Figuring out the verbalisation of temporal constraints in ontologies and conceptual models

Temporal conceptual models, ontologies, and their logics are nothing new, but that sort of information and knowledge representation still doesn’t gain a lot of traction (cf. say, formal methods for verification). This is in no small part because modelling temporal information is not easy. Several conceptual modelling languages do have various temporal extensions, but most modellers don’t even use all of the default language features yet [1]. How could one at least reduce the barrier to adoption of temporal logics and modelling languages? The two principle approaches are visualisation with a diagrammatic language and rendering it in a (pseudo-)natural language. One of my postgraduate students looked at the former, trying to figure out what would be the best icons and such, which showed there was still a steep learning curve [2]. Before examining whether that could be optimised, I wondered whether the natural language option might be promising. The problem was, that no-one had yet tried to determine what the natural language counterpart of the temporal constraints were supposed to be, let alone whether they be ‘adequate’ or the ‘best’ way of rendering the temporal constraints in tolerable natural language sentences. I wanted to know that badly enough that I tried to find out.

Given that using templates is a tried-and-tested relatively successful approach for atemporal conceptual models and ontologies (e.g., for ORM, the ACE system), it makes sense to do something similar, but then for some temporal extension. As temporal conceptual modelling language I used one that has a Description Logics foundation (DLRUS [3,4]) for that easily links to ontologies as well, added a few known temporal constraints (like for relationships/DL roles, mandatory) and removing others (some didn’t seem all that interesting), which resulted in 34 constraints, still. For each one, I tried to devise more and less reasonable templates, resulting in 101 templates overall. Those templates were evaluated on semantics and preference by three temporal logic experts and five ‘mixed experts’ (experts in natural language generation, logic, or modelling). This resulted in a final set of preferred templates to verbalise the temporal constraints. The remainder of this post first describes a bit about the templates and then the results of which I think they are most interesting.

Templates

The basic idea of a template—in the context of the verbalisation of conceptual models and ontologies—is to have some natural language for the constraint where then the vocabulary gets slotted in at runtime. Take, for instance, simple named class subsumption in an ontology, C \sqsubseteq D, for which one could define a template “Each [C] is a(n) [D]”, so that with some axiom Manager \sqsubseteq Employee, it would generate the sentence “Each Manager is an Employee”. One also could have devised the template “All [C] are [D]” and then it would have generated “All Managers are Employees”. The choice between the two templates in this case is just taste, for in both cases, the semantics is the same. More complex axioms are not always that straightforward. For instance, for the axiom type C \sqsubseteq \exists R.D, would “Each [C] [R] some [D]” be good enough, or would perhaps “Each [C] must [R] at least one [D]” be better? E.g., “Each Professor teaches some Course” vs “Each Professor must teach at least one Course”.

The same can be done for the temporal constraints. To get there, I did a bit of a linguistic detour that informed the template design (described in the paper [5]). Let us take as first example for templates temporal class that has a semantics of o \in C^{\mathcal{I}(t)} \rightarrow \exists t' \neq t. o \notin C^{\mathcal{I}(t')}; for instance, UndergraduateStudent (assuming they graduate and end up as alumni or as drop outs, and weren’t undergrads from birth):

  1. If an object is an instance of entity type [C], then there is some time where it is not a(n) [C].
  2. [C] is an entity type whose objects are, for some time in their existence, not instances of [C].
  3. [C] is an entity type of which each object is not a(n) [C] for some time during its existence.
  4. All instances of entity type [C] are not a(n) [C] for some time.
  5. Each [C] is not a(n) [C] for some time.
  6. Each [C] is for some time not a(n) [C].

Which one(s) do you think captures the semantics, and which one(s) do you prefer?

