Data, 2020, 7:181.

# The DiDOn method to develop bio-ontologies from semi-structured life science diagrams

It is well-known among (bio-)ontology developers that ontology development is a resource-consuming task (see [1] for data backing up this claim). Several approaches and tools do exists that speed up the time-consuming efforts of bottom-up ontology development, most notably natural language processing and database reverse engineering. They are generic and the technologies have been proposed from a computing angle, and are therefore noisy and/or contain many heuristics to make them fit for bio-ontology development. Yet, the most obvious one from a domain expert perspective is unexplored: the abundant diagrams in the sciences that function as existing/’legacy’ knowledge representation of the subject domain. So, how can one use them to develop domain ontologies?

The new DiDOn procedure—from Diagram to Domain Ontology—can speed up and simplify bio-ontology development by exploiting the knowledge represented in such semi-structured bio-diagrams. It does this by means of extracting explicit and implicit knowledge, preserving most of the subject domain semantics, and making formalisation decisions explicit, so that the process is done in a clear, traceable, and reproducible way.

DiDOn is a detailed, micro-level, procedure to formalise those diagrams in a logic of choice; it provides migration paths into OBO, SKOS, OWL and some arbitrary FOL, and guidelines which axioms, and how, have to be added to the bio-ontology. It also uses a foundational ontology so as to obtain more precise and interoperable subject domain semantics than otherwise would have been possible with syntactic transformations alone. (Choosing an appropriate foundational ontology is a separate topic and can be done wit, e.g., ONSET.)

The paper describing the rationale and details, Transforming semi-structured life science diagrams into meaningful domain ontologies with DiDOn [2], has just been accepted at the Journal of Biomedical Informatics. They require a graphical abstract, so here it goes:

DiDOn consists of two principal steps: (1) formalising the ‘icon vocabulary’ of a bio-drawing tool, which then functions as a seed ontology, and (2) populating the seed ontology by processing the actual diagrams. The algorithm in the second step is informed by the formalisation decisions taken in the first step. Such decisions include, among others, the representation language and how to represent the diagram’s n-aries (with n≥2, such as choosing between n-aries as relationship or reified as classes).

In addition to the presentation of DiDOn, the paper contains a detailed application of it with Pathway Studio as case study.

The neatly formatted paper is behind a paywall for those with no or limited access to Elsevier’s journals, but the accepted manuscript is openly accessible from my home page.

References

[1] Simperl, E., Mochol, M., Bürger, T. Achieving maturity: the state of practice in ontology engineering in 2009. International Journal of Computer Science and Applications, 2010, 7(1):45-65.

[2] Keet, C.M. Transforming semi-structured life science diagrams into meaningful domain ontologies with DiDOn. Journal of Biomedical Informatics. In print. DOI: http://dx.doi.org/10.1016/j.jbi.2012.01.004

# Bottom-up ontology development using bio-diagrams

Development of (bio-)ontologies takes up a lot of resources, especially when conducted manually. This is a well-known hurdle to overcome, and various strategies and tools for bottom-up ontology development have been proposed from a computing angle, such as the reverse engineering of databases and, most prominently in the bio-ontologies area, natural language processing (NLP) (e.g. [1,2] and a review by [3]). Both, however, generate a rather crude, noisy, and simple ontology that requires substantial manual intervention to clean up and to add ‘missing’ knowledge. Nevertheless, NLP provides at least a set of terms one can start with instead of starting with an empty screen and adding everything de novo. There is, however, a way to have your cake and eat it too: exploiting the plentiful diagrams in the life sciences.

Diagrams are very important in biology, and from early on in the education, students are taught to read and draw them. There is even a rule of thumb that one should be able to understand an article by reading the abstract, conclusions, and diagrams alone. Diagrams also summarise the accompanying text, or even can tell more than what is explained in the text. That much from the biology side. They can be useful from the computing angle as well. They are at least semi-structured (compared to natural language), with conventions about depicting lipid bi-layers, DNA, sequences of interactions by means of arrows, and so forth, and over the years more and more drawing applications have been developed. The nice thing (still for computing) is that those tools have an ‘alphabet’—legend—with permissible icons and colours and how they can be used in the diagrams. There are many diagrams that represent our understanding of biological reality.

Now, imagine that those diagrams can be transferred into an ontology in one fell swoop, and subsequently used for whatever purpose ontologies are being used (such as annotation, consistency checking, and finding implicit knowledge). And because those diagrams are more structured than natural language, we can obtain a richer ontology than with NLP alone—with less effort.

