# Our ESWC17 demos: TDDonto2 and an OWL verbaliser for isiZulu

Besides the full paper on heterogeneous alignments for 14th Extended Semantic Web Conference (ESWC’17) that will take place next week in Portoroz, Slovenia, we also managed to squeeze out two demo papers. You may already know of TDDonto2 with Kieren Davies and Agnieszka Lawrynowicz, which was discussed in an earlier post that has been updated with a tutorial video. It now has a demo paper as well [1], which describes the rationale and a few scenarios. The other demo, with Musa Xakaza and Langa Khumalo, is new-new, but the regular reader might have seen it coming: we finally managed to link the verbalisation patterns for certain Description Logic axiom types [2,3] to those in OWL ontologies. The tool takes as input an ontology in isiZulu and the verbalisation algorithms, and out come the isiZulu sentences, be this in plain text for further processing or in a GUI for inspection by a domain expert [4]. There is a basic demo-screencast to show it’s all working.

The overall architecture may be of interest, for it deviates from most OWL verbalisers. It is shown in the following figure:

For instance, we use the Python-based OWL API Owlready, rather than a Java-based app, for Python is rather popular in NLP and the verbalisation algorithms may be used elsewhere as well. We made more such decisions with the aim to make whatever we did as multi-purpose usable as possible, like the list of nouns with noun classes (surprisingly, and annoyingly, there is no such readily available list yet, though isizulu.net probably will have it somewhere but inaccessible), verb roots, and exceptions in pluralisation. (Problems for integrating the verbaliser with, say, Protégé will be interesting to discuss during the demo session!)

The text-based output doesn’t look as nice as the GUI interface, so I will show here only the GUI interface, which is adorned with some annotations to illustrate that those verbalisation algorithms in the background are far from trivial templates:

For instance, while in English the universal quantification is always ‘Each’ or ‘All’ regardless the named class quantified over, in isiZulu it depends on the noun class of the noun that is the name of the OWL class. For instance, in the figure above, izingwe ‘leopards’ is in noun class 10, so the ‘Each/All’ is Zonke, amavazi ‘vases’ is in noun class 6, so ‘Each/All’ then becomes Onke, and abantu ‘people’/’humans’ is in noun class 2, making Bonke. There are 17 noun classes. They also determine the subject concords (SC, alike conjugation) for the verbs, with zi- for noun class 10, ­a- for noun class 6, and ba- for noun class 2, to name a few. How this all works is described in [2,3]. We’ve implemented all those algorithms and integrated the pluraliser [5] in it to make it work. The source files are available to check and play with already, you can do so and ask us during the ESWC17 demo session, and/or also have a look at the related outputs of the NRF-funded project Grammar Engine for Nguni natural language interfaces (GeNi).

References

[1] Davies, K. Keet, C.M., Lawrynowicz, A. TDDonto2: A Test-Driven Development Plugin for arbitrary TBox and ABox axioms. Extended Semantic Web Conference (ESWC’17), Springer LNCS. Portoroz, Slovenia, May 28 – June 2, 2017. (demo paper)

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

[3] Keet, C.M., Khumalo, L. On the verbalization patterns of part-whole relations in isiZulu. 9th International Natural Language Generation conference (INLG’16), 5-8 September, 2016, Edinburgh, UK. Association for Computational Linguistics, 174-183.

[4] Keet, C.M. Xakaza, M., Khumalo, L. Verbalising OWL ontologies in isiZulu with Python. 14th Extended Semantic Web Conference (ESWC’17). Springer LNCS. Portoroz, Slovenia, May 28 – June 2, 2017. (demo paper)

[5] 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), Springer LNCS. April 3-9, 2016, Konya, Turkey.

# On heterogeneous mappings between ontologies

Representing information and knowledge often can be done in different ways even when the same representation language is used. In some cases, one way of representing it is always better than another—or: the other option is sub-optimal or plain wrong—but in other cases the distinction is not all that clear-cut. For instance, whether to represent ‘Employee’ as a subclass of ‘Person’ or that it inheres in ‘Person’. Now, if two ontologies (or conceptual models) represent it differently but they have to be aligned, then how to find such different modelling patterns and how to align them? And, taking a step back: which alternate modelling patterns are there, and why those? We sought to answer these questions, whose outcome will be presented (and appear in the proceedings of [1]) the 14th Extended Semantic Web Conference (ESWC’17) that will take place later this month in Portoroz, Slovenia.

Setting aside the formal stuff in this blog post, let’s first have a look at some of those different modelling patterns. At it’s core, there are 1) modelling practices in ontologies vs conceptual models and 2) foundational [or: top-level, or upper] ontology guidance vs being ‘compacter’ in representing the knowledge. The generalisations of the following handwaivy examples are described in more detail in the paper, but for this blog post, it hopefully will do as a teaser of the six formalised patterns. Take, e.g., the following examples that are all variations on the same theme: to-reify-or-not-to-reify, where the example in B is further dressed up with content from a foundational ontology:

Indeed, in the examples, what is shown on the left-hand side does not have the exact same information content as what is shown on the right-hand side, but the underlying conceptualization is pretty much the same. The models on the right-hand side are more precise, for one has the opportunity to specify those, like stating that a particular marriage is between two persons (so, no group marriages allowed). Whether one always needs such more precise constraints is a separate matter.