A more elaborate constraint for relationships is ‘dynamic extension for relationships, past, mandatory], which is formalised as \langle o , o' \rangle \in \mbox{{\sc RDexM}-}_{R_1,R_2}^{\mathcal{I}(t)} \rightarrow (\langle o , o' \rangle \in{\tt R_1}^{\mathcal{I}(t)} \rightarrow \exists t'<t. \langle o , o' \rangle \in \mbox{{\sc RDex}}_{R_1,R_2}^{\mathcal{I}(t')} where \langle o , o' \rangle \in \mbox{{\sc RDex}}_{R_1,R_2}^{\mathcal{I}(t)} \rightarrow ( \langle o , o' \rangle \in{\tt R_1}^{\mathcal{I}(t)} \rightarrow    \exists t'>t. \langle o , o' \rangle \in {\tt R_2}^{\mathcal{I}(t')}).; e.g., every passenger who boards a flight must have checked in for that flight. Two options could be:

  1. Each ..C_1.. ..R_1.. ..C_2.. was preceded by ..C_1.. ..R_2.. ..C_2.. some time earlier.
  2. Each ..C_1.. ..R_1.. ..C_2.. must be preceded by ..C_1.. ..R_2.. ..C_2.. .

I’m not saying they are all correct; they were some of the options given, which the participants could choose from and comment on. The full list of constraints and template options are available in the supplementary material, which also contains a file where you can fill in your own answers, see what the (anonymised) participants said, and it has the final list of ‘best’ constraints.

Results

The main aggregate quantitative results are shown in the following table.

Many observations can be made from the data (see the paper for details). Some of the salient aspects are that there was low inter-annotator agreement among the experts, despite that they know each other (temporal logics is a small community) and that the ‘mixed group’ deemed many sentences correct that the experts deemed wrong in the sense of not properly capturing the semantics of the constraint. Put differently, it looks like the mixed experts, as a group, did not fully grasp some subtle distinction in the temporal constraints.

With respect to the templates, the preferred ones don’t follow the structure of the logic, but are, in a way, a separate rendering, or: there’s no neat 1:1 mapping between axiom type and template structure. That said, that doesn’t mean that they always chose the shortest template: the experts definitely did not, while the mixed experts leaned a bit toward preferring templates with fewer words even though they were surely not always the semantically correct option.

It may not look good that the experts preferred different templates, but in a follow-up interview with one of the experts, the expert noted that it was not really a problem “for there is the logic that does have the precise meaning anyway” and thus “resolves any confusion that may arise from using slightly different terminology”. The temporal logic expert does have a point from the expert’s view, fair enough, but that pretty much defeats my aim with the experiment. Asking more non-experts may not be a good strategy either, for they are, on average, too lenient.

So, for now, we do have a set of, relatively, ‘best’ templates to verbalise temporal constraints in temporal conceptual models and ontologies. The next step is to compare that with the diagrammatic representation. This we did [6], and I’ll describe those results informally in a next post.

I’ll present more details at the upcoming CREOL: Contextual Representation of Events and Objects in Language Workshop that is part of the Joint Ontology Workshops 2017, which will be held next week (21-23 September) in Bolzano, Italy. As the KRDB group at FUB in Bolzano has a few temporal logic experts, I’m looking forward to the discussions! Also, I’d be happy if you would be willing to fill in the spreadsheet with your preferences (before looking at the answers given by the participants!), and send them to me.

 

References

[1] Keet, C.M., Fillottrani, P.R. An analysis and characterisation of publicly available conceptual models. 34th International Conference on Conceptual Modeling (ER’15). Johannesson, P., Lee, M.L. Liddle, S.W., Opdahl, A.L., Pastor López, O. (Eds.). Springer LNCS vol 9381, 585-593. 19-22 Oct, Stockholm, Sweden.

[2] T. Shunmugam. Adoption of a visual model for temporal database representation. M. IT thesis, Department of Computer Science, University of Cape Town, South Africa, 2016.

[3] A. Artale, E. Franconi, F. Wolter, and M. Zakharyaschev. A temporal description logic for reasoning about conceptual schemas and queries. In S. Flesca, S. Greco, N. Leone, and G. Ianni, editors, Proceedings of the 8th Joint European Conference on Logics in Artificial Intelligence (JELIA-02), volume 2424 of LNAI, pages 98-110. Springer Verlag, 2002.

[4] A. Artale, C. Parent, and S. Spaccapietra. Evolving objects in temporal information systems. Annals of Mathematics and Artificial Intelligence, 50(1-2):5-38, 2007.

[5] Keet, C.M. Natural language template selection for temporal constraints. CREOL: Contextual Representation of Events and Objects in Language, Joint Ontology Workshops 2017, 21-23 September 2017, Bolzano, Italy. CEUR-WS Vol. (in print).

