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 <expression> 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

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