How?

One thing is recognizing there’s much to be gained in improving bottom-up bio-ontology development by availing of such diagrams (already observed in [4]), another thing is how to go about doing this in the most effective way—not for just one diagram tool, but for any one. This problem I aim to tackle in the paper “Bottom-up ontology development reusing semi-structured life sciences diagrams”, which was recently accepted for the AFRICON’11 Special Session on Robotics and AI in Africa. This 6-page paper is a very condensed version of its 12-page draft, so not everything could be included. Nevertheless, it does give the basics of the method to formalize bio-diagrams in an ontology and a use case to demonstrate it.

The approach consists of a four-stage process: (i) choosing the appropriate language (OBO, SKOS, OWL, and arbitrary FOL are considered), (ii) inclusion of a foundational ontology (DOLCE, BFO, RO etc.), (iii) formalizing the icons of the diagram tool’s ‘legend’ (e.g., ‘enzyme’), and (iv) devising an algorithm to populate the TBox to mine the actual diagrams so that the individual components (e.g., ‘protease’) end up in the right position in the ontology. The main details are described in the paper.

Thus, this bottom-up method is not one of only formalising ‘legacy’ information, but also takes into account subject domain semantics that can be represented better by using a foundational ontology during the principal transformation of the diagram’s vocabulary. In addition to the more precise, formal, representation of the subject domain semantics, the use of a foundational ontology also increases interoperability.

The guidelines are demonstrated with a transformation of the Pathway Studio [6] diagrams into an OWLized (OWL 2 DL) bio-ontology with BFO and RO.

As an aside (from my perspective), it may be of interest to note that such formalized diagrams then can be deployed also as intermediate representation of the knowledge, which can facilitate understanding and communication between logicians and domain experts. And, for the financially challenged: it can bring the information modelled in such diagrams, which are often locked in expensive hardcopy textbooks and pay-per-view scientific articles, into the open access domain for free use and reuse.

References

[1] Alexopoulou D, Wachter T, Pickersgill L, Eyre C, Schroeder M. Terminologies for text-mining: an experiment in the lipoprotein metabolism domain. BMC Bioinformatics 2008;9(Suppl 4).

[2] Coulet A, Shah NH, Garten Y, Musen M, Altman RB. Using text to build semantic networks for pharmacogenomics. Journal of Biomedical Informatics 2010;43(6):1009-19.

[3] Liu K, Hogan WR, Crowley RS. Natural language processing methods and systems for biomedical ontology learning. Journal of Biomedical Informatics 2011;44(1):163-79.

[4] Keet CM. Factors affecting ontology development in ecology. In: Ludaescher B, Raschid L, editors. Data Integration in the Life Sciences 2005 (DILS2005); vol. 3615 of LNBI. Springer Verlag; 2005, p. 46-62. San Diego, USA, 20-22 July 2005.

[5] Keet CM. Bottom-up ontology development reusing semi-structured life sciences diagrams. AFRICON’11 — Special Session on Robotics and Artificial Intelligence in Africa, Livingstone, Zambia 13-15 September, 2011. IEEE (to appear).

[6] Nikitin A, Egorov S, Daraselia N, Mazo I. Pathway studio—the analysis and navigation of molecular networks. Bioinformatics 2003;19(16):2155-2157.

# Ontological realism, methodologies, and mud slinging: a few notes on the AO trilogy

In July at the start of the MOWS’10 course on ontology engineering I pointed to more background literature about the debate about ontology as reality representation, its principle references, the new comprehensive assessment on its problems by Gary Merrill [1], and I included the note from the Applied Ontology Journal editors that Barry Smith and Werner Ceusters were writing a comprehensive rebuttal, to which Merrill would response in turn. They’re out now [2,3], and also freely available through the dedicated AO page.

On cursory glance seeing some juicy sentences, Smith and Ceusters’ 50-page reply [2] seemed like a good pastime to read on the gray, rainy, and cold Sunday afternoon last week and to ponder if and how I would incorporate it in an updated version of the ontology engineering course. It, however, contains many harsh statements with the main message that they’re doing a great thing with their so-called “realist methodology” and that Merrill’s critique is irrelevant. Merrill’s 30-page response to that [3], which I finished reading recently, is that Smith and Ceusters’ clarification made matters worse and thereby confirming it is a misdirection.

