# Progress on generating educational questions from ontologies

With increasing student numbers, but not as much more funding for schools and universities, and the desire to automate certain tasks anyhow, there have been multiple efforts to generate and mark educational exercises automatically. There are a number of efforts for the relatively easy tasks, such as for learning a language, which range from the entry level with simple vocabulary exercises to advanced ones of automatically marking essays. I’ve dabbled in that area as well, mainly with 3rd-year capstone projects and 4th-year honours project student projects [1]. Then there’s one notch up with fact recall and concept meaning recall questions, and further steps up, such as generating multiple-choice questions (MCQs) with not just obviously wrong distractors but good distractors to make the question harder. There’s quite a bit of work done on generating those MCQs in theory and in tooling, notably [2,3,4,5]. As a recent review [6] also notes, however, there are still quite a few gaps. Among others, about generalisability of theory and systems – can you plug in any structured data or knowledge source to question templates – and the type of questions. Most of the research on ‘not-so-hard to generate and mark’ questions has been done for MCQs, but there are multiple of other types of questions that also should be doable to generate automatically, such as true/false, yes/no, and enumerations. For instance, with an axiom such as $impala \sqsubseteq \exists livesOn.land$ in a ontology or knowledge graph, a suitable question generation system may then generate “Does an impala live on land?” or “True or false: An impala lives on land.”, among other options.

We set out to make a start with tackling those sort of questions, for the type-level information from an ontology (cf. facts in the ABox or knowledge graph). The only work done there, when we started with it, was for the slick and fancy Inquire Biology [5], but which did not have their tech available for inspection and use, so we had to start from scratch. In particular, we wanted to find a way to be able to plug in any ontology into a system and generate those non-MCQ other types of educations questions (10 in total), where the questions generated are at least grammatically good and for which the answers also can be generated automatically, so that we get to automated marking as well.

Initial explorations started in 2019 with an honours project to develop some basics and a baseline, which was then expanded upon. Meanwhile, we have some more designed, developed, and evaluated, which was written up in the paper “Generating Answerable Questions from Ontologies for Educational Exercises” [7] that has been accepted for publication and presentation at the 15th international conference on metadata and semantics research (MTSR’21) that will be held online next week.

In short:

• Different types of questions and the answer they have to provide put different prerequisites on the content of the ontology with certain types of axioms. We specified those for 10 types of educational questions.
• Three strategies of question generation were devised, being ‘simple’ from the vocabulary and axioms and plug it into a template, guided by some more semantics in the ontology (a foundational ontology), and one that didn’t really care about either but rather took a natural language approach. Variants were added to cater for differences in naming and other variations, amounting to 75 question templates in total.
• The human evaluation with questions generated from three ontologies showed that while the semantics-based one was slightly better than the baseline, the NLP-based one gave the best results on syntactic and semantic correctness of the sentences (according to the human evaluators).
• It was tested with several ontologies in different domains, and the generalisability looks promising.

To be honest to those getting their hopes up: there are some issues that cause it never to make it to the ‘100% fabulous!’ if one still wants to designs a system that should be able to take any ontology as input. A main culprit is naming of elements in the ontology, which varies widely across ontologies. There are several guidelines for how to name entities, such as using camel case or underscores, and those things easily can be coded into an algorithm, indeed, but developers don’t stick to them consistently or there’s an ontology import that uses another naming convention so that there likely will be a glitch in the generated sentences here or there. Or they name things within the context of the hierarchy where they put the class, but in the question it is out of that context and then looks weird or is even meaningless. I moaned about this before; e.g., ‘American’ as the name of the class that should have been named ‘American Pizza’ in the Pizza ontology. Or the word used for the name of the class can have different POS tags such that it makes the generated sentence hard to read; e.g., ‘stuff’ as a noun or a verb.

Be this as it may, overall, promising results were obtained and are being extended (more to follow). Some details can be found in the (CRC of the) paper and the algorithms and data are available from the GitHub repo. The first author of the paper, Toky Raboanary, recently made a short presentation video about the paper for the yearly Open Evening/Showcase, which was held virtually and that page is still online available.

References

[1] Gilbert, N., Keet, C.M. Automating question generation and marking of language learning exercises for isiZulu. 6th International Workshop on Controlled Natural language (CNL’18). Davis, B., Keet, C.M., Wyner, A. (Eds.). IOS Press, FAIA vol. 304, 31-40. Co. Kildare, Ireland, 27-28 August 2018.

[2] Alsubait, T., Parsia, B., Sattler, U. Ontology-based multiple choice question generation. KI – Kuenstliche Intelligenz, 2016, 30(2), 183-188.

[3] Rodriguez Rocha, O., Faron Zucker, C. Automatic generation of quizzes from dbpedia according to educational standards. In: The Third Educational Knowledge Management Workshop. pp. 1035-1041 (2018), Lyon, France. April 23 – 27, 2018.