Then there’s the Employee example mentioned in this post’s introduction with two alternate ways of representing it:

That is, a modeller chooses between representing the role an object performs/has as a subclass of that object or in a separate hierarchy of roles. Foundational ontologies take the latter option, domain ontologies the former.

These examples are instantiations of small modelling patterns (of which there may be more than the six formalised in the paper). To devise mappings between them, one ends up with alignments in such a way that they are between two patterns, rather than 1:1 mappings. To get there, we had to take some preliminary steps on how to represent it all formally, such as specifying the language for a pattern and a defining an ontology pattern alignment. This allowed us to formalise the patterns and devise that formal specification of the heterogeneous alignments.

That outcome, in turn, feeds into the alignment pattern search and checking algorithms. The algorithms show that it is feasible to find those patterns automatically, which then can propose possible alignments to the modeller, and that, upon aligning, one can check whether that’s done correctly. For instance, take the following two ontologies graphically represented in an (extended, enhanced) ICOM tool:

Two inter-ontology assertions have been made, pointed out with the two yellow arrows; i.e., ‘Tennis’ is a subclass of ‘Tournament’ and ‘TennisPlayer’ is a subclass of ‘Athlete’. The pattern search algorithm then will try to find instantiations for the small modelling patterns for alignment. Once something is found—in this case, pattern A fits—it will check whether all conditions for the alignment can be satisfied, and if so, it will propose a possible alignment, which is shown in the following illustrative figure:

Of interest here is, perhaps, the ‘new’ object property being proposed, indicated with the yellow arrow, that amounts to an equivalence to the partOf+Match+played. (That threesome can’t be mapped as equivalent to ‘participated’ due to differences in domain and range axioms, and drawing three subsumption lines from ‘participated’ to ‘part of’, ‘Match’, and ‘played’ is awkward.). The algorithms’ output then thus reduces the alignment into a final question to the modeller along the line of “are you ok with the alignment between the purple elements in the two diagrams?”, and accept or reject it. Please refer to the paper for further details.

The principles presented could possibly be used also for refactoring of an ontology, like in TDD [2] or when ‘preparing’ an ontology to align to a foundational ontology. More results on this topic are in the pipeline, and if you want to know now already, we can have a chat at ESWC.

References

[1] 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. Portoroz, Slovenia, May 28 – June 2, 2017. (in print)

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

# Relations with roles / verbalising object properties in isiZulu

The narratives can be very different for the paper “A model for verbalising relations with roles in multiple languages” that was recently accepted paper at the 20th International Conference on Knowledge Engineering and Knowledge management (EKAW’16), for the paper makes a nice smoothie of the three ingredients of language, logic, and ontology. The natural language part zooms in on isiZulu as use case (possibly losing some ontologist or logician readers), then there are the logics about mapping the Description Logic DLR’s role components with OWL (lose possible interest of the natural language researchers), and a bit of philosophy (and lose most people…). It solves some thorny issues when trying to verbalise complicated verbs that we need for knowledge-to-text natural language generation in isiZulu and some other languages (e.g., German). And it solves the matching of logic-based representations popularised in mainly UML and ORM (that typically uses a logic in the DLR family of Description Logic languages) with the more commonly used OWL. The latter is even implemented as a Protégé plugin.

Let me start with some use-cases that cause problems that need to be solved. It is well-known that natural language renderings of ontologies facilitate communication with domain experts who are expected to model and validate the represented knowledge. This is doable for English, with ACE in the lead, but it isn’t for grammatically richer languages. There, there are complications, such as conjugation of verbs, an article that may be dependent on the preposition, or a preposition may modify the noun. For instance, works for, made by, located in, and is part of are quite common names for object properties in ontologies. They all do have a dependent preposition, however, there are different verb tenses, and the latter has a copulative and noun rather than just a verb. All that goes into the object properties name in an ‘English-based ontology’ and does not really have to be processed further in ontology verbalisation other than beautification. Not so in multiple other languages. For instance, the ‘in’ of located in ends up as affixes to the noun representing the object that the other object is located in. Like, imvilophu ‘envelope’ and emvilophini ‘in the envelope’ (locative underlined). Even something straightforward like a property eats can end up having to be conjugated differently depending on who’s eating: when a human eats, it is udla in isiZulu, but for, say, a dog, it is idla (modification underlined), which is driven by the system of noun classes, of which there are 17 in isiZulu. Many more examples illustrating different issues are described in the paper. To make a long story short, there are gradations in complicating effects, from no effect where a preposition can be squeezed in with the verb in naming an OP, to phonological conditioning, to modifying the article of the noun to modifying the noun. A ‘3rd pers. sg.’ may thus be context-dependent, and notions of prepositions may modify the verb or the noun or the article of the noun, or both. For a setting other than English ontologies (e.g., Greek, German, Lithuanian), a preposition may belong neither to the verb nor to the noun, but instead to the role that the object plays in the relation described by the verb in the sentence. For instance, one obtains yomuntu, rather than the basic noun umuntu, if it plays the role of the whole in a part-whole relation like in ‘heart is part of a human’ (inhliziyo iyingxenye yomuntu).