[6] Keet, C.M., Berman, S. Determining the preferred representation of temporal constraints in conceptual models. 36th International Conference on Conceptual Modeling (ER’17). Springer LNCS. 6-9 Nov 2017, Valencia, Spain. (in print)

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Bootstrapping a Runyankore CNL from an isiZulu one mostly works well

Earlier this week the 5th Workshop on Controlled Natural Language (CNL’16) was held in Aberdeen, Scotland, where I presented progress made on a Runyankore CNL [1], rather than my student, Joan Byamugisha, who did most of the work on it (she could not attend due to nasty immigration rules by the UK, not a funding issue).

“Runyankore?”, you might ask. It is one of the languages spoken in Uganda. As Runyankore is very under-resourced, any bootstrapping to take a ‘shortcut’ to develop language resources would be welcome. We have a CNL for isiZulu [2], but that is spoken in South Africa, which is a few thousand kilometres further south of Uganda, and it is in a different Guthrie zone of the—in linguistics still called—Bantu languages, so it was a bit of a gamble to see whether those results could be repurposed for Runynakore. They could, needing only minor changes.

What stayed the same were the variables, or: components to make up a grammatically correct sentence when generating a sentence within the context of OWL axioms (ALC, to be more precise). They are: the noun class of the name of the concept (each noun is assigned a noun class—there are 20 in Runyankore), the category of the concept (e.g., noun, adjective), whether the concept is atomic (named OWL class) or an OWL class expression, the quantifier used in the axiom, and the position of the concept in the axiom. The only two real differences were that for universal quantification the word for the quantifier is the same when in the singular (cf. isiZulu, where it changes for both singular or plural), and for disjointness there is only one word, ti ‘is not’ (cf. isiZulu’s negative subject concord + pronomial). Two very minor differences are that for existential quantification ‘at least one’, the ‘at least’ is in a different place in the sentence but the ‘one’ behaves exactly the same, and ‘all’ for universal quantification comes after the head noun rather than before (but is also still dependent on the noun class).

It goes without saying that the vocabulary is different, but that is a minor aspect compared to figuring out the surface realisation for an axiom. Where the bootstrapping thus came in handy was that that arduous step of investigating from scratch the natural language grammar involved in verbalising OWL axioms could be skipped and instead the ones for isiZulu could be reused. Yay. This makes it look very promising to port to other languages in the Bantu language family. (yes, I know, “one swallow does not a summer make” [some Dutch proverb], but one surely is justified to turn up one’s hope a notch regarding generalizability and transferability of results.)

Joan also conducted a user survey to ascertain which surface realisation was preferred among Runyankore speakers, implemented the algorithms, and devised a new one for the ‘hasX’ naming scheme of OWL object properties (like hasSymptom and hasChild). All these details, as well as the details of the Runyankore CNL and the bootstrapping, are described in the paper [1].

 

I cannot resist a final comment on all this. There are people who like to pull it down and trivialise natural language interfaces for African languages, on the grounds of “who cares about text in those kind of countries; we have to accommodate the illiteracy with pictures and icons and speech and such”. People are not as illiterate as is claimed here and there (including by still mentally colonised people from African countries)—if they were, then the likes of Google and Facebook and Microsoft would not invest in localising their interfaces in African languages. The term “illiterate” is used by those people to include also those who do not read/write in English (typically an/the official language of government), even though they can read and write in their local language. People who can read and write—whichever natural language it may be—are not illiterate, neither here in Africa nor anywhere else. English is not the yardstick of (il)literacy, and anyone who thinks it is should think again and reflect a bit on cultural imperialism for starters.

 

References

[1] Byamugisha, J., Keet, C.M., DeRenzi, B. Bootstrapping a Runyankore CNL from an isiZulu CNL. 5th Workshop on Controlled Natural Language (CNL’16), Springer LNAI vol. 9767, 25-36. 25-27 July 2016, Aberdeen, UK. Springer’s version

[2] Keet, C.M., Khumalo, L. Toward a knowledge-to-text controlled natural language of isiZulu. Language Resources and Evaluation, 2016. DOI: 10.1007/s10579-016-9340-0 (in print) accepted version

More results on a CNL for isiZulu

Although it has been a bit quiet here on the controlled natural languages for isiZulu front, lots of new stuff is in the pipeline, and the substantially extended version of our CNL14 and RuleML14 papers [1,2] is in print for publication in the Language Resources and Evaluation journal: Toward a knowledge-to-text controlled natural language of isiZulu [1] (online at LRE as well).

