# An architecture for Knowledge-driven Information and Data access: KnowID

Advanced so-called ‘intelligent’ information systems may use an ontology or runtime-suitable conceptual data modelling techniques in the back end combined with efficient data management. Such a set-up aims to provide a way to better support informed decision-making and data integration, among others. A major challenge to create such systems, is to figure out which components to design and put together to realise a ‘knowledge to data’ pipeline, since each component and process has trade-offs; see e.g., the very recent overview of sub-topics and challenges [1]. A (very) high level categorization of the four principal approaches is shown in the following figure: put the knowledge and data together in the logical theory the AI way (left) or the database way (right), or bridge it by means of mappings or by means of transformations (centre two):

Among those variants, one can dig into considerations like which logic to design or choose in the AI-based “knowledge with (little) data” (e.g.: which OWL species? common logic? Other?), which type of database (relational, object-relational, or rather an RDF store), which query language to use or design, which reasoning services to support, how expressive it all has to and optimized for what purpose. None is best in all deployment scenarios. The AI-only one with, say, OWL 2 DL, is not scalable; the database-only one either lacks interesting reasoning services or supports few types of constraints.

Among the two in the middle, the “knowledge mapping data” is best known under the term ‘ontology-based data access’ (OBDA) and the Ontop system in particular [2] with its recent extension into ‘virtual knowledge graphs’ and the various use cases [3]. Its distinguishing characteristic of the architecture is the mapping layer to bridge the knowledge to the data. In the “Data transformation knowledge” approach, the idea is to link the knowledge to the data through a series of transformations. No such system is available yet. Considering the requirements for that, it turned out that a good few components are already available and just needed one crucial piece of transformations to convincingly put that together.

We did just that and devised a new knowledge-to-data architecture. We dub this the KnowID architecture (pronounced as ‘know it’), abbreviated from Knowledge-driven Information and Data access. KnowID adds novel transformation rules between suitably formalised EER diagrams as application ontology and Borgida, Toman & Weddel’s Abstract Relational Model with SQLP ([4,5]) to complete the pipeline (together with some recently proposed other components). Overall, it then looks like this:

Its details are described in the article entitled “KnowID: an architecture for efficient Knowledge-driven Information and Data access” [6], which was recently publish in the Data Intelligence journal. In a nutshell: the logic-based EER diagram (with deductions materialised) is transformed into an abstract relational model (ARM) that is transformed into a traditional relational model and then onward to a database schema, where the original ‘background knowledge’ of the ARM is used for data completion (i.e., materializing the deductions w.r.t. the data), and then the query posed in SQLP (SQL + path queries) is answered over that ‘extended’ database.

Besides the description of the architecture and the new transformation rules, the open access journal article also describes several examples and it features a more detailed comparison of the four approaches shown in figure 1 above. For KnowID, compared to other ontology-based data access approaches, its key distinctive architectural features are that runtime use can avail of full SQL augmented with path queries, the closed world assumption commonly used in information systems, and it avoids a computationally costly mapping layer.

We are working on the implementation of the architecture. The transformation rules and corresponding algorithms were implemented last year [7] and two computer science honours students are currently finalising their 4th-year project, therewith contributing to the materialization and query formulation steps aspects of the architecture. The latest results are available from the KnowID webpage. If you were to worry that will suffer from link rot: the version associated with the Data Intelligence paper has been archived as supplementary material of the paper at [8]. The plan is, however, to steadily continue with putting the pieces together to make a functional software system.

References

[1] Schneider, T., Šimkus, M. Ontologies and Data Management: A Brief Survey. Künstl Intell 34, 329–353 (2020).

[2] Calvanese, D., Cogrel, B., Komla-Ebri, S., Kontchakov, R., Lanti, D., Rezk, M., Rodriguez-Muro, M., Xiao, G.: Ontop: Answering SPARQL queries over relational databases. Semantic Web Journal, 2017, 8(3), 471-487.

[3] G. Xiao, L. Ding, B. Cogrel, & D. Calvanese. Virtual knowledge graphs: An overview of systems and use cases. Data Intelligence, 2019, 1, 201-223.