[4] Vega-Gorgojo, G. Clover Quiz: A trivia game powered by DBpedia. Semantic Web Journal, 2019, 10(4), 779-793.

[5] Chaudhri, V., Cheng, B., Overholtzer, A., Roschelle, J., Spaulding, A., Clark, P., Greaves, M., Gunning, D. Inquire biology: A textbook that answers questions. AI Magazine, 2013, 34(3), 55-72.

[6] Kurdi, G., Leo, J., Parsia, B., Sattler, U., Al-Emari, S. A systematic review of automatic question generation for educational purposes. Int. J. Artif. Intell. Edu, 2020, 30(1), 121-204.

[7] Raboanary, T., Wang, S., Keet, C.M. Generating Answerable Questions from Ontologies for Educational Exercises. 15th Metadata and Semantics Research Conference (MTSR’21). 29 Nov – 3 Dec, Madrid, Spain / online. Springer CCIS (in print).

# NLG requirements for social robots in Sub-Saharan Africa

When the robots come rolling, or just trickling or seeping or slowly creeping, into daily life, I want them to be culturally aware, give contextually relevant responses, and to do that in a language that the user can understand and speak well. Currently, they don’t. Since I work and in live in South Africa, then what does all that mean for the Southern Africa context? Would social robot use case scenarios be different here than in the Global North where most of the robot research and development is happening, and if so, how? What is meant with  contextually relevant responses? Which language(s) should the robot communicate in?

The question of which languages is the easiest to answer: those spoken in this region, which are mainly those in the Niger-Congo B [NCB] (aka ‘Bantu’) family of languages, and then also Portuguese, French, Afrikaans, and English. I’ve been working on theory and tools for NCB languages, and isiZulu in particular (and some isiXhosa and Runyankore), whose research was mainly as part of the two NRF-funded projects GeNI and MoReNL. However, if we don’t know how that human-robot interaction occurs in which setting, we won’t know whether the algorithms designed so far can also be used for that, which may well be beyond the ontology verbalisation, a patient’s medicine prescription generation, weather forecasts, or language learning exercises that we roughly got covered for the controlled language and natural language generation aspects of it.

So then what about those use case scenarios and contextually relevant responses? Let me first give an example of the latter. A few years ago in one of the social issues and professional practice lectures I was teaching, I brought in the Amazon Echo to illustrate precisely that as well as privacy issues with Alexa and digital assistants (‘robot secretaries’) in general. Upon asking “What is the EFF?”, the whole class—some 300 students present at the time—was expecting that Alexa would respond with something like “The EFF is the economic freedom fighters, a political party in South Africa”. Instead, Alexa fetched the international/US-based answer and responded with “The EFF is the electronic frontier foundation” that the class had never heard of and that EFF doesn’t really do anything in South Africa (it does pass the revue later on in the module nonetheless, btw). There’s plenty of online content about the EFF as political party, yet Alexa chose to ignore that and prioritise information from elsewhere. Go figure with lots of other information that has limited online presence and doesn’t score high in the search engine results because there are fewer queries about it. How to get the right answer in those cases is not my problem (area of expertise), but I take that a solved black box and zoom in on the natural language aspects to automatically generate a sentence that has the answer taken from some structured data or knowledge.

The other aspect of this instance, is that the interactions both during and after the lecture was not a 1:1 interaction of students with their own version of Siri or Cortana and the like, but eager and curious students came in teams, so a 1:m interaction. While that particular class is relatively large and was already split into two sessions, larger classes are also not uncommon in several Sub-Saharan countries: for secondary school class sizes, the SADC average is 23.55 learners per class (the world average is 17), with the lowest is Botswana (13.8 learners) and the highest in Malawi with a whopping 72.3 learners in a class, on average. An educational robot could well be a useful way to get out of that catch-22, and, given resource constraints, end up as a deployment scenario with a robot per study group, and that in a multilingual setting that permits code switching (going back and forth between different languages). While human-robot interaction experts still will need to do some contextual inquiries and such to get to the bottom of the exact requirements and sentences, this variation in use is on top of the hitherto know possible ways for educational robots.

Going beyond this sort of informal chatter, I tried to structure that a bit and narrowed it down to a requirements analysis for the natural language generation aspects of it. After some contextualisation, I principally used two main use cases to elucidate natural language generation requirements and assessed that against key advances in research and technologies for NCB languages. Very, very, briefly, any system will need to i) combine data-to-text and knowledge-to-text, ii) generate many more different types of sentences, including sentences for  both written and spoken languages in the NCB languages that are grammatically rich and often agglutinating, and iii) process non-trivial numbers that is non-trivial to do for NCB languages because the surface realization of the numbers depend on the noun class of the noun that is being counted. At present, no system out there can do all of that. A condensed version of the analysis was recently accepted as a paper entitled Natural Language Generation Requirements for Social Robots in Sub-Saharan Africa [1], for the IST-Africa’21 conference, and it will be presented there next week at the virtual event, in the ‘next generation computing’ session no less, on Wednesday the 12th of May.