For those who haven’t read the other blog post or the papers on the topic, a brief introduction: for a plethora of reasons, one would want to generate natural language sentences based on some data, information, or knowledge stored on the computer. For instance, to generate automatically weather reports in isiZulu or to browse or query ‘intelligently’ online annotated newspaper text that is guided by an ontology behind-the-scenes in the inner workings of the interface. This means ‘converting’ structured input into structured natural language sentences, which amounts to a Controlled Natural Language (CNL) that is a fragment of the full natural language. For instance, class subsumption in DL (“\sqsubseteq “) is verbalised in English as ‘is a/an’. In isiZulu, it is y- or ng- depending on the first character of the name of the superclass. So, in its simplest form, indlovu \sqsubseteq isilwane (that is, elephant \sqsubseteq animal in an ‘English ontology’) would, with the appropriate algorithm, generate the sentence (be verbalized as) indlovu yisilwane (‘elephant is an animal’).

In the CNL14 and RuleML14 papers, we looked into what could be the verbalisation patterns for subsumption, disjointness, conjunction, and simple existential quantification, we evaluated which ones were preferred, and we designed algorithms for them, as none of them could be done with a template. The paper in the LRE journal extends those works with, mainly: a treatment of verbs (OWL object properties) and their conjugation, updated/extended algorithms to deal with that, design considerations for those algorithms, walk-throughs of the algorithms, and an exploratory evaluation to assess the correctness of the algorithm (is the sentence generated [un]grammatical and [un]ambiguous?). There’s also a longer discussion section and more related works.

Conjugation of the verb in isiZulu is not as trivial as in English, where, for verbalizing knowledge represented in ontologies, one simply uses the 3rd person singular (e.g., ‘eats’) or plural (‘eat’) anywhere it appears in an axiom. In isiZulu, it is conjugated based on the noun class of the noun to which it applies. There are 17 noun classes. For instance, umuntu ‘human’ is in noun class 1, and indlovu in noun class 9. Then, when a human eats something, it is umuntu udla whereas with the elephant, it is indlovu idla. Negating it is not simply putting a ‘not’ or ‘does not’ in front of it, as is the case in English (‘does not eat’), but it has its own conjugation (called negative subject concord) again for each noun class, and modifying the final vowel; the human not eating something then becomes umuntu akadli and for the elephant indovu ayidli. This is now precisely captured in the verbalization patterns and algorithms.

Though a bit tedious and not an easy ride compared to a template-based approach, but surely doable to put in an algorithm. Meanwhile, I did implement the algorithms. I’ll readily admit it’s a scruffy Python file and you’ll have to type the function in the interpreter rather than having it already linked to an ontology, but it works, and that’s what counts. (see that flag put in the sand? 😉 ) Here’s a screenshot with a few examples, just to show that it does what it should do.

Screenshot showing the working functions for verbalising subsumption, disjointness, universal quantificaiton, existential quantification and its negation, and conjunction.

Screenshot showing the working functions for verbalising subsumption, disjointness, universal quantificaiton, existential quantification and its negation, and conjunction.

The code and other files are available from the GeNi project page. The description of the implementation, and the refinements we made along the way in doing so (e.g., filling in that ‘pluralise it’ of the algorithm), is not part of the LRE article, for we were already pushing it beyond the page limit, so I’ll describe that in a later post.

 

References

[1] Keet, C.M., Khumalo, L. Toward verbalizing logical theories in isiZulu. 4th Workshop on Controlled Natural Language (CNL’14), Davis, B, Kuhn, T, Kaljurand, K. (Eds.). Springer LNAI vol. 8625, 78-89. 20-22 August 2014, Galway, Ireland.

[2] Keet, C.M., Khumalo, L. Basics for a grammar engine to verbalize logical theories in isiZulu. 8th International Web Rule Symposium (RuleML’14), A. Bikakis et al. (Eds.). Springer LNCS vol. 8620, 216-225. August 18-20, 2014, Prague, Czech Republic.

