The avid blog reader with a good memory might remember we had developed a controlled natural language (CNL) in 2019 that we called CLaRO, a Competency question Language for specifying Requirements for an Ontology, model, or specification , for specifying requirements on the contents of the TBox (type-level) knowledge specifically. The paper won the best student paper award at the MTSR’19 conference. Then COVID-19 came along.
Notwithstanding, we did take next steps and obtained some advances in the meantime, which resulted in a substantially extended CNL, called CLaRO v2 . The paper describing how it came about has been accepted recently at the 7th Controlled Natural Language Workshop (CNL2020/21), which will be held on 8-9 September in Amsterdam, The Netherlands, in hybrid mode.
So, what is it about, being “new and improved!” compared to the first version? The first version was created in a bottom-up fashion based on a dataset of 234 competency questions  in a few domains only. It turned out alright with decent performance on coverage for unseen questions (88% overall) and very significantly outperforming the others, but there were some nagging doubts about the feasibility of bottom-up approaches to template development, which are essentially at the heart of every bottom-up approach: questions about representativeness and quality of the source data. We used more questions as basis to work from than others and had better coverage, but would coverage improve further then still with even more questions? Would it matter for coverage if the CQs were to come from more diverse subject domains? Also, upon manual inspection of the original CQs, it could be seen that some CQs from the dataset were ill-formed, which propagated through to the final set of templates of CLaRO. Would ‘cleaning’ the source data to presumably better quality templates improve coverage?
One of the PhD students I supervise, Mary-Jane Antia, set out to find answer to these questions. CQs were cleaned and vetted by a linguist, the templates recreated and compared and evaluated—this time automatically in a new testing pipeline. New CQs for ontologies were sourced by searching all over the place and finding some 70, to which we added 22 more variants by tweaking wording of existing CQs such that they still would be potentially answerable by an ontology. They were tested on the templates, which resulted in a lower than ideal percentage of coverage and so new templates were created from them, and yet again evaluated. The key results:
- An increase from 88% for CLaRO v1 to 94.1% for CLaRO v2 coverage.
- The new CLaRO v2 has 147 main templates and another 59 variants to cater for minor differences (e.g., singular/plural, redundant words), up from 93 and 41 in CLaRO.
- Increasing the number of domains that the CQs were drawn from had a larger effect on the CQ coverage than cleaning the source data.
All the data, including the new templates, are available on Github and the details are described in the paper . The CLaRO tool that supports the authoring is in the process of being updated so as to incorporate the v2 templates (currently it is working with the v1 templates).
I will try to make it to Amsterdam where CNL’21 will take place, but travel restrictions aren’t cooperating with that plan just yet; else I’ll participate virtually. Mary-Jane will present the paper, and also for her, despite also having funding for the trip, it increasingly looks like a virtual presentation. On the bright side: at least there is a way to participate virtually.
 Keet, C.M., Mahlaza, Z., Antia, M.-J. CLaRO: a Controlled Language for Authoring Competency Questions. 13th Metadata and Semantics Research Conference (MTSR’19). 28-31 Oct 2019, Rome, Italy. Springer CCIS vol. 1075, 3-15.
 Antia, M.-J., Keet, C.M. Assessing and Enhancing Bottom-up CNL Design for Competency Questions for Ontologies. 7th International Workshop on Controlled Natural language (CNL’21), 8-9 Sept. 2021, Amsterdam, the Netherlands. (in print)
 Potoniec, J., Wisniewski, D., Lawrynowicz, A., Keet, C.M. Dataset of Ontology Competency Questions to SPARQL-OWL Queries Translations. Data in Brief, 2020, 29: 105098.