[4] A. Borgida, D. Toman & G.E. Weddell. On referring expressions in information systems derived from conceptual modeling. In: Proceedings of ER’16, 2016, pp. 183–197

[5] W. Ma, C.M. Keet, W. Oldford, D. Toman & G. Weddell. The utility of the abstract relational model and attribute paths in SQL. In: C. Faron Zucker, C. Ghidini, A. Napoli & Y. Toussaint (eds.) Proceedings of the 21st International Conference on Knowledge Engineering and Knowledge Management (EKAW’18)), 2018, pp. 195–211.

[6] P.R. Fillottrani & C.M. Keet. KnowID: An architecture for efficient knowledge-driven information and data access. Data Intelligence, 2020 2(4), 487–512.

[7] Fillottrani, P.R., Jamieson, S., Keet, C.M. Connecting knowledge to data through transformations in KnowID: system description. Künstliche Intelligenz, 2020, 34, 373-379.

[8] Pablo Rubén Fillottrani, C. Maria Keet. KnowID. V1. Science Data Bank. http://www.dx.doi.org/10.11922/sciencedb.j00104.00015. (2020-09-30)

# 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.

# A requirements catalogue for ontology languages

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

Expressiveness/constructs/modelling features

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

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

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

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

Features of the language as a whole

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

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

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

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

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

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

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

F-8 Specified with a particular semantics.

F-9 Should not make arbitrary assumptions about semantics.

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

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

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

F-13 Have a query language for the ontology.

F-14 Declared with Closed World Assumption.

F-15 Declared with Open World Assumption.

F-16 Use Unique Name Assumption.

F-17 Do not use Unique Name Assumption.

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

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

Usability by computer

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

UC-2 Be usable on the Web.

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

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

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

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

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

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

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

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

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

Usability & modelling by humans

HU-1 Easy to use

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

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

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

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

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

Interaction with outside

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

I-2 Interoperable (with what?)

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

I-4 Support an open networks of ontologies

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

I-6 Option ot declare inter-ontology assertions

Ontological decisions

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

O-2 3-Dimensionalist with a temporal extension

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

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

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

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

O-7 Statements are either true or false

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

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

O-10 Ontology and natural language are intertwined

That’s all, for now.

# A set of competency questions and SPARQL-OWL queries, with analysis

As a good beginning of the new year, our Data in Brief article Dataset of Ontology Competency Questions to SPARQL-OWL Queries Translations [1] was accepted and came online this week, which accompanies our Journal of Web Semantics article Analysis of Ontology Competency Questions and their Formalisations in SPARQL-OWL [2] that was published in December 2019—with ‘our’ referring to my collaborators in Poznan, Dawid Wisniewski, Jedrzej Potoniec, and Agnieszka Lawrynowicz, and myself. The former article provides extensive detail of a dataset we created that was subsequently used for analysis that provided new insights that is described in the latter article.

The dataset

In short, we tried to find existing good TBox-level competency questions (CQs) for available ontologies and manually formulate (i.e., formalise the CQ in) SPARQL-OWL queries for each of the CQs over said ontologies. We ended up with 234 CQs for 5 ontologies, with 131 accompanying SPARQL-OWL queries. This constitutes the first gold standard pipeline for verifying an ontology’s requirements and it presents the systematic analyses of what is translatable from the CQs and what not, and when not, why not. This may assist in further research and tool development on CQs, automating CQ verification, assessing the main query language constructs and therewith language optimisation, among others. The dataset itself is indeed independently reusable for other experiments, and has been reused already [3].

The key insights

The first analysis we conducted on it, reported in [2], revealed several insights. First, a larger set of CQs (cf. earlier work) indeed did increase the number of CQ patterns. There are recurring patterns in the shape of the CQs, when analysed linguistically; a popular one is What EC1 PC1 EC2? obtained from CQs like “What data are collected for the trail making test?” (a Dem@care CQ). Observe that, yes, indeed, we did decouple the language layer from the formalisation layer rather than mixing the two; hence, the ECs (resp. PCs) are not necessarily classes (resp. object properties) in an ontology. The SPARQL-OWL queries were also analysed at to what is really used of that query language, and used most often (see table 7 of the paper).