The question then becomes how to handle such a representation that also has to include roles? This is quite common in conceptual data modelling languages and in the DLR family of DL languages, which is known in ontology as positionalism [2]. Bumping up the role to an element in the representation language—thus, in addition to the relationship—enables one to attach information to it, like whether there is a (deep) preposition associated with it, the tense, or the case. Such role-based annotations can then be used to generate the right element, like einen Betrieb ‘some company’ to adjust the article for the case it goes with in German, or ya+umuntu=yomuntu ‘of a human’, modifying the noun in the object position in the sentence.

To get this working properly, with a solid theoretical foundation, we reused a part of the conceptual modelling languages’ metamodel [3] to create a language model for such annotations, in particular regarding the attributes of the classes in the metamodel. On its own, however, it is rather isolated and not immediately useful for ontologies that we set out to be in need of verbalising. To this end, it links to the ‘OWL way of representing relations’ (ontologically: the so-called standard view), and we separate out the logic-based representation from the readings that one can generate with the structured representation of the knowledge. All in all, the simplified high-level model looks like the picture below.

Simplified diagram in UML Class Diagram notation of the main components (see paper for attributes), linking a section of the metamodel (orange; positionalist commitment) to predicates (green; standard view) and their verbalisation (yellow). (Source: [1])

That much for the conceptual part; more details are described in the paper.

Just a fluffy colourful diagram isn’t enough for a solid implementation, however. To this end, we mapped one of the logics that adhere to positionalism to one of the standard view, being DLR [4] and OWL, respectively. It equally well could have been done for other pairs of languages (e.g., with Common Logic), but these two are more popular in terms of theory and tools.

Having the conceptual and logical foundations in place, we did implement it to see whether it actually can be done and to check whether the theory was sufficient. The Protégé plugin is called iMPALA—it could be an abbreviation for ‘Model for Positionalism And Language Annotation’—that both writes all the non-OWL annotations in a separate XML file and takes care of the renderings in Protégé. It works; yay. Specifically, it handles the interaction between the OWL file, the positionalist elements, and the annotations/attributes, plus the additional feature that one can add new linguistic annotation properties, so as to cater for extensibility. Here are a few screenshots:

OWL’s arbeitetFuer ‘works for’ is linked to the relationship arbeiten.

The prey role in the axiom of the impala being eaten by the ibhubesi.

Annotations of the prey role itself, which is a role in the relationship ukudla.

We did test it a bit, from just the regular feature testing to the African Wildlife ontology that was translated into isiZulu (spoken in South Africa) and a people and pets ontology in ciShona (spoken in Zimbabwe). These details are available in the online supplementary material.

The next step is to tie it all together, being the verbalisation patterns for isiZulu [5,6] and the OWL ontologies to generate full sentences, correctly. This is set to happen soon (provided all the protests don’t mess up the planning too much). If you want to know more details that are not, or not clearly, in the paper, then please have a look at the project page of A Grammar engine for Nguni natural language interfaces (GeNi), or come visit EKAW16 that will be held from 21-23 November in Bologna, Italy, where I will present the paper.

References

[1] Keet, C.M., Chirema, T. A model for verbalising relations with roles in multiple languages. 20th International Conference on Knowledge Engineering and Knowledge Management EKAW’16). Springer LNAI, 19-23 November 2016, Bologna, Italy. (in print)

[2] Leo, J. Modeling relations. Journal of Philosophical Logic, 2008, 37:353-385.

[3] Keet, C.M., Fillottrani, P.R. An ontology-driven unifying metamodel of UML Class Diagrams, EER, and ORM2. Data & Knowledge Engineering, 2015, 98:30-53.

[4] Calvanese, D., De Giacomo, G. The Description Logics Handbook: Theory, Implementation and Applications, chap. Expressive description logics, pp. 178-218. Cambridge University Press (2003).

[5] Keet, C.M., Khumalo, L. Toward a knowledge-to-text controlled natural language of isiZulu. Language Resources and Evaluation, 2016, in print.

[6] Keet, C.M., Khumalo, L. On the verbalization patterns of part-whole relations in isiZulu. Proceedings of the 9th International Natural Language Generation conference 2016 (INLG’16), Edinburgh, Scotland, Sept 2016. ACL, 174-183.