So, what to make of all that? If I were a VIP in ontology engineering, I would ask the AO editors to write a proper reply to the Smith and Ceusters’ (BS & WC) paper. But I am not; hence, I will mention a few aspects on my blog only (which might me do more harm than good, but I hope not). I will start with a note on realism, then the usage of the term “application ontologies”, and finally claims about BS & WC’s “realist methodology” that is not a methodology.

Notes on realism

On the realism dimension of the debate, I have not much to say. I subscribe to the, what Merrill formulates as the, “Empiricist Doctrine” [1], which states that “the terms of science… are to be taken to refer to the actually existing entities in the real world”, especially when it comes to ontologies for the life sciences and (bio)medicine. If you want an ontology of deities, fairies, or other story characters, that’s fine, too—just do not put them in a bio-ontology. What I had understood from the conversations, presentations, and papers of BS & WC is that if you accept the “Empiricist Doctrine”, then so you must go along with universals (as opposed to concepts). Merrill calls the latter component the “Universalist Doctrine” where “the terms of science… are to be understood as referring directly to universals”, which is one of many metaphysical stances [1]. I do not know if I subscribe to universals and I do not care about that that much. Although I did some philosophy of science and philosophy of nature a while ago and read up on other subjects in philosophy in the past few years, I am not a philosopher by training and do not know about all intricacies of all alternatives around (but maybe I should).

Another reason for my misunderstanding—or: conflating the two doctrines—is also due to the fact that descriptions and definitions in the BS & WC papers are not consistent throughout (elaborated on by Merrill [1,3]). For instance, in [4], ontology is taken as reality representations, but in [2] it is reality representation that is described by science, i.e., as scientists understand it, or in other words: the representation of the theories. Thus, where the things in the ontology are terms that do not have a ‘direct link’ to the actual entities, but they go through the scientists’ mind with their conceptualizations of reality. This is quite a difference from [4]. Make of it what you like.

Last, the ‘funny’ thing is that when you use the Empiricists Doctrine it does not matter if you use BFO, DOLCE, GFO, or whichever foundational ontology for practical ontology development. The current formalisations of BFO, DOLCE or any of the others do not have in their formalisation that the categories [unary predicates] denote either universals or concepts. Clearly, the communication of the informal intentions would be different if the top (OWL:thing or similar) in the ontology is called Universal or Concept, but in BFO it is called Entity and in DOLCE it is Particular. Thus, de facto, neither one commits to one philosophical doctrine or another in the top-level categorization and formalisation.

What are “application ontologies”?

Smith and Ceusters in [4] make a distinction between reference ontologies and application ontologies, the former intended to represent “settled science” and latter that part of science that is in flux. This rather difficult to maintain distinction is discussed at length in [3]. What I wish to add, and which was only mentioned in passing in [3], is that the notion of ‘application ontologies’ elsewhere in the ontologies enterprise is used quite differently. It refers to OWL- or DL-formalised conceptual data models modeled in one of the common conceptual data modelling languages (UML, EER, ORM), but not real ontologies. The discussion about the difference between an ontology and a conceptual data model is beyond the current scope, but it is important to note that the same term means something different in pretty much all other literature about ontologies. Perhaps BS & WC have not read that literature, given that they happily attack computer science, knowledge engineering, and conceptual modelling (section 3.1 in [2]) with ‘justifications’ that Wüster-the-businessman over at ISO is a telling example of knowledge engineering and conceptual modelling (he is not), and that it was the training in cognitive psychology we all got as computer scientists (we did not) that makes us confused and stick to concepts instead of buying into universalist doctrine. Such statements are not helpful.

Either way, application ontology as a formal conceptual data model is definitely a more tenable definition [setting aside if one agrees with it] than application ontology for the non-settled science for the fact that there is no crisp boundary between settled and non-settled science. If the vague distinction is not enough already to complicate the debate: concepts are allowed to appear in BS & WC’s application ontologies.

About “methodologies”

Smith and Ceusters propose their “realist methodology” in section 1 of [2], but a methodology it is not—at least, not in the sense I, and (m)any other people in CS & IT, use the term. What BS & WC put forward is a set of principles. It does not say what to do, how, and when. And there is no empirical validation that the resultant ontologies are better (validation sensu a proper scientific experiment with subjects with/without using the ‘methodology’, measurable quality criteria, statistically significant, etc.).