Probably none of you has ever heard of this conference. IST-Africa is yearly IT conference in Africa that aims to foster North-South and South-South  networking, promote the academia->industry and academia->policy bridge-creation and knowledge transfer pipelines, and capacity building for paper writing and presentation. The topics covered are distinctly of regional relevance and, according to its call for papers, the “Technical, Policy, Social Implications Papers must present analysis of early/final Research or Implementation Project Results, or business, government, or societal sector Case Study”.

Why should I even bother with an event like that? It’s good to sometimes reflect on the context and ponder about relevance of one’s research—after all, part of the university’s income (and thus my salary) and a large part of the research project funding I have received so far comes ultimately from the taxpayers. South African tax payers, to be more precise; not the taxpayers of the Global North. I can ‘advertise’, ahem, my research area and its progress to a regional audience. Also, I don’t expect that the average scientist in the Global North would care about HRI in Africa and even less so for NCB languages, but the analysis needed to be done and papers equate brownie points. Also, if everyone thinks to better not participate in something locally or regionally, it won’t ever become a vibrant network of research, applied research, and technology. I’ve attended the event once, in 2018 when we had a paper on error correction for isiZulu spellcheckers, and from my researcher viewpoint, it was useful for networking and ‘shopping’ for interesting problems that I may be able to solve, based on other participants’ case studies and inquiries.

Time will tell whether attending that event then and now this paper and online attendance will be time wasted or well spent. Unlike the papers on the isiZulu spellcheckers that reported research and concrete results that a tech company easily could take up (feel free to do so), this is a ‘fluffy’ paper, but exploring the use of robots in Africa was an interesting activity to do, I learned a few things along the way, it will save other interested people time in the analysis phase, and hopefully it also will generate some interest and discussion about what sort of robots we’d want and what they could or should be doing to assist, rather than replace, humans.

p.s.: if you still were to think that there are no robots in Africa and deem all this to be irrelevant: besides robots in the automotive and mining industries by, e.g., Robotic Innovations and Robotic Handling Systems, there are robots in education (also in Cape Town, by RD-9), robot butlers in hotels that serve quarantined people with mild COVID-19 in Johannesburg, they’re used for COVID-19 screening in Rwanda, and the Naledi personal banking app by Botlhale, to name but a few examples. Other tools are moving in that direction, such as, among others, Awezamed’s use of speech synthesis with (canned) text in isiZulu, isiXhosa and Afrikaans and there’s of course my research group where we look into knowledge-to-text text generation in African languages.

References

[1] Keet, C.M. Natural Language Generation Requirements for Social Robots in Sub-Saharan Africa. IST-Africa 2021, 10-14 May 2021, online. in print.

# 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.

# 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

# A controlled language for competency questions

The formulation of so-called competency questions (CQs) at the start of the development of an ontology or a similar artefact is a recurring exercise in various ontology development methodologies. For instance, “Which animals are the predators of impalas?” that an African Wildlife ontology should be able to answer and “What are the main parsers for compilers?” that a software ontology may be able to answer. Yet, going by the small number of publicly available CQs, it seems like that many developers skip that step in the process. And it turned out that for those who at least try, a considerable number of purported CQs, actually aren’t at all, are mis-formulated for even having a chance for it to work smoothly (for, say, automated formalisation), or are grammatically incorrect (a depressing 1/3 of the sentences in our test set, to be more precise). Also, there’s no software support in guiding a modeller to formulate CQs, nor to actually do something with it, such as converting it automatically into SPARQL; hence, it is disjointed from the actual artefact under development, which doesn’t help uptake.

In an attempt to narrow this gap, we have developed a controlled natural language (CNL) called CLaRO: a Competency question Language for specifying Requirements for an Ontology, model, or specification [1] for CQs for ‘TBoxes’ (type-level information and knowledge, not instances). Advantages of a CNL for CQs include that it should be easier—or at least less hard—to formalise a CQ into a query over the model and to formulate a CQ in the first place. CLaRO more specifically operates at the language layer, so it deals with noun and verb phrases, rather that the primitives of a representation language and the predetermined modeling style that comes with it. It is also the first one that has been evaluated on coverage, which turned out to be good and better than earlier works on templates for CQs. To add more to it, we also made a basic tool that offers assistive authoring to write CQs (screencast).