Second, these characteristics are not the same across CQ sets by different authors of different ontologies in different subject domains, although some patterns do recur and are thus somehow ‘popular’ regardless. Third, the relation CQ (pattern or not) : SPARQL-OWL query (or its signature) is m:n, not 1:1. That is, a CQ may have multiple SPARQL-OWL queries or signatures, and a SPARQL-OWL query or signature may be put into a natural language question (CQ) in different ways. The latter sucks for any aim of automated verification, but unfortunately, there doesn’t seem to be an easy way around that: 1) there are different ways to say the same thing, and 2) the same knowledge can be represented in different ways and therewith leading to a different shape of the query. Some possible ways to mitigate either is being looked into, like specifying a CQ controlled natural language [3] and modelling styles [4] so that one might be able to generate an algorithm to find and link or swap or choose one of them [5,6], but all that is still in the preliminary stages.

Meanwhile, there is that freely available dataset and the in-depth rigorous analysis, so that, hopefully, a solution may be found sooner rather than later.

References

[1] Potoniec, J., Wisniewski, D., Lawrynowicz, A., Keet, C.M. Dataset of Ontology Competency Questions to SPARQL-OWL Queries Translations. Data in Brief, 2020, in press.

[2] Wisniewski, D., Potoniec, J., Lawrynowicz, A., Keet, C.M. Analysis of Ontology Competency Questions and their Formalisations in SPARQL-OWL. Journal of Web Semantics, 2019, 59:100534.

[3] Keet, C.M., Mahlaza, Z., Antia, M.-J. CLaRO: a Controlled Language for Authoring Competency Questions. 13th Metadata and Semantics Research Conference (MTSR’19). 28-31 Oct 2019, Rome, Italy. Springer CCIS vol 1057, 3-15.

[4] Fillottrani, P.R., Keet, C.M. Dimensions Affecting Representation Styles in Ontologies. 1st Iberoamerican conference on Knowledge Graphs and Semantic Web (KGSWC’19). Springer CCIS vol 1029, 186-200. 24-28 June 2019, Villa Clara, Cuba. Paper at Springer

[5] 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 vol 10249, 371-386. Portoroz, Slovenia, May 28 – June 2, 2017.

[6] Khan, Z.C., Keet, C.M. Automatically changing modules in modular ontology development and management. Annual Conference of the South African Institute of Computer Scientists and Information Technologists (SAICSIT’17). ACM Proceedings, 19:1-19:10. Thaba Nchu, South Africa. September 26-28, 2017.

# More and better TDD for ontology authoring

Test-driven development (TDD) for ontology authoring [1] has received attention previously, including its accompanying tool TDDOnto [2] that was subsequently improved upon into the (also open source) TDDonto2 tool [3]. The TDDonto2 demo paper [3] did not contain the technical details about the new-and-improved algorithms and specification for TDD testing that we claimed it had. They are published just now in the International Journal on Artificial Intelligence Tools, as the article entitled More Effective Ontology Authoring with Test-Driven Development and the TDDonto2 tool [4]. The better algorithms cover more OWL language features than the original v1 of the theory and tool and it includes a specification for TDD testing such that there is not just pass/fail/absent as test result, but specific outcomes of the TDD test that are more informative, like that the ontology will become incoherent if that axiom were to be added. Given that model, the general flow for a simple standard case of a single TDD test (though more axioms can be tested at once) is as follows:

simplified view of the extended TDD process (source: adapted from [4])

The elements in the figure that are coloured light grey are the steps covered by the specification for TDD testing, algorithms, and TDDonto2 tool that is introduced in the paper.

The paper’s title clearly also hints to another contribution: using TDDonto2 for ontology authoring is significantly more effective. It was compared against the commonly used (and test-last) Protégé interface, which showed that the participants completed a larger part of the task in less time and with fewer mistakes. It also requires fewer interactions (clicking and typing) in the interface, which we reported on in an earlier (longer) tech report [5].

screenshot of the outcome of running the four tests on the sample ontology, in TDDonto2

As usual with research, more can be done. This is especially with respect to the white boxes in the figure above, i.e., the other aspects that would contribute toward a complete TDD methodology for ontology development. One step that we have been working on, is the idea of turning competency questions into axioms for TDD, which now is doable from CQ to SPARQL-OWL query [6] (more about that later), a CNL that may contribute to the authoring [7], and trying to figure out the modelling styles more precisely [8], since they hamper automation of these first steps in the process to get those axioms into the TDD plugin in a user-friendly way.