[3] Keet, C.M., Khumalo, L. Toward a knowledge-to-text controlled natural language of isiZulu. Language Resources and Evaluation, 2016: in print. DOI: 10.1007/s10579-016-9340-0

Enjoyable and interesting controlled natural languages workshop (CNL’14)

Conferencing in Ireland was a good experience again. Like EKAW 2012, the Fourth Workshop on Controlled Natural Language (CNL’14) was held in the Aula Maxima at the University of Galway, a beautiful ivy-covered building conducive of a stimulating scientific atmosphere and, as any good event, one leaves with plenty of ideas to pursue, and it was a good ambience to meet up again with colleagues as well as meeting new ones, such as Allan Third of the SWAT natural language tools that we use in the ROMULUS foundational ontology library. The remainder of this post is a quick write-up about several of the papers and presentations, written during an otherwise lost moment at Dublin airport.

If you’re not too familiar with CNLs, a useful brief overview to start with is Safwat and Davis’ state of the art [1]. However, some of you might first prefer to read something that is one of the answers to “what would it be good for?”; in that case, I can highly recommend the paper on automatically generating the Swiss avalanche bulleting in 4 languages [2], presented by Kurt Winkler: not only their participants found it very difficult to figure out which ones were manually generated and which ones automatically (55% correct, on average), but also the CNL attendees had trouble with ‘guessing’ it right (yeah, including me). From a technical perspective, it uses a catalogue-based translation system with chunks of text segments. Rather more theoretical were the two papers on the Grammatical Framework. The first one was the invited talk by Aarne Ranta [3] about embedded controlled languages. He provided a brief overview of GF (which started in 1998 at Xerox in Grenoble) up to the current state in the EU project Molto for multilingual machine translation, and different levels of quality of the generated text. Inari Listenmaa presented an extension to the system so that GF will be able to handle compositionality [4].

Interesting to me was the question whether CNLs exist for generating text about temporal events, in part because I’ve another strand of research on temporal conceptual data modelling. Not everyone agreed whether there was anything other than simple stories, but it was hard to find much about it (if you do or know of it, please leave a pointer in the comments). Gordon Pace presented results on verbalizing finite state machines (events with properties), in particular violation traces through the FSM [5]; e.g., when one has a process for logins and failed logins that is violated, the sysadmin needs to know what has happened, and ideally be informed about what essentially went wrong in an intelligible way, and summarized rather than having to pour over endless logs.

On the multilingual front for less common languages, there were two papers for Latvian involving FrameNet for their controlled natural language [6,7], and Langa Khumalo presented our joint paper about isiZulu natural language generation [8] about which I blogged earlier.

Last, but not least—and, more precisely: first—the best paper award. It was awarded to two papers, being to the paper on technical text authoring by Juyeon Kang and Patrick Saint-Dizier [9] and to the paper on style guides as controlled languages, by Karolina Suchowolec [10].

The next CNL workshop will be held in about 2 years time, also most likely co-located with a larger conference (now it was co-located with COLING in Dublin), and some other activities are also in the pipeline, such as a mailing list, wiki etc. so it will be easier for people to stay tuned with the latest developments in CNLs. I’m already looking forward to the next installment of the event.

References

Note: all links are to the CRCs posted on arxiv; the final versions formatted by Springer are on the Springer site (behind a paywall for most people).

[1] Hazem Safwat and Brian Davis. A Brief State of the Art of CNLs for Ontology Authoring. Fourth Workshop on Controlled Natural Language (CNL’14). Springer LNAI vol 8625, 190-200. 20-22 Aug, 2014, Galway, Ireland.

[2] Kurt Winkler, Tobias Kuhn and Martin Volk. Evaluating the fully automatic multi-language translation of the Swiss avalanche bulletin. Fourth Workshop on Controlled Natural Language (CNL’14). Springer LNAI vol 8625, 44-54. 20-22 Aug, 2014, Galway, Ireland.

[3] Aarne Ranta. Embedded Controlled Languages. (invited paper). Fourth Workshop on Controlled Natural Language (CNL’14). Springer LNAI vol 8625, 1-7. 20-22 Aug, 2014, Galway, Ireland.