We got there by availing of a recently published dataset of 234 CQs that had been analysed linguistically into patterns. We analysed those patterns, and that outcome informed the design of CLaRO. Given the size, this first version pf CLaRO is template-based, with core CQs and several variants, totalling to 134 templates. CLaRO was evaluated with a random sample from the original 234 CQs, a newly created set of CQs scrambled together for related work, and half of the Pizza CQs, as well as evaluated against templates presented elsewhere [2,3]. The results are summarised in the paper and discussed in more detail in a related longer technical report [4]. Here’s the nice table with the aggregate data:

Aggregated results for coverage of the three test sets. The best values are highlighted in italics. (CLaRO results are for the complete set of 134 templates) (source: based on [1])

Given the encouraging results, we also created a proof of concept CQ authoring tool, which both can assist in the authoring of CQs and may contribute to get a better idea of requirements for such a tool. One can use autocomplete so that it proposes a template, and then fill in a selected template, or just ignore it and write a free-form CQ, hit enter, and save it to a text file; the file can also be opened and CQs deleted. Here are a few screenshots on selecting and adding a CQ in the tool:

We will be presenting CLaRO at the 13th International Conference on Metadata and Semantics Research (MTSR’19) in Rome at the end of October. If you have any questions, comments, or suggestions, please don’t hesitate to contact us. The CNL specification in csv and XML formats, the evaluation data, and the tool with the source code are available from the CLaRO Github repo.

References:

[1] 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)

[2] Ren, Y., Parvizi, A., Mellish, C., Pan, J.Z., van Deemter, K., Stevens, R.: Towards competency question-driven ontology authoring. In: Extended Semantic Web Conference (ESWC’14). LNCS, Springer (2014)

[3] Bezerra, C., Freitas, F., Santana, F.: Evaluating ontologies with competency questions. In: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) – Volume 03. pp. 284-285. IEEE Computer Society, Washington, DC, USA (2013)

[4] Keet, C.M., Mahlaza, Z., Antia, M.-J. CLaRO: A data-driven CNL for specifying competency questions. University of Cape Town. Technical Report. 17 July 2019. https://arxiv.org/abs/1907.07378

# Language annotation on the Web with MoLA

The Web consists of very many resources in many languages and has information about even more. Sure, the majority of Internet users speak English, Chinese, or Spanish, but there are sites, pages, paragraphs, and documents in other languages and about lesser known ‘languoids’ (language, dialect, variant, etc.), ranging from, say, a poem about the poor man’s dinner written in an old Brabants dialect that used to be spoken in the south of the Netherlands to the effects of mobile phones on Zimbabwean (cf. South African) isiNdebele [1]. How should that be annotated? Here’s a complex use case of languoids for old French:

(source: [2])

The extant multilingual Semantic Web models, such as W3C’s ontolex-lemon community standard have outsourced that to a ‘the language tag comes from some place’, as they focus on the word-level and/or sentence-level for the multilingual (semantic) Web. There are indeed standardisations of language tags. Notably, there are the ISO 639 codes (parts 1, 2, 3 and 5) for some 8000 languages—but there are more languoids that are not covered by the ISO list, with an estimated 8-15K or so currently spoken languoids and 150K extinct. There are also Glottolog, Ethnologue, and MultiTree, which are more comprehensive in some respect, but they are limited and problematic in other cases. For instance, Glottolog—the best among them—still uses the broader/narrower than, has artificial names for grouping languoids, has inconsistencies in modelling decisions, and is still incomplete in coverage.

My co-authors—Frances Gillis-Webber, also at UCT, and Sabine Tittel, with the Heidelberg Academy of Sciences and Humanities—and I aim to change that so as to allow for more comprehensive and more inclusive language tags and annotations on the Semantic Web.

In order to be able to do so, we developed a Model for Language Annotation (MoLA) that caters for relatively comprehensive languoid annotations and how they are related, such as allowing recording which languoid evolved from or was influenced by which other languoid, when it was spoken and where, its preferred and alternate names, what sort of lect it is (e.g., dialect, pidgin), which dialect cluster or language family it is a member of, and backward compatibility with the ISO 639 codes.

The design approach was that of labour-intensive manual modelling, including competency questions, an extensive use case, and iterative development of the model at the conceptualisation stage using the Object-Role Modeling (ORM) language. This model was then formalised in OWL (well, most of it). It was tested on the competency questions, smaller use case scenarios, and validated with the large use case. A snippet for Spanish is as follows, as the one for Old French gets quite lengthy (some details).

It enhances Glottolog’s model on several key points, including proper relations between languoids cf BT/RT, a languoid can be associated with zero or more regions, and it allows for multiple names of a languoid both concurrently and over time.

This sounds like a smooth process, but there were a few modelling hurdles that had to be overcome. One of them is level of granularity of analysis of a languoid. For instance, one could argue both that isiXhosa is a language—it’s one of the 11 official languages of South Africa—but also that it is a dialect cluster (i.e., a collection), as there are multiple dialects of isiXhosa. This is a similar case for Old French that’s a language and member of the Romance family of languages, but also can be seen as a collection of dialects (e.g., Picard and Norman), and dialects, in turn, may have varieties (e.g., Artois and Santerre for Picard). On the bright side, this now can be represented and, because it is represented explicitly, it can be queried, such as “Which languoids are dialects of French that were spoken in the middle ages in France?” and “Which languoids are a member of Nguni?”. The knowledgebase still needs to be populated, though, so it won’t work yet with all languoids.