References

[1] Keet, C.M., Lawrynowicz, A. Test-Driven Development of Ontologies. 13th Extended Semantic Web Conference (ESWC’16). Springer LNCS vol. 9678, 642-657. 29 May – 2 June, 2016, Crete, Greece.

[2] Lawrynowicz, A., Keet, C.M. The TDDonto Tool for Test-Driven Development of DL Knowledge bases. 29th International Workshop on Description Logics (DL’16). April 22-25, Cape Town, South Africa. CEUR WS vol. 1577.

[3] Davies, K. Keet, C.M., Lawrynowicz, A. TDDonto2: A Test-Driven Development Plugin for arbitrary TBox and ABox axioms. The Semantic Web: ESWC 2017 Satellite Events, Blomqvist, E et al. (eds.). Springer LNCS vol 10577, 120-125. Portoroz, Slovenia, May 28 – June 2, 2017.

[4] Davies, K., Keet, C.M., Lawrynowicz, A. More Effective Ontology Authoring with Test-Driven Development and the TDDonto2 tool. International Journal on Artificial Intelligence Tools, 2019, 28(7): 1950023.

[5] Keet, C.M., Davies, K., Lawrynowicz, A. More Effective Ontology Authoring with Test-Driven Development. Technical Report 1812.06015. December 2018

[6] Wisniewski, D., Potoniec, J., Lawrynowicz, A., Keet, C.M. Analysis of Ontology Competency Questions and their Formalisations in SPARQL-OWL. Journal of Web Semantics. (in print)

[7] Keet, C.M., Mahlaza, Z., Antia, M.-J. CLaRO: a Controlled Language for Authoring Competency Questions. 13th Metadata and Semantics Research Conference (MTSR’19). 28-31 Oct 2019, Rome, Italy. Springer CCIS. (in print)

[8] Fillottrani, P.R., Keet, C.M.. Dimensions Affecting Representation Styles in Ontologies. 1st Iberoamerican conference on Knowledge Graphs and Semantic Web (KGSWC’19). Springer CCIS vol. 1029, 186-200. 23-30 June 2019, Villa Clara, Cuba.

# Localising Protégé with Manchester syntax into your language of choice

Some people like a quasi natural language interface in ontology development tools, which is why Manchester Syntax was proposed [1]. A downside is that it locks the ontology developer into English, so that weird chimaeras are generated in the interface if the author prefers another language for the ontology, such as, e.g., the “jirafa come only (oja or ramita)” mentioned in an earlier post and that was deemed unpleasant in an experiment a while ago [2]. Those who prefer the quasi natural language components will have to resort to localising Manchester syntax and the tool’s interface.

This is precisely what two of my former students—Adam Kaliski and Casey O’Donnell—did during their mini-project in the ontology engineering course of 2017. A localisation in Afrikaans, as the case turned out to be. To make this publicly available, Michael Harrison brushed up the code a bit and tested it worked also in the new version of Protégé. It turned out it wasn’t that easy to localise it to another language the way it was done, so one of my PhD students, Toky Raboanary, redesigned the whole thing. This was then tested with Spanish, and found to be working. The remainder of the post describes informally some main aspects of it. If you don’t want to read all that but want to play with it right away: here are the jar files, open source code, and localisation instructions for if you want to create, say, a French or Dutch variant.

Some sensible constraints, some slightly contrived ones (and some bad ones), for the purpose of showing the localisation of the interface for the various keywords. The view in English is included in the screenshot to facilitate comparison.

Some sensible constraints, some slightly contrived ones (and some bad ones), for the purpose of showing the localisation of the interface for the various keywords. The view in English is included in the screenshot to facilitate comparison.

The localisation functions as a plugin for Protégé as a ‘view’ component. It can be selected under “Windows – Views – Class views” and then Beskrywing for the Afrikaans and Descripción for Spanish, and dragged into the desired position; this is likewise for object properties.