# Surprising similarities and differences in orthography across several African languages

It is well-known that natural language interfaces and tools in one’s own language are known to be useful in ICT-mediated communication. For instance, tools like spellcheckers and Web search engines, machine translation, or even just straight-forward natural language processing to at least ‘understand’ documents and find the right one with a keyword search. Most languages in Southern Africa, and those in the (linguistically called) Bantu language family, are still under-resourced, however, so this is not a trivial task due to the limited data and researched and documented grammar. Any possibility to ‘bootstrap’ theory, techniques, and tools developed for one language and to fiddle just a bit to make it work for a similar one will save many resources compared to starting from scratch time and again. Likewise, it would be very useful if both the generic and the few language-specific NLP tools for the well-resourced languages could be reused or easily adapted across languages. The question is: does that work? We know very little about whether it does. Taking one step back, then: for that bootstrapping to work well, we need to have insight into how similar the languages are. And we may be able to find that out if only we knew how to measure similarity of languages.

The most well-know qualitative way for determining some notion of similarity started with Meinhof’s noun class system [1] and the Guthrie zones. That’s interesting, but not nearly enough for computational tools. An experiment has been done for morphological analysers [2], with promising results, yet it also had more of a qualitative flavour to it.

I’m adding here another proverbial “2 cents” to it, by taking a mostly quantitative approach to it, and focusing on orthography (how things are written down) in text documents and corpora. This was a two-step process. First, 12 versions of the Universal Declaration of Human Rights were examined on tokens and their word length; second, because the UDHR is a quite small document, isiZulu corpora were examined to see whether the UDHR was a representative sample, i.e., whether extrapolation from its results may be justified. The methods, results, and discussion are described in “An assessment of orthographic similarity measures for several African languages” [3].

The really cool thing of the language comparison is that it shows clusters of languages, indicating where bootstrapping may have more or less success, and they do not quite match with Guthrie zones. The cumulative frequency distributions of the words in the UDHR of several languages spoken in Sub-Saharan Africa is shown in the figure below, where the names of the languages are those of the file names of the NLTK data kit that contains the quality translations of the UDHR.

Cumulative frequency distributions of the words in the UDHR of several languages spoken in Sub-Saharan Africa (Source: [3]).

The paper contains some statistical tests, showing that the bottom cluster are not statistically significantly different form each other, but they are from the ‘middle’ cluster. So, the word length distribution of Kiswahili is substantially different from that of, among others, isiZulu, in that it has more shorter words and isiZulu more longer words, but Kiswahili’s pattern is similar to that of Afrikaans and English. This is important for NLP, for isiZulu is known to be highly agglutinating, but English (and thus also Kiswahili) is disjunctive. How important is such a difference? The simple answer is that grammatical elements of a sentences get ‘glued’ together in isiZulu, whereas at least some of them are written as separate words in Kiswahili. This is not to be conflated with, say, German, Dutch, and Afrikaans, where nouns can be concatenated to form new words, but, e.g., a preposition is glued onto a noun. For instance, ‘of clay’ is ngobumba, contracting nga+ubumba with a vowel coalescence rule (-a + u- = -o-), which thus happens much less often in a language with disjunctive orthography. This, in turn, affects the algorithms needed to computationally process the languages, hence, the prospects for bootstrapping.

Note that middle cluster looks deceptively isolating, but it isn’t. Sesotho and Setswana are statistically significantly different from the others, in that they are even more disjunctive than English. Sepedi (top-most line) even more so. While I don’t know that language, a hypothetical example suffice to illustrate this notion. There is conjugation of verbs, like ‘works’ or trabajas or usebenza (inflection underlined), but some orthographer a while ago could have decided to write that separate from the verb stem (e.g., trabaj as and u sebenza instead), hence, generating more tokens with fewer characters.

There are other aspects of language and orthography one can ‘play’ with to analyse quantitatively, like whether words mainly end in a vowel or not, and which vowel mostly, and whether two successive vowels are acceptable for a language (for some, it isn’t). This is further described in the paper [3].

Yet, the UDHR is just one document. To examine the generalisability of these observations, we need to know whether the UDHR text is a ‘typical’ one. This was assessed in more detail by zooming in on isiZulu both quantitatively and qualitatively with four other corpora and texts in different genres. The results show that the UHDR is a typical text document orthographically, at least for the cumulative frequency distribution of the word length.

There were some other differences across the other corpora, which have to do with genre and datedness, which was observed elsewhere for whole words [4]. For instance, news items of isiZulu newspapers nowadays include words like iFacebook and EFF, which surely don’t occur in a century-old bible translation. They do violate the ‘no two successive vowels’ rule and the ‘final vowel’ rule, though.

