Conference notes from EKAW 2014

Yet another successful International Conference on Knowledge Engineering and Knowledge Management 2014 (EKAW’14) (in Linköping, Sweden) has just concluded. It was packed with three keynotes, long ans short presentations, posters and demo session, and related workshops and PhD symposium. Big thanks to Patrick Lambrix for the excellent local organisation, and to Stefan Schlobach and Krzysztof Janowicz for putting an interesting programme together! The remainder of the post touches upon some highlights.

Invited talks

The first keynote was by Pascal Hitzler, who talked about ontology design patterns (ODPs) for large-scale data interchange and discovery. He emphasised the need for principled use of ODPs, including the development of a theory of patterns concerning generic vs specific modelling patterns, developing pattern languages and tools, and understanding and formalising relationships between patterns. It sort of did set the tone, and ODPs were a recurring item of the conference. Oscar Corcho gave a reflective and very entertaining keynote on ontology engineering (slides on slideshare). Not to mention the language and tool wars (DL and Protégé won), are you an alpha (philosopher—one term a day), beta, gamma, delta, or epsilon ( contributor), or a ‘savage’ in the brave little world of knowledge management? He identified five deadlocks on communicating the message to ‘the masses’ (ontology reuse, inferences, lightweight vs heavyweight, tooling, multilingualism) and four recommendations; the one missing being on what to do with multilingualism. A lively discussion followed, and references to some of the aspects raised were returning throughout the conference and probably will afterwards as well. The third keynote was by philosopher Arianna Betti, who was basically putting forward the question what we can give her for helping her in the digital humanities on tracking scientific ideas, as described in humanities texts, over time—toward a computational history of ideas. The view from outside in a way was describing some requirements for us and generated some brainstorming afterward, as it does not seem unfeasible to do. A brief handout with some more precise ideas on where models would fit is available via here twitter account (direct link).


Unlike in my PhD student years where I typically tried to read at least a third of the papers before going to the conference, I’ve gotten in the habit of selecting papers to read based on the titles and presentations, and I haven’t read yet the ones I’m mentioning now, but they seem worth mentioning anyway (obviously with my bias and interests, daily intake-capacity, and time constraints writing this the evening before departure in the very early morning).

Several people at UCT are looking into crowdsourcing, and there were two papers about that, being one using pay-as-you-go alignments [1] and one Protégé plugin linked to CrowdFlower for ontology development that despite the CrowdFlower costs, ended up to being cheaper than a few manual experts [2]. Somewhat related to that is Klink UM for extracting hierarchical and similarity relationships based on user feedback [3], and when we’re at it with relationships, there’s a paper on finding (improving) the semantics of relations, being DBpedia’s wikiPage wiki links [4], as well as how object properties are used in ontologies [5]. The latter discovered that object properties are used quite differently when using ODPs vs not using ODPs: the former more often reuses a property and constrains it in an axiom, the latter uses more subtyping and domain and range axioms, and the latter appears to be computationally more efficient (so there are some interesting trade-offs to look into). Other considerations in modelling included further works on anti-patterns with results from real knowledge base development [6]. Related to my own talk about the stuff ontology, was the paper on supply chains and traceability of datasets [7], which we possibly can combine in some way. The paper on clinical guidelines [8] will be passed on to one of my students, who’s trying to build one tailored to a low resource setting with less-skilled health workers, and we probably also will follow up on the study question generation paper [9] that used a knowledge base and template questions to generate natural language questions that the system also can answer, therewith automating to some extent interactive learning by the student. The latter also won the best demo award. The best paper award went to the paper on adaptive knowledge propagation in web ontologies [10].