An example of a fairly straightforward methodology for ontology development is METHONTOLOGY (among others [5]), and a more recent one for collaborative distributed ontology development: the NeON Methodology [6]. The latter has a nice fairly comprehensive overview picture of the interactions of the different steps (see Fig. 1, below) that are described further in [6] (and an aspect of this are the interactions between the different steps [7]). In my lectures, I like to be impartial and include a variety of options to sensitise  ontology developers to the plethora of options (see, e.g., Sections 3 and 4 of the MOWS’10 course, which is an updated version of SemWebTech lecture 3+4, where the what comes before the how, outlined in SemWebTech lecture 5: Methods and Methodologies), but a set of principles that is labeled “methodology” is not something that fits in a real methodology section (though they may well fit in another module).

How can BS & WC even dare to propose a methodology for ontology development when disregarding all literature on ontology development (except for the OntoClean method)? If their methodology is so superior, than give me evidence why and how it is better than all the methodologies that have been proposed over the past 15 years or so. Spoon-feed me about the shortcomings of those procedures; that is, not a lecture about the realist vs anti-realist, conceptualist and what have you, but why I should not buy into collecting non-ontological resources, looking at ontology design patterns, providing intermediate steps for the formalization, and so forth.

Whilst reading section 1 of [2], I have been trying to extract a methodology—that is, reading it with a positive attitude to try to make something of it—but could find little, and what I extracted from it, is not enough for practical ontology development and maintenance. As example, let us take the step of  “non-ontological resource reuse” for the chosen subject domain. In an ontology engineering methodology, this includes options, such as assessing chosen sources such as relevant thesauri, databases, natural language text, and methods for each option, i.e., the how-to to reuse the non-ontological resources, such as the manual database reverse engineering steps vs. semi-automated tools (in, say, VisioModeler, or the Protégé plugin Lubyte  developed [8]), data mining and clustering, the different methods to extract terms from text etc. From [2], e.g. section 1.13, I gather that the only way to execute this step of “non-ontological resource reuse” is that domain experts manually read the scientific literature and manually add the knowledge to the ontology. No help from, say, the KEGG, AGROVOC, ICD10, or ontologies that were already developed by other groups—all that should be ignored—let alone automating anything to find, say, candidate terms automatically with NLP tools. That surely must be a joke (or oversight, or sheer ignorance) and does not reflect what happens during the development of OBO ontologies. Or take, e.g., METHONTOLOGY or MoKi’s stage of intermediate representations between de domain expert’s informal representation and the formalisation of it in a suitable logic language, such as pseudo-natural language, diagrams as syntactic sugar for the underlying logic, the Protégé and OBOEdit ODEs: are they to be ignored, too? Of course not; well, I presume that that is not the intention of BS & WC’s “methodology”.

They may have enjoyed having written a trashing of 20 years of knowledge engineering and conceptual data modelling whose outputs apparently can be ignored, but there surely is room to learn a thing or two about it. After reading up on the related works on methodologies, they can make a real attempt at developing a methodology that satisfies the set of principles, be that by developing a methodology from scratch or integrating it into (or extending) existing methodologies. Until then, what is presented in section 1 of [2] will not—cannot—be added to a ‘methods and methodologies’ module in an ontology engineering course.

P.S.: Other views

A different online debate about realism in ontology engineering can be read over at Phil Lord’s blog (The Status quo farewell tour on realism, Why not?, and Why realism is wrong) and his paper together with Robert Stevens at PLoS ONE [9], versus David Sutherland’s Realism, Really? and Yes, really in favour of the realist approach for practical ontology development. Then there is the OBO-Foundry discussion list, and, e.g., a paper in FOIS’10 by Michel Dumontier and Robert Hoehndorf [10], and undoubtedly more papers about the issues raised in the AO trilogy will follow.

References

[1] Gary H. Merrill. Ontological realism: Methodology or misdirection? Applied Ontology, 5 (2010) 79–108.

[2] Barry Smith and Werner Ceusters. Ontological realism: A methodology for coordinated evolution of scientific ontologies. Applied Ontology, 5 (2010) 79–108.

[3] Gary H. Merrill. Realism and reference ontologies: Considerations, reflections and problems. Applied Ontology, 5 (2010) 79–108.

[4] Barry Smith. Beyond Concepts, or: Ontology as Reality Representation. Achille Varzi and Laure Vieu (eds.), Formal Ontology and Information Systems. Proceedings of the Third International Conference (FOIS 2004), Amsterdam: IOS Press, 2004, 73-84.