On the qualitative side of the matter, and which will have an effect on searching for information in texts, text summarization, and error correction of spellcheckers, is, again, that agglutination. For instance, searching on imali ‘money’ alone would be woefully inadequate to find all relevant texts; e.g., those news items also include kwemali, yimali, onemali, osozimali, kwezimali, and ngezimali, which are, respectively of -, and -, that/which/who has -, of – (pl.), about/by/with/per – (pl.) money. Searching on the stem or root only is not going to help you much either, however. Take, for instance -fund-, of which the results of just two days of Isolezwe news articles is shown in the table below (articles from 2015, when there were protests, too). Depending on what comes before fund and what comes after it, it can have a different meaning, such as abafundi ‘students’ and azifundi ‘they do not learn’.

Placing this is the broader NLP scope, it also affects the widely-used notion of lexical diversity, which, in its basic form, is a type-to-token ratio. Lexical diversity is used as a proxy measure for ‘difficulty’ or level of a text (the higher the more difficult), language development in humans as they grow up, second-language learning, and related topics. Letting that loose on isiZulu text, it will count abafundi, bafundi, and nabafundi as three different tokens, so wheehee, high lexical diversity, yet in English, it amounts to ‘students’, ‘students’ and ‘and the students’. Put differently, somehow we have to come up with a more meaningful notion of lexical diversity for agglutinating languages. A first attempt is made in the paper in its section 4 [3].

Thus, the last word has not been said yet about orthographic similarity, yet we now do have more insight into it. The surprising similarity of isiZulu (South Africa) with Runyankore (Uganda) was exploited in another research activity, and shown to be very amenable to bootstrapping [5], so, in its own way providing supporting evidence for bootstrapping potential that the figure above also indicated as promising.

As a final comment on the tooling side of things, I did use NLTK (Python). It worked well for basic analyses of text, but it (and similar NLP tools) will need considerable customization for the agglutinating languages.

References

[1] C. Meinhof. 1932. Introduction to the phonology of the Bantu languages . Dietrich Reiner/Ernst Vohsen, Johannesburg. Translated, revised and enlarged in collaboration with the author and Dr. Alice Werner by N.J. Van Warmelo.

[2] L. Pretorius and S. Bosch. Exploiting cross-linguistic similarities in Zulu and Xhosa computational morphology: Facing the challenge of a disjunctive orthography. In Proceedings of the EACL 2009 Workshop on Language Technologies for African Languages – AfLaT 2009, pages 96–103, 2009.

[3] C.M. Keet. An assessment of orthographic similarity measures for several African languages. Technical report, arxiv 1608.03065. August 2016.

[4] Ndaba, B., Suleman, H., Keet, C.M., Khumalo, L. The Effects of a Corpus on isiZulu Spellcheckers based on N-grams. IST-Africa 2016. May 11-13, 2016, Durban, South Africa.

[5] J. Byamugisha, C. M. Keet, and B. DeRenzi. Bootstrapping a Runyankore CNL from an isiZulu CNL. In B. Davis et al., editors, 5th Workshop on Controlled Natural Language (CNL’16), volume 9767 of LNAI, pages 25–36. Springer, 2016. 25-27 July 2016, Aberdeen, UK.

# On generating isiZulu sentences with part-whole relations

It all sounded so easy… We have a pretty good and stable idea about part-whole relations and their properties (see, e.g., [1]), we know how to ‘verbalise’/generate a natural language sentence from basic description logic axioms with object properties that use simple verbs [2], like $Professor \sqsubseteq \exists teaches.Course$ ‘each professor teaches at least one course’, and SNOMED CT is full of logically ‘simple’ axioms (it’s in OWL 2 EL, after all) and has lots of part-whole relations. So why not combine that? We did, but it took some more time than initially anticipated. The outcomes are described in the paper “On the verbalization patterns of part-whole relations in isiZulu”, which was recently accepted at the 9th International Natural Language Generation Conference (INLG’16) that will be held 6-8 September in Edinburgh, Scotland.

What it ended up to be, is that notions of ‘part’ in isiZulu are at times less precise and other times more precise compared to the taxonomy of part-whole relations. This interfered with devising the sentence generation patterns, it pushed the number of ‘elements’ to deal with in the language up to 13 constituents, and there was no way to avoid proper phonological conditioning. We already could handle quantitative, relative, and subject concords, the copulative, and conjunction, but what had to be added were, in particular, the possessive concord, locative affixes, a preposition (just the nga in this context), epenthetic, and the passive tense (with modified final vowel). As practically every element has to be ‘completed’ based on the context (notably the noun class), one can’t really speak of a template-based approach anymore, but a bunch of patterns and partial grammar engine instead. For instance, plain parthood, structural parthood, involvement, membership all have:

• (‘each whole has some part’) $QCall_{nc_{x,pl}}$ $W_{nc_{x,pl}}$ $SC_{nc_{x,pl}}-CONJ-P_{nc_y}$ $RC_{nc_y}-QC_{nc_y}-$dwa
• (‘each part is part of some whole’) $QCall_{nc_{x,pl}}$ $P_{nc_{x,pl}}$ $SC_{nc_{x,pl}}-COP-$ingxenye $PC_{\mbox{\em ingxenye}}-W_{nc_y}$ $RC_{nc_y}-QC _{nc_y}-$dwa

