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

Automatically finding the feasible object property

Late last month I wrote about the updated taxonomy of part-whole relations and claimed it wasn’t such a big deal during the modeling process to have that many relations to choose from. Here I’ll back up that claim. Primarily, it is thanks to the ‘Foundational Ontology and Reasoner enhanced axiomatiZAtion’ (FORZA) approach which includes the Guided ENtity reuse and class Expression geneRATOR (GENERATOR) method that was implemented in the OntoPartS-2 tool [1]. The general idea of the GENERATOR method is depicted in the figure below, which outlines two scenarios: one in which the experts perform the authoring of their domain ontology with the help of a foundational ontology, and the other one without a foundational ontology.

generator

I think the pictures are clearer than the following text, but some prefer text, so here goes the explanation attempt. Let’s start with scenario A on the left-hand side of the figure: a modeller has a domain ontology and a foundational ontology and she wants to relate class two domain classes (indicated with C and D) and thus needs to select some object property. The first step is, indeed, selecting C and D (e.g., Human and Heart in an anatomy ontology); this is step (1) in the Figure.

Then (step 2) there are those long red arrows, which indicate that somehow there has to be a way to deal with the alignment of Human and of Heart to the relevant categories in the foundational ontology. This ‘somehow’ can be either of the following three options: (i) the domain ontology was already aligned to the foundational ontology, so that step (2) is executed automatically in the background and the modeler need not to worry, (ii) she manually carries out the alignment (assuming she knows the foundational ontology well enough), or, more likely, (iii) she chooses to be guided by a decision diagram that is specific to the selected foundational ontology. In case of option (ii) or (iii), she can choose to save it permanently or just use it for the duration of the application of the method. Step (3) is an automated process that moves up in the taxonomy to find the possible object properties. Here is where an automated reasoner comes into the equation, which can step-wise retrieve the parent class, en passant relying on taxonomic classification that offers the most up-to-date class hierarchy (i.e., including implicit subsumptions) and therewith avoiding spurious candidates. From a modeller’s viewpoint, one thus only has to select which classes to relate, and, optionally, align the ontology, so that the software will do the rest, as each time it finds a domain and range axiom of a relationship in which the parents of C and D participate, it is marked as a candidate property to be used in the class expression. Finally, the candidate object properties are returned to the user (step 4).

While the figure shows only one foundational ontology, one equally well can use a separate relation ontology, like PW or PWMT, which is just an implementation variant of scenario A: the relation ontology is also traversed upwards and on each iteration, the base ontology class is matched against relational ontology to find relations where the (parent of the) class is defined in a domain and range axiom, also until the top is reached before returning candidate relations.

The second scenario with a domain ontology only is a simplified version of option A, where the alignment step is omitted. In Figure-B above, GENERATOR would return object properties W and R as options to choose from, which, when used, would not generate an inconsistency (in this part of the ontology, at least). Without this guidance, a modeler could, erroneously, select, say, object property S, which, if the branches are disjoint, would result in an inconsistency, and if not declared disjoint, move class C from the left-hand branch to the one in the middle, which may be an undesirable deduction.

For the Heart and Human example, these entities are, in DOLCE terminology, physical objects, so that it will return structural parthood or plain parthood, if the PW ontology is used as well. If, on the other hand, say, Vase and Clay would have been the classes selected from the domain ontology, then a constitution relation would be proposed (be this with DOLCE, PW, or, say, GFO), for Vase is a physical object and Clay an amount of matter. Or with Limpopo and South Africa, a tangential proper parthood would be proposed, because they are both geographic entities.

The approach without the reasoner and without the foundational ontology decision diagram was tested with users, and showed that such a tool (OntoPartS) made the ontology authoring more efficient and accurate [2], and that aligning to DOLCE was the main hurdle for not seeing even more impressive differences. This is addressed with OntoPartS-2, so it ought to work better. What still remains to be done, admittedly, is that larger usability study with the updated version OntoPartS-2. In the meantime: if you use it, please let us know your opinion.

 

References

[1] Keet, C.M., Khan, M.T., Ghidini, C. Ontology Authoring with FORZA. 22nd ACM International Conference on Information and Knowledge Management (CIKM’13). ACM proceedings, pp569-578. Oct. 27 – Nov. 1, 2013, San Francisco, USA.

[2] Keet, C.M., Fernandez-Reyes, F.C., Morales-Gonzalez, A. Representing mereotopological relations in OWL ontologies with OntoPartS. 9th Extended Semantic Web Conference (ESWC’12), Simperl et al. (eds.), 27-31 May 2012, Heraklion, Crete, Greece. Springer, LNCS 7295, 240-254.

A few refreshing feminist articles—to point out and fix bugs in the game

Most articles on gender issues and feminism regurgitate the same old story and arguments, or are reports on more data and experiments with similar results popping up. Some articles or blog posts do bring something relatively new to the table, or apply a feminist analysis to something else, or explain things in a novel way that resonates better in this day and age. Upfront, to those who think gender issues and feminism is mostly rubbish, please read the parable by John Scalzi about the computer game, which is set at the lowest difficulty setting in the Game of the Real World for the Straight White Male; then read ‘those feminazi articles’ as one of pointing out bugs in the code, and of suggesting bug fixes or of a slight rewriting of the game logic to level the playing field. So, here are a few links to some such articles that otherwise may be snowed-under by the online articles on women in STEM, IT, management etc.

The feminist appraisal of Dirty Dancing over at Jezebel’s blog, or, as another one puts it “It’s the feminist sleeper agent of chick flicks” (and some class issues); after reading this, you won’t see the movie the same anymore. (Yes, I did watch the movie again, and the points made in the articles are valid, which, honestly, had escaped me when I watched it in the 80s.)

The many shortcomings of (old) white men futurology, who have a rather limited set of imaginations (fantasies?) in prognosticating. Maybe people in that (non-STEM) discipline already know about the issues and limitations, but I’m in another field of research, so it was new to me. Obviously, if futurology is a science, then it should not make a difference whether men or women do it, but that’s another discussion.

The Super-exploitation of women by Marlene Dixon on capitalism and patriarchy in cahoots to keep women as their unpaid servants and labour-producers wives. I did search for more recent analyses, but they don’t compare in content and clarity to this one.

I did not manage to find again the recent fine rant on feminist issues in Africa that are, at least in part, different from ‘the [white middle-class] feminism in the West’, but these will do on scope as well: feminism here on the continent driven by African women who really do have lots of agency (e.g., all the way up to presidents/prime ministers and Nobel Peace Prize winners) and where certain types of ‘help’ from the outside is counterproductive for it enforces dependence. An example of a currently hot topic here (and, afaik, never was in Europe) is the need for free sanitary pads for girls whose family cannot afford them, so that they can keep going to school to learn rather than miss out on it for a few days each month.

Finally, a slightly crudely formulated article that discusses a whining “pick-up artist” who is “cockblocked by redistribution” in Denmark, a socialist-like and feminist-friendly country. Squeezed between the chatter are notes on flaws on evolutionary psychology and the criticism on feminism as an individual pursuit (e.g., ‘lean in’) versus as a collective goal. Even the pick-up artist eventually notes “we can’t fulfill basic human rights for all without viewing everyone as equal”.