[5] Corcho, O., Fernandez-Lopez, M. and Gomez-Perez, A. (2003). Methodologies, tools and languages for building ontologies. Where is their meeting point?. Data & Knowledge Engineering 46(1): 41-64.

[6] Mari Carmen Suarez-Figueroa, Guadalupe Aguado de Cea, Carlos Buil, Klaas Dellschaft, Mariano Fernandez-Lopez, Andres Garcia, Asuncion Gomez-Perez, German Herrero, Elena Montiel-Ponsoda, Marta Sabou, Boris Villazon-Terrazas, and Zheng Yufei. NeOn Methodology for Building Contextualized Ontology Networks. NeOn Deliverable D5.4.1. 2008.

[8] Lina Lubyte. Techniques and Tools for the Design of Ontologies for Data Access. PhD Thesis, Free University of Bozen-Bolzano, KRDB Dissertation Series DS-2010-02, 2010.

[9] Lord, P. & Stevens, R. Adding a little reality to building ontologies for biology. PLoS One, 2010, 5(9), e12258. DOI: 10.1371/journal.pone.0012258.

[10] Dumontier, M. & Hoehndorf, R. Realism for scientific ontologies. In: Proceeding of the Conference on Formal Ontology in Information Systems: Proceedings of the Sixth International Conference (FOIS 2010), 387–399. Amsterdam: IOS Press.

Fig 1. Graphical depiction of different steps in ontology development, where each step has its methods and interactions with other steps (taken from 6).

# A strike against the ‘realism-based approach’ to ontology development

The ontology engineering course starting this Monday at the Knowledge Representation and Reasoning group at Meraka commences with the question What is an ontology? In addition to assessing definitions, it touches upon long-standing disagreements concerning if ontologies are about representing reality, our conceptualization of entities in reality, or some conceptualization that does not necessarily ascribe to existence of reality. The “representation of reality school” is advocated in ontology engineering most prominently by Barry Smith and cs. and their foundational ontology BFO, the “conceptualization of entities in reality school” by various people and research groups, such as the LOA headed by Nicola Guarino and their DOLCE foundational ontology, whereas the “conceptualization irrespective regardless reality school” can be (but not necessarily is) encountered in organisations developing, e.g., medical ontologies that do not ascribe to evidence-based medicine to decide what goes in the ontology and how (but instead base it on, say, the outcome of power plays between big pharma and health insurance companies).

Due to the limited time and scope of this and previous courses on ontology engineering I taught, I mention[ed] only succinctly that those differences exist (e.g., pp10-11 of the UH slides), and briefly illustrate some of the aspects of the debate and their possible consequences in practical aspects of ontology engineering. This information is largely based on a few papers and extracting consequences from that, the examples they describe and that I encountered, and the discussions that took place at the various meetings, workshops, conferences, and summer schools that I participated in. But there was no nice, accessible, paper that describes de debate—or even part of it—more precisely and is readable also by ontologists who are not philosophers. Until last week, that is. The Applied Ontology journal published a paper by Gary Merrill, entitled Ontological realism: Methodology or misdirection? [1], that assess critically the ontological realism advocated by Barry Smith and his colleague Werner Ceusters. Considering its relevance in ontology engineering, the article has been made freely available, and in the announcement of the journal issue, its editors in chief (Nicola Guarino and Mark Musen) mentioned that Smith and Ceusters are busy preparing a response on Merrill’s paper, which will be published in a subsequent issue of Applied Ontology. Merrill, in turn, promised to respond to this rebuttal.

But for now, there are 30 pages of assessment on the merits of, and problems with, the philosophical underpinnings of the “realism-based approach” that is used in particular in the realm of ontology engineering within the OBO Foundry project and its large set of ontologies, BFO, and the Relation Ontology. The abstract gives an idea of the answer to the question in the paper’s title:

… The conclusion reached is that while Smith’s and Ceusters’ criticisms of prior practice in the treatment of ontologies and terminologies in medical informatics are often both perceptive and well founded, and while at least some of their own proposals demonstrate obvious merit and promise, none of this either follows from or requires the brand of realism that they propose.

The paper’s contents backs this up with analysis, arguments, examples, and bolder statements than the abstracts suggests.
For anyone involved in ontology development and interested in the debate—even if you think you’re tired of it—I recommend reading the paper, and to at least follow how the debate will unfold with responses and rebuttals.