There are a couple of noteworthy things here. First, the whole-part relation does not have one single string, like a ‘has part’ in English, but it is composed of the subject concord (SC) for the noun class (nc) of the noun that play the role of the whole ( W ) together with the phonologically conditioned conjunction na- ‘and’ (the “SC-CONJ”, above) and glued onto the noun of the entity that play the role of the part (P). Thus, the surface realisation of what is conceptually ‘has part’ is dependent on both the noun class of the whole (as the SC is) and on the first letter of the name of the part (e.g., na-+i-=ne-). The ‘is part of’ reading direction is made up of ingxenye ‘part’, which is a noun that is preceded with the copula (COP) y– and together then amounts to ‘is part’. The ‘of’ of the ‘is part of’ is handled by the possessive concord (PC) of ingxenye, and with ingxenye being in noun class 9, the PC is ya-. This ya- is then made into one word together with the noun for the object that plays the role of the whole, taking into account vowel coalescence (e.g., ya-+u-=yo-). Let’s illustrate this with heart (inhliziyo, nc9) standing in a part-whole relation to human (umuntu, NC1), with the ‘has part’ and ‘is part of’ underlined:

• bonke abantu banenhliziyo eyodwa ‘All humans have as part at least one heart’
• The algorithm, in short, to get this sentence from, say $Human \sqsubseteq \exists hasPart.Heart$: 1) it looks up the noun class of umuntu (nc1); 2) it pluralises umuntu into abantu (nc2); 3) it looks up the quantitative concord for universal quantification (QCall) for nc2 (bonke); 4) it looks up the SC for nc2 (ba); 5) then it uses the phonological conditioning rules to add na- to the part inhliziyo, resulting in nenhliziyo and strings it together with the subject concord to banenhliziyo; 6) and finally it looks up the noun class of inhliziyo, which is nc9, and from that it looks up the relative concord (RC) for nc9 (e-) and the quantitative concord for existential quantification (QC) for nc9 (being yo-), and strings it together with –dwa to eyodwa.
• zonke izinhliziyo ziyingxenye yomuntu oyedwa ‘All hearts are part of at least one human’
• The algorithm, in short, to get this sentence from $Heart \sqsubseteq \exists isPartOf.Human$: 1) it looks up the noun class of inhliziyo (nc9); 2) it pluralises inhliziyo to izinhliziyo (nc10); 3) it looks up the QCall for nc10 (zonke); 4) it looks up the SC for nc10 (zi-), takes y- (the COP) and adds them to ingxenye to form ziyingxenye; 5) then it uses the phonological conditioning rules to add ya- to the whole umuntu, resulting in yomuntu; 6) and finally it looks up the noun class of umuntu, which is nc1, and from that the RC for nc10 (o-) and the QC for nc10 (being ye-), and strings it together with –dwa to oyedwa.

For subquantities, we end up with three variants: one for stuff-parts (as in ‘urine has part water’, still with ingxenye for ‘part’), one for portions of solid objects (as in ‘tissue sample is a subquantity of tissue’ or a slice of the cake) that uses umunxa instead of ingxenye, and one ‘spatial’ notion of portion, like that an operating theatre is a portion of a hospital, or the area of the kitchen where the kitchen utensils are is a portion of the kitchen, which uses isiqephu instead of ingxenye. Umunxa is in nc3, so the PC is wa- so that with, e.g., isbhedlela ‘hospital’ it becomes wesibhedlela ‘of the hospital’, and the COP is ng- instead of y-, because umunxa starts with an u. And yet again part-whole relations use locatives (like the containment type of part-whole relation). The paper has all those sentence generation patterns, examples for each, and explanations for them.

The meronymic part-whole relations participation and constitution have added aspects for the verb, such as generating the passive for ‘constituted of’: –akha is ‘to build’ for objects that are made/constituted of some matter in some structural sense, else –enza is used. They are both ‘irregular’ in the sense that it is uncommon that a verb stem starts with a vowel, so this means additional vowel processing (called hiatus resolution in this case) to put the SC together with the verb stem. Then, for instance za+akhiwe=zakhiwe but u+akhiwe=yakhiwe (see rules in paper).

Finally, this was not just a theoretical exercise, but it also has been implemented. I’ll readily admit that the Python code isn’t beautiful and can do with some refactoring, but it does the job. We gave it 42 test cases, of which 38 were answered correctly; the remaining errors were due to an ‘incomplete’ (and unresolvable case for any?) pluraliser and that we don’t know how to systematically encode when to pick akha and when enza, for that requires some more semantics of the nouns. Here is a screenshot with some examples:

The ‘wp’ ones are that a whole has some part, and the ‘pw’ ones that the part is part of the whole and, in terms of the type of axiom that each function verbalises, they are of the so-called ‘all some’ pattern.