[4] Ramona Enache, Inari Listenmaa and Prasanth Kolachina. Handling non-compositionality in multilingual CNLs. Fourth Workshop on Controlled Natural Language (CNL’14). Springer LNAI vol 8625, 147-154. 20-22 Aug, 2014, Galway, Ireland.

[5] Gordon Pace and Michael Rosner. Explaining Violation Traces with Finite State Natural Language Generation Models. Fourth Workshop on Controlled Natural Language (CNL’14). Springer LNAI vol 8625, 179-189. 20-22 Aug, 2014, Galway, Ireland.

[6] Guntis Barzdins. FrameNet CNL: a Knowledge Representation and Information Extraction Language. Fourth Workshop on Controlled Natural Language (CNL’14). Springer LNAI vol 8625, 90-101. 20-22 Aug, 2014, Galway, Ireland.

[7] Dana Dannells and Normunds Gruzitis. Controlled Natural Language Generation from a Multilingual FrameNet-based Grammar. Fourth Workshop on Controlled Natural Language (CNL’14). Springer LNAI vol 8625, 155-166. 20-22 Aug, 2014, Galway, Ireland.

[8] C. Maria Keet and Langa Khumalo. Toward verbalizing ontologies in isiZulu. Fourth Workshop on Controlled Natural Language (CNL’14). Springer LNAI vol 8625, 78-89. 20-22 Aug, 2014, Galway, Ireland.

[9] Juyeon Kang and Patrick Saint-Dizier. Towards an Error Correction Memory to Enhance Technical Texts Authoring in LELIE. Fourth Workshop on Controlled Natural Language (CNL’14). Springer LNAI vol 8625, 55-65. 20-22 Aug, 2014, Galway, Ireland.

[10] Karolina Suchowolec. Are Style Guides Controlled Languages? The Case of Koenig & Bauer AG. Fourth Workshop on Controlled Natural Language (CNL’14). Springer LNAI vol 8625, 112-122. 20-22 Aug, 2014, Galway, Ireland.

First steps for isiZulu natural language generation

Yes, Google Translate English-isiZulu does exist, but it has many errors (some very funny) and there’s a lot more to Natural Language Generation (NLG) than machine translation, such as natural language-based query interfaces that has some AI behind it, and they are needed, too [1]. Why should one bother with isiZulu? Muendane has his lucid opinions about that [2], and in addition to that, it is the first language of about 23% of the population of South Africa (amounting to some 10 million people), about half can speak it, and it is a Bantu language, which is spoken by nearly 300 million people—what works for isiZulu grammar may well be transferrable to its related languages. Moreover, it being in a different language family than the more well-resourced languages, it can uncover some new problems to solve for NLG, and facilitate access to online information without the hurdle of having to learn English or French first, as is the case now in Sub-Saharan Africa.

The three principal approaches for NLG are canned text, templates, and grammars. While I knew from previous efforts [3] that the template-based approach is very well doable but has its limitations, and knowing some basic isiZulu, I guessed it might not work with the template-based approach but appealing if it would (for a range of reasons), that no single template could be identified so far was the other end of the spectrum. Put differently: we had to make a start with something resembling the foundations of a grammar engine.

Langa Khumalo, with the Linguistics program and director of the University Language Planning and Development Office at the University of KwaZulu-Natal, and I have been trying to come up with isiZulu NLG. We have patterns and algorithms for (‘simple’) universal and existential quantification, subsumption, negation (class disjointness), and conjunction; or: roughly OWL 2 EL and a restricted version of ALC. OWL 2 EL fist neatly with SNOMED CT, and therewith has the potential for interactive healthcare applications with the isiZulu healthcare terminologies that are being developed at UKZN.

The first results on isiZulu NLG are described in [4,5], which was not an act of salami-slicing, but we had more results than that fitted in a single paper. The first paper [4] will appear in the proceedings ofthe 4th workshop on Controlled Natural language (CNL’14), and is about finding those patterns and, for the options available, an attempt at figuring out which one would be best. The second paper [5], which will appear in the 8th International Web Rule Symposium (RuleML’14) conference proceedings, is more about devising the algorithms to make it work and how to actually generate those sentences. Langa and I plan to attend both events, so you can ask us about the details either in Prague (18-20 Aug) or Galway (20-22 Aug) in person. In the meantime, the CRCs of the papers are online (here and here).