My opinion? Well, I have one, of course, but this post is an addendum to the general course page of MOWS’10, hence I try to refrain from adding too much bias to the course material.

UPDATE (27-7-2010): On whales and apples, and on ontology and reality: you might enjoy also “Moby Dick: an exercise in ontology”, written by Lorne A. Smith.

References

[1] Gary H. Merrill. Ontological realism: Methodology or misdirection? Applied Ontology, 5 (2010) 79–108.

# Teaching ontology engineering at Uni de la Habana in April

I mentioned in an earlier post about Informatica 2009 that the Cuban Government has decided to support the knowledge society with respect to informatics; read, e.g., the transcript of the speech by Commander of the Revolution and Minister of Informatics and Communications Ramiro Valdés Menéndez. Clearly, this includes ontologies and, to a greater or lesser extent, the Semantic Web, and the desire to develop local capacities in this area. To make a long story short, it took a while to find the time, choose the topics, and figure out the bureaucratic aspects, but—and with many thanks due to Rafael Oliva Santos’ efforts on the Cuban side of the organisation—finally I’ll be on my way to Cuba this Sunday to teach a course on ontology engineering at the Universidad de la Habana from 5 to 16 April.

For the curious among you, I have put the handouts of the course slides together into one pdf. They are not meant as a summary, but instead intended to give some structure in the flow of information and a place for some examples so that students are not completely absorbed with writing down what I’ll chalk up on the board (and so that I do not spend too much time on trying to make pretty slides). Nevertheless, it does give an idea about the topics that will pass the revue in the limited time, such as top-down and bottom-up ontology development, differences between conceptual models and ontologies, methods, ontology design parameters and their interactions, methodologies, and OWL (yes, some sections of the SWT course are reused and extended with more ontology engineering). In addition, there is an associated lab and a mini-project to get hands-on experience in ontology engineering.

The internet connection being what it is in Cuba, I will only sporadically check the blog and emails during my stay until the end of April 2010 (and I do hope this blog post will not be spammed as much as the previous ones about Cuba).

# 72010 SemWebTech lecture 9: Successes and challenges for ontologies in the life sciences

To be able to talk about successes and challenges of SWT for health care and life sciences (or any other subject domain), we first need to establish when something can be deemed a success, when it is a challenge, and when it is an outright failure. Such measures can be devised in an absolute sense (compare technology x with an SWT one: does it outperform on measure y?) and relative (to whom is technology x deemed successful?) Given these considerations, we shall take a closer look at several attempts, being two successes and a few challenges in representation and reasoning. What were the problems and how did they solve it, and what are the problems and can that be resolved, respectively?

As success stories we take the experiments by Wolstencroft and coauthors about classifying protein phosphatases [1] and Calvanese et al for graphical, web-based, ontology-based data access applied to horizontal gene transfer data [2]. They each focus on different ontology languages and reasoning services to solve different problems. What they have in common is that there is an interaction between the ontology and instances (and that it was a considerable amount of work by people with different specialties): the former focuses on classifying instances and the latter on querying instances. In addition, modest results of biological significance have been obtained with the classification of the protein phosphatases, whereas with the ontology-based data analysis we are tantalizingly close.

The challenges for SWT in general and for HCLS in particular are quite diverse, of which some concern the SWT proper and others are by its designers—and W3C core activities on standardization—considered outside their responsibility but still need to be done. Currently, for the software aspects, the onus is put on the software developers and industry to pick up on the proof-of-concept and working-prototype tools that have come out of academia and to transform them into the industry-grade quality that a widespread adoption of SWT requires. Although this aspect should not be ignored, we shall focus on the language and reasoning limitations during the lecture.

In addition to the language and corresponding reasoning limitations that passed the revue in the lectures on OWL, there are language “limitations” discussed and illustrated at length in various papers, with the most recent take [3], where it might well be that the extensions presented in lecture 6 and 7 (parts, time, uncertainty, and vagueness) can ameliorate or perhaps even solve the problem. Some of the issues outlined by Schultz and coauthors are ‘mere’ modelling pitfalls, whereas others are real challenges that can be approximated to a greater or lesser extent. We shall look at several representation issues that go beyond the earlier examples of SNOMED CT’s “brain concussion without loss of consciousness”; e.g. how would you represent in an ontology that in most but not all cases hepatitis has as symptom fever, or how would you formalize the defined concept “Drug abuse prevention”, and (provided you are convinced it should be represented in an ontology) that the world-wide prevalence of diabetes mellitus is 2.8%?