The source code, additional files, and the (slightly annotated) test sentences are available from the GENI project’s website. If you want to test it with other nouns, please check whether the noun is already in nncPairs.txt; if not, you can add it, and then invoke the function again. (This remains this ‘clumsily’ until we make a softcopy of all isiZulu nouns with their noun classes. Without the noun class explicitly given, the automatic detection of the noun class is not, and cannot be, more than about 50%, but with noun class information, we can get it up to 90-100% correct in the pluralisation step of the sentence generation [4].)

References

[1] Keet, C.M., Artale, A. Representing and Reasoning over a Taxonomy of Part-Whole Relations. Applied Ontology, 2008, 3(1-2):91-110.

[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. On the verbalization patterns of part-whole relations in isiZulu. 9th International Natural Language Generation conference (INLG’16), September 5-8, 2016, Edinburgh, UK. (in print)

[4] 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), Springer LNCS. April 3-9, 2016, Konya, Turkey. (in print)

# Experimentally-motivated non-trivial intermodel links between conceptual models

I am well aware that some people prefer Agile and mash-ups and such to quickly, scuffily, put an app together, but putting a robust, efficient, lasting, application together does require a bit of planning—analysis and design in the software development process. For instance, it helps to formalise one’s business rules or requirements, or at least structure them with, say, SBVR or ORM, so as to check that the rules obtained from the various stakeholders do not contradict themselves cf. running into problems when testing down the line after having implemented it during a testing phase. Or analyse a bit upfront which classes are needed in the front-end application layer cf. perpetual re-coding to fix one’s mistakes (under the banner ‘refactoring’, as if naming the process gives it an air of respectability), and create, say, a UML diagram or two. Or generating a well-designed database based on an EER model.

Each of these three components can be done in isolation, but how to do this for complex system development where the object-oriented application layer hast to interact with the database back-end, all the while ensuring that the business rules are still adhered to? Or you had those components already, but they need to be integrated? One could link the code to tables in the implementation layer, on an ad hoc basis, and figure it out again and again for any combination of languages and systems. Another one is to do that at the conceptual modelling layer irrespective of the implementation language. The latter approach is reusable (cf. reinventing the mapping wheel time and again), and at a level of abstraction that is easier to cope with for more people, and even more so if the system is really large. So, we went after that option for the past few years and have just added another step to realising all this: how to link which elements in the different models for the system.

It is not difficult to imagine a tool where one can have several windows open, each with a model in some conceptual modelling language—many CASE tools already support modelling in different notations anyway. It is conceptually also fairly straightforward when in, say, the UML model there is a class ‘Employee’ and in the ER diagram there’s an ‘Employee’ entity type: it probably will work out to align these classes. Implementing just this is a bit of an arduous engineering task, but doable. In fact, there is such a tool for models represented in the same language, where the links can be subsumption, equivalence, or disjointness between classes or between relationships: ICOM [2]. But we need something like that to work across modelling languages as well, and for attributes, too. In the hand-waiving abstract sense, this may be intuitively trivial, but the gory details of the conceptual and syntax aspects are far from it. For instance, what should a modeller do if one model has ‘Address’ as an attribute and the other model has it represented as a class? Link the two despite being different types of constructs in the respective languages? Or that ever-recurring example of modelling marriage: a class ‘Marriage’ with (at least) two participants, or ‘Marriage’ as a recursive relationship (UML association) of a ‘Person’ class? What to do if a modeller in one model had chosen the former option and another modeller the latter? Can they be linked up somehow nonetheless, or would one have to waste a lot of time redesigning the other model?

Instead of analysing this for each case, we sought to find a generic solution to it; with we being: Zubeida Khan, Pablo Fillottrani, Karina Cenci, and I. The solution we propose will appear soon in the proceedings of the 20th Conference on Advances in DataBases and Information Systems (ADBIS’16) that will be held at the end of this month in Prague.

So, what did we do? First, we tried to narrow down the possible links between elements in the models: in theory, one might want to try to link anything to anything, but we already knew some model elements are incompatible, and we were hoping that others wouldn’t be needed yet other suspected to be needed, so that a particular useful subset could be the focus. To determine that, we analysed a set of ICOM projects created by students at the Universidad Nacionál del Sur (in Bahía Blanca), and we created model integration scenarios based on publicly available conceptual models of several subject domains, such as hospitals, airlines, and so on, including EER diagrams, UML class diagrams, and ORM models. An example of an integration scenario is shown in the figure below: two conceptual models about airline companies, with on the left the ER diagram and on the right the UML diagram.