The other activities

A conference would not be complete without some social event(s). There was even an extra social event the first evening: ice hockey, which was fun, not only because it was the first time I watched such a game in a stadium, but also because there’s a lot of action and it never gets dull, and to top it off, the Linköping team won. Really impressive was the ‘movie’ at Norrköping’s Visualiseringscenter, being the “cosmos 3D” interactive show narrated live by the centre’s director Prof. Anders Ynnerman. We were treated on a trip through space—navigating from the ISS to the outer boundary of the universe—that was all based on current data and scientific evidence. This was followed by a walk-and-play-around in the rest of the centre, and a tasty dinner where Patrick made a fun story out of the talking frog joke. As per usual, it was also a great opportunity to meet colleagues again, discuss, and plan follow-up research, as well as meeting new people and finally meeting others in person whom I only knew by papers. The next EKAW will be in 2016 Bologna, Italy (statistically less cold and dark than here, though the lights have their charm).


(note: in time, people will have their papers on their home pages; for now, most links are to the Springer version)

[1] I.F. Cruz, F. Loprete, M. Palmonari, C. Stroe and A. Taheri. Pay-As-You-Go Multi-user Feedback Model for Ontology Matching. EKAW’14. Springer LNAI 8876, 80-96.

[2] F. Hanika, G. Wohlgenannt and M. Sabou. The uComp Protégé Plugin: Crowdsourcing Enabled Ontology Engineering. EKAW’14. Springer LNAI 8876, 181-196.

[3] F. Osborne and E. Motta. Inferring Semantic Relations by User Feedback. EKAW’14. Springer LNAI 8876, 339-355.

[4] V. Presutti, S. Consoli, A.G. Nuzzolese, D.R. Recupero, A. Gangemi, I. Bannour and H. Zargayouna. Uncovering the Semantics of Wikipedia Pagelinks. EKAW’14. Springer LNAI 8876, 413-428.

[5] K. Hammar. Ontology Design Pattern Property Specialisation Strategies. EKAW’14. Springer LNAI 8876, 165-180

[6] V.K. Chaudhri, R. Katragadda, J. Shrager and M. Wessel. Inconsistency Monitoring in a Large Scientific Knowledge Base. EKAW’14. Springer LNAI 8876, 66-79

[7] M. Solanki and C. Brewster. A Knowledge Driven Approach towards the Validation of Externally Acquired Traceability Datasets in Supply Chain Business Processes. EKAW’14. Springer LNAI 8876, 503-518.

[8] V. Zamborlini, R. Hoekstra, M. da Silveira, C. Pruski, A. ten Teije and F. van Harmelen. A Conceptual Model for Detecting Interactions among Medical Recommendations in Clinical Guidelines: A Case-Study on Multimorbidity. EKAW’14. Springer LNAI 8876, 591-606.

[9] V.K. Chaudhri, P.E. Clark, A. Overholtzer and A. Spaulding. Question Generation from a Knowledge Base. EKAW’14. Springer LNAI 8876, 54-65

[10] P. Minervini, C. d’Amato, N. Fanizzi and F. Esposito. Adaptive Knowledge Propagation in Web Ontologies. EKAW’14. Springer LNAI 8876, 304-319.

VocabLift to learn some isiZulu, Shona, French, and English words

While I’ll be at EKAW’14 to network, present the stuff ontology, and support SUGOI, some of my students will hold the fort locally at the African Language Technologies Workshop (AFLaT’14) on 27-28 November in Cape Town. One of the two posters & demos I contributed to is about a cute tool that two 3rd-year students—Ntokozo Zwane and Sungunani Silubonde—designed and implemented as their capstone project for software engineering, which they called VocabLift (zip). The capstone groups’ task was to develop a tool that can help someone to learn vocabulary in a playful way, which had some leeway to be creative in how to realize that.

The context is that everyone has to learn vocabulary over the years, from basic words in primary school to scientific terminology at university, and any time when one is learning a new language. Besides memorizing ‘boring’ lists of words from a sheet of paper, there are more playful ways to do this, like the multi-player dictionary game and hangman, or single-player memory cards game from the EuroTalk DVDs. There are indeed many word games online, e.g., for English, and learning a foreign language on duolingo, but there is less for multilingualism and the languages in Southern Africa. EuroTalk DVDs for Zulu, Shona, Swahili, Yoruba and a few other African languages do exist, true, but at a cost and they are inflexible in a teaching setting. Enter VocabLift, which is both technologically interesting and for the target languages chosen: isiZulu and Shona, and English and French. Conceptually, it is based on natural language-independent root questions that are mapped to the language of choice, so another language easily can be added, and, unlike the usual ‘closed’ world of the computer-based language games, a teacher can add words to the dictionary, making it in principle adaptable to the desired level of language learning.