More details can be found in the paper that was recently published [2]. It will be presented in a few weeks at the 1st Iberoamerican Conference on Knowledge Graphs and Semantic Web (KGSWC’19), in Villa Clara, Cuba, alongside 13 other papers on ontologies. The first author, Frances, is soon travelling to the ISWS summer school, so I will present it at KGSWC’19.

References

[1] Nkomo, D., Khumalo, L. Embracing the mobile phone technology: its social and linguistic impact with special reference to Zimbabwean Ndebele. African Identities, 10(2): 143-153.

[2] Gillis-Webber, F., Tittel, S., Keet, C.M.. A Model for Language Annotations on the Web. 1st Iberoamerican conference on Knowledge Graphs and Semantic Web (KGSWC’19). Springer CCIS. 23-30 June 2019, Villa Clara, Cuba.

# From ontology verbalisation to language learning exercises

I’m aware that to most people ‘playing with’ (investigating) ontologies and isiZulu does not sound particularly useful on the face of it. Yet, there’s the some long-term future music, like eventually being able to generate patient discharge notes in one’s own language, which will do its bit to ameliorate the language barrier in healthcare in South Africa so that patients at least will adhere to the treatment instructions a little better, and therewith receive better quality healthcare. But benefits in the short-term might serve something as well. To that end, I proposed an honours project last year, which has been completed in the meantime, and one of the two interesting outcomes has made it into a publication already [1]. As you may have guessed from the title, it’s about automation for language learning exercises. The results will be presented at the 6th Workshop on Controlled Natural Language, in Maynooth, Ireland in about 2 weeks time (27-28 August). In the remainder of this post, I highlight the main contributions described in the paper.

First, regarding the post’s title, one might wonder what ontology verbalisation has to do with language learning. Nothing, really, except that we could reuse the algorithms from the controlled natural language (CNL) for ontology verbalisation to generate (computer-assisted) language learning exercises whose answers can be computed and marked automatically. That is, the original design of the CNL for things like pluralising nouns, verb conjugation, and negation that is used for verbalising ontologies in isiZulu in theory [2] and in practice [3], was such that the sentence generator is a detachable module that could be plugged in elsewhere for another task that needs such operations.

Practically, the student who designed and developed the back-end, Nikhil Gilbert, preferred Java over Python, so he converted most parts into Java, and added a bit more, notably the ‘singulariser’, a sentence scrabble, and a sentence generator. Regarding the sentence generator, this is used as part of the exercises & answers generator. For instance, we know that humans and the roles they play (father, aunt, doctor, etc.) are mostly in isiZulu’s noun classes 1, 2, 1a, 2a, or 3a, that those classes do not (or rarely?) have non-human nouns and generally it holds for all humans and their roles that they can ‘eat’, ‘talk’ etc. This makes it relatively easy create a noun chain and a verb chain list to mix and match nouns with verbs accordingly (hurrah! for the semantics-based noun class system). Then, with the 231 nouns and 59 verbs in the newly constructed mini-corpus, the noun chain and the verb chain, 39501 unique question sentences could be generated, using the following overall architecture of the system:

Architecture of the CNL-driven CALL system. The arrows indicate which upper layer components make use of the lower layer components. (Source: [1])

From a CNL perspective as well as the language learning perspective, the actual templates for the exercises may be of interest. For instance, when a learner is learning about pluralising nouns and their associated verb, the system uses the following two templates for the questions and answers:

```Q: <prefixSG+stem> <SGSC+VerbRoot+FV>
A: <prefixPL+stem> <PLSC+VerbRoot+FV>
Q: <prefixSG+stem> <SGSC+VerbRoot+FV> <prefixSG+stem>
A: <prefixPL+stem> <PLSC+VerbRoot+FV> <prefixPL+stem>```

The answers can be generated automatically with the algorithms that generate the plural noun (from ‘prefixSG’ to ‘prefixPL’) and add the plural subject concord (from ‘SGSC’ to ‘PLSC’, in agreement with ‘prefixPL’), which were developed as part of the GeNI project on ontology verbalization. This can then be checked against what the learner has typed. For instance, a generated question could be umfowethu usula inkomishi and the correct answer generated (to check the learner’s response against) is abafowethu basula izinkomishi. Another example is generation of the negation from the positive, or, vv.; e.g.:

```Q: <PLSC+VerbRoot+FV>
A: <PLNEGSC+VerbRoot+NEGFV>```

For instance, the question may present batotoba and the correct answer is then abatotobi. In total, there are six different types of sentences, with two double, like the plural above, hence a total of 16 templates. It is not a lot, but it turned out it is one of the very few attempts to use a CNL in such way: there is one paper that also will be presented at CNL’18 in the same session [4], and an earlier one [5] uses a fancy grammar system (that we don’t have yet computationally for isiZulu). This is not to be misunderstood as that this is one of the first CNL/NLG-based system for computer-assisted language learning—e.g., there’s assistance in essay writing, grammar concept question generation, reading understanding question generation—but curiously very little on CNLs or NLG for the standard entry-level type of questions to learn the grammar. Perhaps the latter is considered ‘boring’ for English by now, given all the resources. However, thousands of students take introduction courses in isiZulu each year, and some automation can alleviate the pressure of routine activities from the lecturers. We have done some evaluations with learners—with encouraging results—and plan to do some more, so that it may eventually transition to actual use in the courses; that is: TBC…

References

[1] Gilbert, N., Keet, C.M. Automating question generation and marking of language learning exercises for isiZulu. 6th International Workshop on Controlled Natural language (CNL’18). IOS Press. Co. Kildare, Ireland, 27-28 August 2018. (in print)

[2] Keet, C.M., Khumalo, L. Toward a knowledge-to-text controlled natural language of isiZulu. Language Resources and Evaluation, 2017, 51(1): 131-157.

[3] Keet, C.M. Xakaza, M., Khumalo, L. Verbalising OWL ontologies in isiZulu with Python. The Semantic Web: ESWC 2017 Satellite Events, Blomqvist, E. et al. (eds.). Springer LNCS vol. 10577, 59-64.

[4] Lange, H., Ljunglof, P. Putting control into language learning. 6th International Workshop on Controlled Natural language (CNL’18). IOS Press. Co. Kildare, Ireland, 27-28 August 2018. (in print)

[5] Gardent, C., Perez-Beltrachini, L. Using FB-LTAG Derivation Trees to Generate Transformation-Based Grammar Exercises. Proc. of TAG+11, Sep 2012, Paris, France. pp117-125, 2012.

# An Ontology Engineering textbook

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

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

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

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

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

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

# ICTs for South Africa’s indigenous languages should be a national imperative, too

South Africa has 11 official languages with English as the language of business, as decided during the post-Apartheid negotiations. In practice, that decision has resulted in the other 10 being sidelined, which holds even more so for the nine indigenous languages, as they were already underresourced. This trend runs counter to the citizens’ constitutional rights and the state’s obligations, as she “must take practical and positive measures to elevate the status and advance the use of these languages” (Section 6 (2)). But the obligations go beyond just language promotion. Take, e.g., the right to have access to the public health system: one study showed that only 6% of patient-doctor consultations was held in the patient’s home language[1], with the other 94% essentially not receiving the quality care they deserve due to language barriers[2].

Learning 3-4 languages up to practical multilingualism is obviously a step toward achieving effective communication, which therewith reduces divisions in society, which in turn fosters cohesion-building and inclusion, and may contribute to achieve redress of the injustices of the past. This route does tick multiple boxes of the aims presented in the National Development Plan 2030. How to achieve all that is another matter. Moreover, just learning a language is not enough if there’s no infrastructure to support it. For instance, what’s the point of searching the Web in, say, isiXhosa when there are only a few online documents in isiXhosa and the search engine algorithms can’t process the words properly anyway, hence, not returning the results you’re looking for? Where are the spellcheckers to assist writing emails, school essays, or news articles? Can’t the language barrier in healthcare be bridged by on-the-fly machine translation for any pair of languages, rather than using the Mobile Translate MD system that is based on canned text (i.e., a small set of manually translated sentences)?

Rule-based approaches to develop tools

Research is being carried out to devise Human Language Technologies (HLTs) to answer such questions and contribute to realizing those aspects of the NDP. This is not simply a case of copying-and-pasting tools for the more widely-spoken languages. For instance, even just automatically generating the plural noun in isiZulu from a noun in the singular required a new approach that combined syntax (how it is written) with semantics (the meaning) through inclusion of the noun class system in the algorithms[3] [summary]. In contrast, for English, just syntax-based rules can do the job[4] (more precisely: regular expressions in a Perl script). Rule-based approaches are also preferred for morphological analysers for the regional languages[5], which split each word into its constituent parts, and for natural language generation (NLG). An NLG system generates natural language text from structured data, information, or knowledge, such as data in spreadsheets. A simple way of realizing that is to use templates where the software slots in the values given by the data. This is not possible for isiZulu, because the sentence constituents are context-dependent, of which the idea is illustrated in Figure 1[6].

Figure 1. Illustration of a template for the ‘all-some’ axiom type of a logical theory (structured knowledge) and some values that are slotted in, such as Professors, resp. oSolwazi, and eat, resp. adla and zidla; ‘nc’ denotes the noun class of the noun, which governs agreement across related words in a sentence. The four sample sentences in English and isiZulu represent the same information.

Therefore, a grammar engine is needed to generate even the most basic sentences correctly. The core aspects of the workflow in the grammar engine [summary] are presented schematically in Figure 2[7], which is being extended with more precise details of the verbs as a context-free grammar [summary][8]. Such NLG could contribute to, e.g., automatically generating patient discharge notes in one’s own language, text-based weather forecasts, or online language learning exercises.