Concerning challenges for automated reasoning, we shall look at two of the nine identified required reasoning scenarios [4], being the “model checking (violation)” and “finding gaps in an ontology and discovering new relations”, thereby reiterating that it is the life scientists’ high-level goal-driven approach and desire to use OWL ontologies with reasoning services to, ultimately, discover novel information about nature. You might find it of interest to read about the feedback received from the SWT developers upon presenting [4] here: some requirements are met in the meantime and new useful reasoning services were presented.

References

[1] Wolstencroft, K., Stevens, R., Haarslev, V. Applying OWL reasoning to genomic data. In: Semantic Web: revolutionizing knowledge discovery in the life sciences, Baker, C.J.O., Cheung, H. (eds), Springer: New York, 2007, 225-248.

[2] Calvanese, D., Keet, C.M., Nutt, W., Rodriguez-Muro, M., Stefanoni, G. Web-based Graphical Querying of Databases through an Ontology: the WONDER System. ACM Symposium on Applied Computing (ACM SAC’10), March 22-26 2010, Sierre, Switzerland.

[3] Stefan Schulz, Holger Stenzhorn, Martin Boekers and Barry Smith. Strengths and Limitations of Formal Ontologies in the Biomedical Domain. Electronic Journal of Communication, Information and Innovation in Health (Special Issue on Ontologies, Semantic Web and Health), 2009.

[4] Keet, C.M., Roos, M. and Marshall, M.S. A survey of requirements for automated reasoning services for bio-ontologies in OWL. Third international Workshop OWL: Experiences and Directions (OWLED 2007), 6-7 June 2007, Innsbruck, Austria. CEUR-WS Vol-258.

[5] Ruttenberg A, Clark T, Bug W, Samwald M, Bodenreider O, Chen H, Doherty D, Forsberg K, Gao Y, Kashyap V, Kinoshita J, Luciano J, Scott Marshall M, Ogbuji C, Rees J, Stephens S, Wong GT, Elizabeth Wu, Zaccagnini D, Hongsermeier T, Neumann E, Herman I, Cheung KH. Advancing translational research with the Semantic Web, BMC Bioinformatics, 8, 2007.

p.s.: the first part of the lecture on 21-12 will be devoted to the remaining part of last week’s lecture; that is, a few discussion questions about [5] that are mentioned in the slides of the previous lecture.

Note: references 1 and 3 are mandatory reading, 2 and 4 recommended to read, and 5 was mandatory for the previous lecture.

Lecture notes: lecture 9 – Successes and challenges for ontologies

Course website

# 72010 SemWebTech lecture 8: SWT for HCLS background and data integration

After the ontology languages and general aspects of ontology engineering, we now will delve into one specific application area: SWT for health care and life sciences. Its frontrunners in bioinformatics were adopters of some of the Semantic Web ideas even before Berners-Lee, Hendler, and Lassila wrote their Scientific American paper in 2001, even though they did not formulate their needs and intentions in the same terminology: they did want to have shared, controlled vocabularies with the same syntax, to facilitate data integration—or at least interoperability—across Web-accessible databases, have a common space for identifiers, it needing to be a dynamic, changing system, to organize and query incomplete biological knowledge, and, albeit not stated explicitly, it all still needed to be highly scalable [1].

Bioinformaticians and domain experts in genomics already organized themselves together in the Gene Ontology Consortium, which was set up officially in 1998 to realize a solution for these requirements. The results exceeded anyone’s expectations in its success for a range of reasons. Many tools for the Gene Ontology (GO) and its common KR format, .obo, have been developed, and other research groups adopted the approach to develop controlled vocabularies either by extending the GO, e.g., rice traits, or adding their own subject domain, such as zebrafish anatomy and mouse developmental stages. This proliferation, as well as the OWL development and standardization process that was going on at about the same time, pushed the goal posts further: new expectations were put on the GO and its siblings and on their tools, and the proliferation had become a bit too wieldy to keep a good overview what was going on and how those ontologies would be put together. Put differently, some people noticed the inferencing possibilities that can be obtained from moving from obo to OWL and others thought that some coordination among all those obo bio-ontologies would be advantageous given that post-hoc integration of ontologies of related and overlapping subject domains is not easy. Thus came into being the OBO Foundry to solve such issues, proposing a methodology for coordinated evolution of ontologies to support biomedical data integration [2].

