Reblogging 2013: Release of the (beta version of the) foundational ontology library ROMULUS

From the “10 years of keetblog – reblogging: 2013”: also not easy to choose regarding research. Here, then, the first results of Zubeida Khan’s MSc thesis on the foundational ontology library ROMULUS, being the first post of several papers on the theoretical and methodological aspects of it (KCAP’13 poster, KEOD’13 paper, MEDI’13 paper, book chapter, EKAW’14 paper) and her winning the award for best Masters from the CSIR. The journal paper on ROMULUS has just been published last week in the Journal on Data Semantics, in a special issue edited by Alfredo Cuzzocrea.

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With the increase on ontology development and networked ontologies, both good ontology development and ontology matching for ontology linking and integration are becoming a more pressing issue. Many contributions have been proposed in these areas. One of the ideas to tackle both—supposedly in one fell swoop—is the use of a foundational ontology. A foundational ontology aims to (i) serve as a building block in ontology development by providing the developer with guidance how to model the entities in a domain, and  (ii) serve as a common top-level when integrating different domain ontologies, so that one can identify which entities are equivalent according to their classification in the foundational ontology. Over the years, several foundational ontologies have been developed, such as DOLCE, BFO, GFO, SUMO, and YAMATO, which have been used in domain ontology development. The problem that has arisen now, is how to link domain ontologies that are mapped to different foundational ontologies?

To be able to do this in a structured fashion, the foundational ontologies have to be matched somehow, and ideally have to have some software support for this. As early as 2003, this issue as foreseen already and the idea of a “WonderWeb Foundational Ontologies Library” (WFOL) proposed, so that—in the ideal case—different domain ontologies can to commit to different but systematically related (modules of) foundational ontologies [1]. However, the WFOL remained just an idea because it was not clear how to align those foundational ontologies and, at the time of writing, most foundational ontologies were still under active development, OWL was yet to be standardised, and there was scant stable software infrastructure. Within the Semantic Web setting, the solvability of the implementation issues is within reach yet not realised, but their alignment is still to be carried out systematically (beyond the few partial comparisons in the literature).

We’re trying to solve these theoretical and practical shortcomings through the creation of the first such online library of machine-processable, aligned and merged, foundational ontologies: the Repository of Ontologies for MULtiple USes ROMULUS. This version contains alignments, mappings, and merged ontologies for DOLCE, BFO, and GFO and some modularized versions thereof, as a start. It also has a section on logical inconsistencies; i.e., entities that were aligned manually and/or automatically and seemed to refer to the same thing—e.g., a mathematical set, a temporal region—actually turned out not to be (at least from a logical viewpoint) due to other ‘interfering’ axioms in the ontologies. What one should be doing with those, is a separate issue, but at least it is now clear where the matching problems really are down to the nitty-gritty entity-level.

We performed a small experiment on the evaluation of the mappings (thanks to participants from DERI, Net2 funds, and Aidan Hogan), and we would like to have more feedback on the alignments and mappings. It is one thing that we, or some alignment tool, aligned two entities, another that asserting an equivalence ends up logically consistent (hence mapped) or inconsistent, and yet another what you think of the alignments, especially the ontology engineers. You can participate in the evaluation: you will get a small set of a few alignments at a time, and then you decide whether you agree, partially agree, or disagree with it, are unsure about it, or skip it if you have no clue.

Finally, ROMULUS also has a range of other features, such as ontology selection, a high-level comparison, browsing the ontology through WebProtégé, a verbalization of the axioms, and metadata. It is the first online library of machine-processable, modularised, aligned, and merged foundational ontologies around. A poster/demo paper [2] was accepted at the Seventh International Conference on Knowledge Capture (K-CAP’13), and papers describing details are submitted and in the pipeline. In the meantime, if you have comments and/or suggestions, feel free to contact Zubeida or me.

References

[1] Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A. Ontology library. WonderWeb Deliverable D18 (ver. 1.0, 31-12-2003). (2003) http://wonderweb.semanticweb.org.

[2] Khan, Z., Keet, C.M. Toward semantic interoperability with aligned foundational ontologies in ROMULUS. Seventh International Conference on Knowledge Capture (K-CAP’13), ACM proceedings. 23-26 June 2013, Banff, Canada. (accepted as poster &demo with short paper)

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.

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