Figure 2. The isiZulu grammar engine for knowledge-to-text consists conceptually of three components: the verbalisation patterns with their algorithms to generate natural language for a selection of axiom types, a way of representing the knowledge in a structured manner, and the linking of the two to realize the generation of the sentences on-the-fly. It has been implemented in Python and Owlready.

Data-driven approaches that use lots of text

The rules-based approach is known to be resource-intensive. Therefore, and in combination with the recent Big Data hype, data-driven approaches with lost of text are on the rise: it offers the hope to achieve more with less effort, not even having to learn the language, and easier bootstrapping of tools for related languages. This can work, provided one has a lot of good quality text (a corpus). Corpora are being developed, such as the isiZulu National Corpus[9], and the recently established South African Centre for Digital Language Resources (SADiLaR) aims to pool the resources. We investigated the effects of a corpus on the quality of an isiZulu spellchecker [summary], which showed that learning the statistics-driven language model on old texts like the bible does not transfer well to modern-day texts such as news items, nor vice versa[10]. The spellchecker has about 90% accuracy in single-word error detection and it seems to contribute to the intellectualisation[11] of isiZulu [summary][12]. Its algorithms use trigrams and probabilities of their occurrence in the corpus to compute the probability that a word is spelled correctly, illustrated in Figure 3, rather than a dictionary-based approach that is impractical for agglutinating languages. The algorithms were reused for isiXhosa simply by feeding it a small isiXhosa corpus: it achieved about 80% accuracy already even without optimisations.

Figure 3. Illustration of the underlying approach of the isiZulu spellchecker

Data-driven approaches are also pursued in information retrieval to, e.g., develop search engines for isiZulu and isiXhosa[13]. Algorithms for data-driven machine translation (MT), on the other hand, can easily be misled by out-of-domain training data of parallel sentences in both languages from which it has to learn the patterns, such as such as concordial agreement like izi- zi- (see Figure 1). In one of our experiments where the MT system learned from software localization texts, an isiXhosa sentence in the context of health care, Le nto ayiqhelekanga kodwa ngokwenene iyenzeka ‘This is not very common, but certainly happens.’ came out as ‘The file is not valid but cannot be deleted.’, which is just wrong. We are currently creating a domain-specific parallel corpus to improve the MT quality that, it is hoped, will eventually replace the afore-mentioned Mobile Translate MD system. It remains to be seen whether such a data-driven MT or an NLG approach, or a combination thereof, may eventually further alleviate the language barriers in healthcare.

Because of the ubiquity of ICTs in all of society in South Africa, HLTs for the indigenous languages have become a necessity, be it for human-human or human-computer interaction. Profit-driven multinationals such as Google, Facebook, and Microsoft put resources into development of HLTs for African languages already. Languages, and the identities and cultures intertwined with them, are a national resource, however; hence, suggesting the need for more research and the creation of a substantial public good of a wide range of HLTs to assist people in the use of their language in the digital age and to contribute to effective communication in society.

[1] Levin, M.E. Language as a barrier to care for Xhosa-speaking patients at a South African paediatric teaching hospital. S Afr Med J. 2006 Oct; 96 (10): 1076-9.

[2] Hussey, N. The Language Barrier: The overlooked challenge to equitable health care. SAHR, 2012/13, 189-195.

[3] Byamugisha, J., Keet, C.M., Khumalo, L. Pluralising Nouns in isiZulu and Related Languages. 17th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing’16). A. Gelbukh (Ed.). Springer LNCS vol 9623, pp. April 3-9, 2016, Konya, Turkey.

[4] Conway, D.M.: An algorithmic approach to English pluralization. In: Salzenberg, C. (ed.) Proceedings of the Second Annual Perl Conference. O’Reilly (1998), San Jose, USA, 17-20 August, 1998

[5] Pretorius, L. & Bosch, S.E. Enabling computer interaction in the indigenous languages of South Africa: The central role of computational morphology. ACM Interactions, 56 (March + April 2003).

[6] Keet, C.M., Khumalo, L. Toward a knowledge-to-text controlled natural language of isiZulu. Language Resources and Evaluation, 2017, 51(1): 131-157.

[7] Keet, C.M. Xakaza, M., Khumalo, L. Verbalising OWL ontologies in isiZulu with Python. The Semantic Web: ESWC 2017 Satellite Events, Blomqvist, E et al. (eds.). Springer LNCS vol 10577, 59-64.

[8] Keet, C.M., Khumalo, L. Grammar rules for the isiZulu complex verb. Southern African Linguistics and Applied Language Studies, 2017, 35(2): 183-200.

[9] L. Khumalo. Advances in Developing corpora in African languages. Kuwala, 2015, 1(2): 21-30.