Instead of burying the translations in the code, they are specified in a separate XML file, whose content is fetched during the rendering. Adding a new ‘simple’ (more about that later) language merely amounts to adding a new XML file with the translations of the Protégé labels and of the relevant Manchester syntax. Here are the ‘simple’ translations—i.e., where both are fixed strings—for Afrikaans for the relevant tool interface components:

 Class Description (Label) Klasbeskrywing (Label in Afrikaans) Equivalent To Dieselfde as SubClass Of Subklas van General Class axioms Algemene Klasaksiomas SubClass Of (Anonymous Ancestor) Subklas van (Naamlose Voorvader) Disjoint With Disjunkte van Disjoint Union Of Disjunkte Unie van

The second set of translations is for the Manchester syntax, so as to render that also in the target language. The relevant mappings for Afrikaans class description keywords are listed in the table below, which contain the final choices made by the students who developed the original plugin. For instance, min and max could have been rendered as minimum and maksimum, but the ten minste and by die meeste were deemed more readable despite being multi-word strings. Another interesting bit in the translation is negation, where there has to be a second ‘no’ since Afrikaans has double negation in this construction, so that it renders it as nie <expression> nie. That final rendering is not grammatically perfect, but (hopefully) sufficiently clear:

An attempt at double negation with a fixed string

An attempt at double negation with a fixed string

 Manchester OWL Keyword Afrikaans Manchester OWL Keyword or phrase some sommige only slegs min ten minste max by die meeste exactly precies and en or of not nie nie SubClassOf SubklasVan EquivalentTo DieselfdeAs DisjointWith DisjunkteVan

The people involved in the translations for the object properties view for Afrikaans are Toky, my colleague Tommie Meyer (also at UCT), and myself; snyding for ‘intersection’ sounds somewhat odd to me, but the real tough one to translate was ‘SuperProperty’. Of the four options that were considered—SuperEienskap, SuperVerwantskap, SuperRelasie, and SuperVerband SuperVerwantskap was chosen with Tommie having had the final vote, which is also a semantic translation, not a literal translation.

Screenshot of the object properties description, with comparison to the English

The Spanish version also has multi-word strings, but at least does not do double negation. On the other hand, it has accents. To generate the Spanish version, myself, my collaborator Pablo Fillottrani from the Universidad Nacional del Sur, Argentina, and Toky had a go at it in translating the terms. This was then implemented with the XML file. In case you do not want to dig into the XML file and not install the plugin either, but have a quick look at the translations, they are as follows for the class description view:

 Class Description Label Descripción (in Spanish) Equivalent To Equivalente a SubClass Of Subclase de General Class axioms Axiomas generales de clase SubClass Of (Anonymous Ancestor) Subclase de (Ancestro Anónimo) Disjoint With Disjunto con Disjoint Union Of Unión Disjunta de Instances Instancias

 Manchester OWL Keyword Spanish Manchester OWL Keyword some al menos uno only sólo min al mínimo max al máximo and y or o not no exactly exactamente SubClassOf SubclaseDe EquivalentTo EquivalenteA DisjointWith DisjuntoCon

And here’s a rendering of a real ontology, for geo linked data in Spanish, rather than African wildlife yet again:

screenshot of the plugin behaviour with someone else’s ontology in Spanish

One final comment remains, which has to do with the ‘simple’ mentioned above. The approach of localisation presented here works only with fixed strings, i.e., the strings do not have to change depending on the context where it is uses. It won’t work with, say, isiZulu—a highly agglutinating and inflectional language—because isiZulu doesn’t have fixed strings for the Manchester syntax keywords nor for some other labels. For instance, ‘at least one’ has seven variants for nouns in the singular, depending on the noun class of the noun of the OWL class it quantifies over; e.g., elilodwa for ‘at least one’ apple, and esisodwa for ‘at least one’ twig. Also, the conjugation of the verb for the object property depends on the noun class of the noun of the OWL class, but in this case for the one that plays the subject; e.g., it’s “eats” in English for both humans and elephants eating, say, fruit, so one string for the name of the object property, but that’s udla and idla, respectively, in isiZulu. This requires annotations of the classes with ontolex-lemon or a similar approach and a set of rules (which we have, btw) to determine what to do in which case, which requires on-the-fly modifications to Manchester syntax keywords and elements’ names or labels. And then there’s still phonological conditioning to account for. It surely can be done, but it is not as doable as with the ‘simple’ languages that have at least a disjunctive orthography and much less genders or noun classes for the nouns.