Regarding the technical aspects, the main reasons why we cannot get away with devising templates to generate isiZulu controlled natural language is that isiZulu is non-trivial:

  • There is a whole system of noun classes: nouns are grouped in one of the 17 noun classes, each with their own peculiarities, which is illustrated in Figure 1, below;
  • Agglutination, informally: putting lots of bits and pieces together to make a word. A selection of those so-called ‘concords’ is included in Figure 2, below;
  • Phonological conditioned copulatives, meaning that the ‘is a’ depends on the term that comes after it (ng or y); and
  • Complex verb conjugation.
isiZulu noun classes with an example (source: [5]).

isiZulu noun classes with an example (source: [5]).

A selection of isiZulu concords (source: [5])

A selection of isiZulu concords (source: [5])

What does this mean for the verbalization? In English, we use ‘Each…’ or ‘For all…’ for the universal quantifier and it doesn’t matter over which noun it is quantified. In isiZulu, it does. Each noun class has its own ‘each’ and ‘for all’, and it is not acceptable (understandable) to use one for the wrong noun class. For disjointness, like “Cup is not a Glass” ({\sf Cup \sqsubseteq \neg Glass} in DL), in English we have the ‘is not a’ regardless what comes before or after the subsumption+negation, but in isiZulu, the copulative is omitted, the first noun (OWL class, if you will) brings in a so-called negative subject concord, the second noun brings in a pronominal, and they are glued together (e.g., Indebe akuyona Ingilazi, where the second word is composed of aku + yona), and to top it off, each noun class has its own concord and pronomial. A seemingly simple conjunction—just an ‘and’ in English—has to be divided into an and-when-it-is-used-in-an-enumeration and an and-when-it-is-a-connective, and when it is used in an enumeration, it depends on the first letter of the noun that comes after the ‘and’. Existential quantification is even more of a hassle. The table below shows a very brief summary comparing typical patterns in English with those for isiZulu.

A few DL symbols, their typical verbalization options in English, and an indication of possible patterns (source: [4])

A few DL symbols, their typical verbalization options in English, and an indication of possible patterns (source: [4])

We did ask isiZulu speakers which of the possible options they preferred (in a survey, with Limesurvey localized to isiZulu), but there wasn’t an overwhelming consistent agreement among them except for one of the options for existential quantification (the –dwa option), although there was more agreement among the linguists than among the non-linguists, possibly due to dialect influences (results can be found in [4]).

If you don’t feel like reading the two papers, but still would like to have some general overview and examples, you also can check out the slides of the CS colloquium I gave last week. I managed to ‘lure in’ also ICT4D people—and then smack them with a bit of logic and algorithms—but the other option, being talking about the other paper accepted at RuleML, probably would have had to be a ‘cookie colloquium’ to get anyone to attend (more about that paper in another post—it is fascinating, but possibly of less interest to a broader audience). If you want to skip the tedious bits and just get a feel of how one of the algorithms works out: check out the example starting on slide 63, which shows the steps to go from {\sf \forall x (uSolwazi(x) \rightarrow \exists y (ufundisa(x, y) \land Isifundo(y)))} in FOL, or {\sf uSolwazi \sqsubseteq \exists ufundisa.Isifundo} in DL (“Each professor teaches at least one course”, if the vocabulary were in English), to “Bonke oSolwazi bafundisa isifundo esisodwa”.

Clearly, a lot remains to be done.

References

[1] Alberts, R., Fogwill, T., Keet, C.M. Several Required OWL Features for Indigenous Knowledge Management Systems. 7th Workshop on OWL: Experiences and Directions (OWLED’12). 27-28 May, Heraklion, Crete, Greece. CEUR-WS Vol-849. 12p

[2] Muendane, N.M. I am an African. 2006, Soultalk CC.

[3] Jarrar, M., Keet, C.M., Dongilli, P. Multilingual verbalization of ORM conceptual models and axiomatized ontologies. STARLab Technical Report, Vrije Universiteit Brussels, Belgium. February 2006.

[4] Keet, C.M., Khumalo, L. Toward verbalizing logical theories in isiZulu. 4th Workshop on Controlled Natural Language (CNL’14), 20-22 August 2014, Galway, Ireland. Springer LNAI. (in press)

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