People in related disciplines, such as ecology, have taken on board experiences of these very early adopters, and instead decided to jump on board after the OWL standardization. They, however, were not only motivated by data(base) integration. Referring to Madin et al’s paper [3] again, I highlight three points they made: “terminological ambiguity slows scientific progress, leads to redundant research efforts, and ultimately impedes advances towards a unified foundation for ecological science”, i.e., identification of some serious problems they have in ecological research; “Formal ontologies provide a mechanism to address the drawbacks of terminological ambiguity in ecology”, i.e., what they expect that ontologies will solve for them (disambiguation); and “and fill an important gap in the management of ecological data by facilitating powerful data discovery based on rigorously defined, scientifically meaningful terms”, i.e., for what purpose they want to use ontologies and any associated computation (discovery). That is, ontologies not as a—one of many possible—tool in the engineering/infrastructure means, but as a required part of a method in the scientific investigation that aims to discover new information and knowledge about nature (i.e., in answering the who, what, where, when, and how things are the way they are in nature).

What has all this to do with actual Semantic Web technologies? On the one hand, there are multiple data integration approaches and tools that have been, and are being, tried out by the domain experts, bioinformaticians, and interdisciplinary-minded computer scientists [4], and, on the other hand, there are the W3C Semantic Web standards XML, RDF(S), SPARQL, and OWL. Some use these standards to achieve data integration, some do not. Since this is a Semantic Web course, we shall take a look at two efforts who (try to) do, which came forth from the activities of the W3C’s Health Care and Life Sciences Interest Group. More precisely, we take a closer look at a paper written about 3 years ago [5] that reports on a case study to try to get those Semantic Web Technologies to work for them in order to achieve data integration and a range of other things. There is also a more recent paper from the HCLS IG [6], where they aimed at not only linking of data but also querying of distributed data, using a mixture of RDF triple stores and SKOS. Both papers reveal their understanding of the purposes of SWT, and, moreover, what their goals are, their experimentation with various technologies to achieve them, and where there is still some work to do. There are notable achievements described in these, and related, papers, but the sought-after “killer app” is yet to be announced.

The lecture will cover a ‘historical’ overview and what more recent ontology-adopters focus on, the very basics of data integration approaches that motivated the development of ontologies, and we shall analyse some technological issues and challenges mentioned in [5] concerning Semantic Web (or not) technologies.

References:

[1] The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nature Genetics, May 2000;25(1):25-9.

[2] Barry Smith, Michael Ashburner, Cornelius Rosse, Jonathan Bard, William Bug, Werner Ceusters, Louis J. Goldberg, Karen Eilbeck, Amelia Ireland, Christopher J Mungall, The OBI Consortium, Neocles Leontis, Philippe Rocca-Serra, Alan Ruttenberg, Susanna-Assunta Sansone, Richard H Scheuermann, Nigam Shah, Patricia L. Whetzel, Suzanna Lewis. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology 25, 1251-1255 (2007).

[3] Joshua S. Madin, Shawn Bowers, Mark P. Schildhauer and Matthew B. Jones. (2008). Advancing ecological research with ontologies. Trends in Ecology & Evolution, 23(3): 159-168.

[4] Erhard Rahm. Data Integration in Bioinformatics and Life Sciences. EDBT Summer School, Bolzano, Sep. 2007.

[5] Ruttenberg A, Clark T, Bug W, Samwald M, Bodenreider O, Chen H, Doherty D, Forsberg K, Gao Y, Kashyap V, Kinoshita J, Luciano J, Scott Marshall M, Ogbuji C, Rees J, Stephens S, Wong GT, Elizabeth Wu, Zaccagnini D, Hongsermeier T, Neumann E, Herman I, Cheung KH. Advancing translational research with the Semantic Web, BMC Bioinformatics, 8, 2007.

[6] Kei-Hoi Cheung, H Robert Frost, M Scott Marshall, Eric Prud’hommeaux, Matthias Samwald, Jun Zhao, and Adrian Paschke. A journey to Semantic Web query federation in the life sciences. BMC Bioinformatics 2009, 10(Suppl 10):S10

Note: references 1, 2, and (5 or 6) are mandatory reading, and 3 and 4 are recommended to read.

Lecture notes: lecture 8 – SWLS background and data integration

Course website