One of the integration scenarios [1]

The solid purple links are straightforward 1:1 mappings; e.g., er:Airlines = uml:Airline. Long-dashed dashed lines represent ‘half links’ that are semantically very similar, such as er:Flight.Arr_time ≈ uml:Flight.arrival_time, where the idea of attribute is the same, but ER attributes don’t have a datatype specified whereas UML attributes do. The red short-dashed dashed lines require some transformation: e.g., er:Airplane.Type is an attribute yet uml:Aircraft is a class, and er:Airport.code is an identifier (with its mandatory 1:1 constraint, still no datatype) but uml:Airport.ID is just a simple attribute. Overall, we had 40 models with 33 schema matchings, with 25 links in the ICOM projects and 258 links in the integration scenarios. The detailed aggregates are described in the paper and the dataset is available for reuse (7MB). Unsurprisingly, there were more attribute links than class links (if a class can be linked, then typically also some of its attributes). There were 64 ‘half’ links and 48 transformation links, notably on the slightly compatible attributes, attributes vs. identifiers, attribute<->value type, and attribute<->class.

Armed with these insights from the experiment, a general intermodel link validation approach [3] that uses the unified metamodel [4], and which type of elements occur mostly in conceptual models with their logic-based profiles [5,6], we set out to define those half links and transformation links. While this could have been done with a logic of choice, we opted for a clear step toward implementability by exploiting the ATLAS Transformation Language (ATL) [7] to specify the transformations. As there’s always a source model and a target model in ATL, we constructed the mappings such that both models in question are the ‘source’ and both are mapped into a new, single, ‘target’ model that still adheres to the constraints imposed by the unifying metamodel. A graphical depiction of the idea is shown in the figure below; see paper for details of the mapping rules (they don’t look nice in a blog post).

Informal, graphical rendering of the rule Attribute<->Object Type output [1]

Someone into this matter might think, based on this high-level description, there’s nothing new here. However, there is, and the paper has an extensive related works section. For instance, there’s related work on Distributed Description Logics with bridge rules [8], but they don’t do attributes and the logics used for that doesn’t fit well with the features needed for conceptual modelling, so it cannot be adopted without substantial modifications. Triple Graph Grammars look quite interesting [9] for this sort of task, as does DOL [10], but either would require another year or two to figure it out (feel free to go ahead already). On the practical side, e.g., the Eclipse metamodel of the popular Eclipse Modeling Framework didn’t have enough in the metamodel for what needs to be included, both regarding types of entities and the constraints that would need to be enforced. And so on, such that by a process of elimination, we ended up with ATL.

It would be good to come up with those logic-based linking options and proofs of correctness of the transformation rules presented in the paper, but in the meantime, an architecture design of the new tool was laid out in [11], which is in the stage of implementation as I write this. For now, at least a step has been taken from the three years of mostly theory and some experimentation toward implementation of all that. To be continued J.

References

[1] Khan, Z.C., Keet, C.M., Fillottrani, P.R., Cenci, K.M. Experimentally motivated transformations for intermodel links between conceptual models. 20th Conference on Advances in Databases and Information Systems (ADBIS’16). Springer LNCS. August 28-31, Prague, Czech Republic. (in print)

[2] Fillottrani, P.R., Franconi, E., Tessaris, S. The ICOM 3.0 intelligent conceptual modelling tool and methodology. Semantic Web Journal, 2012, 3(3): 293-306.

[3] Fillottrani, P.R., Keet, C.M. Conceptual Model Interoperability: a Metamodel-driven Approach. 8th International Web Rule Symposium (RuleML’14), A. Bikakis et al. (Eds.). Springer Lecture Notes in Computer Science LNCS vol. 8620, 52-66. August 18-20, 2014, Prague, Czech Republic.

[4] Keet, C.M., Fillottrani, P.R. An ontology-driven unifying metamodel of UML Class Diagrams, EER, and ORM2. Data & Knowledge Engineering, 2015, 98:30-53.

[5] 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.

[6] Fillottrani, P.R., Keet, C.M. Evidence-based Languages for Conceptual Data Modelling Profiles. 19th Conference on Advances in Databases and Information Systems (ADBIS’15). Morzy et al. (Eds.). Springer LNCS vol. 9282, 215-229. Poitiers, France, Sept 8-11, 2015.

[7] Jouault, F. Allilaire, F. Bzivin, J. Kurtev, I. ATL: a model transformation tool. Science of Computer Programming, 2008, 72(12):31-39.

[8] Ghidini, C., Serafini, L., Tessaris, S., Complexity of reasoning with expressive ontology mappings. Formal ontology in Information Systems (FOIS’08). IOS Press, FAIA vol. 183, 151-163.

[9] Golas, U., Ehrig, H., Hermann, F. Formal specification of model transformations by triple graph grammars with application conditions. Electronic Communications of the ESSAT, 2011, 39: 26.

[10] Mossakowsi, T., Codescu, M., Lange, C. The distributed ontology, modeling and specification language. Proceedings of the Workshop on Modular Ontologies 2013 (WoMo’13). CEUR-WS vol 1081. Corunna, Spain, September 15, 2013.

[11] Fillottrani, P.R., Keet, C.M. A Design for Coordinated and Logics-mediated Conceptual Modelling. 29th International Workshop on Description Logics (DL’16). Peñaloza, R. and Lenzerini, M. (Eds.). CEUR-WS Vol. 1577. April 22-25, Cape Town, South Africa. (abstract)

# 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