[10] Ndaba, B., Suleman, H., Keet, C.M., Khumalo, L. The effects of a corpus on isiZulu spellcheckers based on N-grams. In IST-Africa.2016. (May 11-13, 2016). IIMC, Durban, South Africa, 2016, 1-10.

[11] Finlayson, R, Madiba, M. The intellectualization of the indigenous languages of South Africa: Challenges and prospects. Current Issues in Language Planning, 2002, 3(1): 40-61.

[12] Keet, C.M., Khumalo, L. Evaluation of the effects of a spellchecker on the intellectualization of isiZulu. Alternation, 2017, 24(2): 75-97.

[13] Malumba, N., Moukangwe, K., Suleman, H. AfriWeb: A Web Search Engine for a Marginalized Language. Proceedings of 2015 Asian Digital Library Conference, Seoul, South Korea, 9-12 December 2015.

# Updated isiZulu spellchecker and new isiXhosa spellchecker

Noting that February is the month of language activism in South Africa and that 21 February is the International Mother Language Day (a United Nations event since 2000), let me add my proverbial two cents to that. Since the launch of the isiZulu spellchecker in November 2016, research and development has progressed quite a bit, so that we have released a new ‘version 2’ of the spellchecker. For those not in-the-know: isiZulu and isiXhosa are both among the 11 official languages of South Africa, with isiZulu the largest language in the country by first language speakers and isiXhosa is slated to make an international breakthrough, as it’s used in the Black Panther movie that was released this weekend. Anyhow, the main novelties of the updated spellchecker are:

• first error correction algorithms for isiZulu;
• improved error detection with a few basic rules, also for isiZulu;
• new isiXhosa error detection and correction;

The source code is open source, and, due to various tool limitations beyond our control, it’s still a standalone jar file (zipped for download). Here’s a screenshot of the tool, where it checks a piece of text from a novel in isiZulu, illustrating that *khupels has a substitution error (khupela was the intended word):

Single word error *khupels that has a substitution error s for a in the intended word (khupela)

The error corrector can propose possible corrections for single-word errors that are either transpositions, substitutions, insertions, or deletions. So, for instance, *eybo, *yrbo, *yeebo, and *ybo, respectively, cf. the correctly spelled yebo ‘yes’. It doesn’t perform equally well on each type of typo yet, with the best results obtained for transpositions. As with the error detector, it relies on a data-driven approach, with, for error correction, a lot more statistics-based algorithms cf. the error detection-only algorithms. They are described in detail in Frida Mjaria’s 2017 CS honours project. Suggestion accuracy (i.e., that it at least can suggest something) is 95% and suggestion relevance (that it contains the intended word) made it to 61%, mainly due to weak results of corrections for insertion errors (they mess too much with the trigrams).

The error detection accuracy has been improved mainly through better handling of punctuation, thanks to Norman Pilusa’s programming efforts. This was done through a series of rules on top of the data-driven approach, for it is too hard to learn those from a large corpus. For instance, semi-colons, end-of-sentence periods, and numbers (written in isiZulu like, e.g., ngu-42 rather than just 42) are now mostly ignored rather than the words adjacent to it being detected as probably misspelt. It works better than spellchecker.net’s version, which is the only other available isiZulu spellchecker: on a random selection of actual pieces of text, our tool obtained 91.71% lexical recall for error detection, whereas the spellchecker.net’s version got to 82.66% on the same text. Put differently: spellchecker.net flagged about twice as many words as incorrect as ours did (so there wasn’t much point in comparing error corrections).

Finally, because all the algorithms are essentially language-independent (ok, there’s an underlying assumption of using them for highly agglutinative languages), we fed the algorithms a large isiXhosa corpus that is being developed as part of another project, and incorporated that into the spellchecker. There’s room for some fine-tuning especially for the corrector, but at least now there is one, thanks to Norman Pilusa’s software development contributions. That we thought we could get away with this approach is thanks to Nthabiseng Mashiane’s 2017 CS honours project, which showed that the results would be fairly good (>80% error detection) with more data. We also tried a rules-based approach for isiXhosa. It obtained better accuracies than the statistical language model of Nthabiseng, but only for those parts of speech covered by the rules, which is a subset of all types of words. If you’re interested in those rules, please check out Siseko Neti’s 2017 CS Honours project. To the best of my knowledge, it’s the first time those rules have been formally represented in a computer-usable format and they may be useful for other endeavours, such as morphological analysers.

A section of the isiXhosa Wikipedia entry about the UN (*ukuez should be ukuze, which is among the proposed words).

Further improvements are possible, which are being scoped for a v3 some time later. For instance, for the linguists and language scholars: what are the most common typos? What are the most commonly used words? If we had known that, it would have been an easy way to boost the performance. Can we find optimisations to substitutions, insertions, and deletions similar to the one for transpositions? Should some syntax rules be added for further optimisation? These are some of the outstanding questions. If you’re interested in that or related questions, or you would like to use the algorithms in your tool, please contact me.