In closing, while there’s indeed more to translate in the Protégé interface in order to fully localise it, hopefully this already helps as-is either for reading at least a whole axiom in one’s language or as stepping stone to extend it further for the other terms in the Manchester syntax and the interface. Feel free to extend our open source code.

References

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

[2] Keet, C.M. The use of foundational ontologies in ontology development: an empirical assessment. 8th Extended Semantic Web Conference (ESWC’11), G. Antoniou et al (Eds.), Heraklion, Crete, Greece, 29 May-2 June, 2011. Springer LNCS 6643, 321-335.

[3] Keet, C.M., Khumalo, L. Toward a knowledge-to-text controlled natural language of isiZulu. Language Resources and Evaluation, 2017, 51:131-157. accepted version

# Design rationale and overview of the African Wildlife tutorial ontologies

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

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

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

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

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

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

The AWO is freely available under a CC-BY licence through the textbook’s webpage at https://people.cs.uct.ac.za/~mkeet/OEbook/ in this folder. A more comprehensive description of the requirements, design, and content is described in a technical report [1] for the time being.

References

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

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

# DL notation plugin for Protégé 5.x

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

# About modelling styles in ontologies

As any modeller will know, there are pieces of information or knowledge that can be represented in different ways. For instance, representing ‘marriage’ as class or as a ‘married to’ relationship, adding ‘address’ as an attribute or a class in one’s model, and whether ‘employee’ will be positioned as a subclass of ‘person’ or as a role that ‘person’ plays. In some cases, there a good ontological arguments to represent it in one way or the other, in other cases, that’s less clear, and in yet other cases, efficiency is king so that the most compact way of representing it is favoured. This leads to different design decisions in ontologies, which hampers ontology reuse and alignment and affects other tasks, such as evaluating competency questions over the ontology and verbalising ontologies.

When such choices are made consistently throughout the ontology, one may consider this to be a modelling style or representation style. If one then knows which style an ontology is in, it would simplify use and reuse of the ontology. But what exactly is a representation style?

While examples are easy to come by, shedding light on that intuitive notion turned out to be harder than it looked like. My co-author Pablo Fillottrani and I tried to disentangle it nonetheless, by characterising the inherent features and the dimensions by which a style may differ. This resulted in 28 different traits for the 10 identified dimensions.  For instance, the dimension “modular vs. monolithic” has three possible options: 1) ‘Monolithic’, where the ontology is stored in one file (no imports or mergers); 2) ‘Modular, external’, where at least one ontology is imported or merged, and it kept its URI (e.g., importing DOLCE into one’s domain ontology, not re-creating it there); 3) ‘Modular, internal’, where there’s at least one ontology import that’s based on having carved up the domain in the sense of decomposition of the domain (e.g., dividing up a domain into pizzas and drinks at pizzerias).  Other dimensions include, among others, the granularity of relations (many of few), how the hierarchy looks like, and attributes/data properties.

We tried to “eat our own dogfood” and applied the dimensions and traits to a set of 30 ontologies. This showed that it is feasible to do, although we needed two rounds to get to that stage—after the first round of parallel annotation, it turned out we had interpreted a few traits differently, and needed to refine the number of traits and be more precise in their descriptions (which we did). Perhaps unsurprising, some tendencies were observed, and we could identify three easily recognisable types of ontologies because most ontologies had clearly one or the other trait and similar values for sets of trait. Of course, there were also ontologies that were inherently “mixed” in the sense of having applied different and conflicting design decisions within the same ontology, or even included two choices. Coding up the results, we generated two spider diagrams that visualise that difference. Here’s one:

Details of the dimensions, traits, set-up and results of the evaluation, and discussion thereof have been published this week [1] and we’ll present it next month at the 1st Iberoamerican Conference on Knowledge Graphs and Semantic Web (KGSWC’19), in Villa Clara, Cuba, alongside 13 other papers on ontologies. I’m looking forward to it!

References

[1] Keet, C.M., Fillottrani, P.R.. Dimensions Affecting Representation Styles in Ontologies. 1st Iberoamerican conference on Knowledge Graphs and Semantic Web (KGSWC’19). Springer CCIS vol 1029, 186-200. 24-28 June 2019, Villa Clara, Cuba. Paper at Springer

# Some experiences on making a textbook available

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

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

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

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

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

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

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

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

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

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

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

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

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