Currently, VocabLift has three games: the Picture Matcher, Vocab Trainer, and Word Tetris. In Picture Matcher, the name of the object in the picture has to be provided by the user, with as objective to improve memory and spelling in the chosen language; a screenshots for avocado in isiZulu and pineapple Shona are shown below.

avocado in isiZUlu, right before selecting 'confirm word'

Avocado in isiZulu, right before selecting ‘confirm word’

Pineapple in Shona, after I clicked 'I don't know'

Pineapple in Shona, after I clicked ‘I don’t know’

Vocab Trainer tests the user’s ability to recall the word given in English in the target language; screenshots for green in isiZulu and gray in French are shown below.

Choosing the right word for 'green' in isiZulu (the answer also can be found further below in another screenshot)

Choosing the right word for ‘green’ in isiZulu (the answer also can be found further below in another screenshot)

Same story, and just to show it works for French, too.

Same story, and just to show it works for French, too.

The third game, Word Tetris is included so that the user can learn to match the word to the picture. The user has to type the word associated with the picture before it falls below the bar; a screenshot is shown below (I lost points due to trying make nice screenshots, really).

Halfway playing 'word tetris'

Halfway playing ‘word tetris’

One needs to be logged in as administrator to add words (admin 1234 will do the trick) and use the tool in ‘dictionary mode’, as illustrated in the next two screenshots.

Adding terms, having selected to add it to the isiZulu dictionary

Adding terms, having selected to add it to the isiZulu dictionary

Cellphone was added (and you also can find the answer to 'green', above)

Cellphone was added (and you also can find the answer to ‘green’, above)

VocabLift has been implemented using JavaFX and XML, making the tool platform-independent (click the VocabLift.jar file once the downloaded zip file is unzipped). I’ll readily admit it is very well possible to add more features, adapt it to have it running also on a mobile, or refine the HCI, and some educational technologies researcher may want to investigate whether this or something like it improves the learning outcome significantly, but it was a fun software engineering project over a timespan of a mere two months (with other parts of the course being taught) and words surely can be learnt (at least I have, and so did the students).

The AFLaT’14 poster and demo session runs from 15:30-16:30 on the 27th and will remain available during the breaks on the 28th as well, and Ntokozo and Sungunani will be happy to demo it for you and describe more details about it.

Zubeida Khan awarded with best Master’s thesis from CSIR

Zubeida Khan

I’m delighted to highlight here that Zubeida Khan (Dawood) was awarded with a “Best Master’s Thesis” from the CSIR (South Africa’s Council for Scientific and Industrial Research), where she was based when she did her Msc (cum laude) from UKZN, with a scholarship from the UKZN/CSIR-Meraka Centre for Artificial Intelligence Research, and yours truly as her supervisor.

Her thesis was about realising that library of foundational ontologies that had been proposed since late 2003 (in that WonderWeb deliverable D18). The concrete library is the online Repository for Ontologies of MULtiple Uses, ROMULUS, which was described briefly in the MEDI’13 paper [1], and she has a CSIR “technology demonstrator” about it (file) that received an overall panel evaluation of 90%. The theoretical foundations principally had to do with aligning and mapping the foundational ontologies that are included in the library, which are, to date, the OWL versions of DOLCE, GFO, and BFO, which has appeared in a KCAP’13 poster [2] and KEOD’13 full paper [3] and an extended version is due to appear in a best-papers-of-KEOD book [4]. In case you want to have more details: check Zubeida’s thesis, and I have a few blog posts that informally introduce the material: the first announcement of ROMULUS and the KCAP poster.

ROMULUS also contains an online and extended version of the foundational ontology recommender ONSET [5] (which was mostly her Bsc(hons) project, and whose integration into ROMULUS was part of her MSc), various documentation and browse and search features, and the new SUGOI tool for automated foundational ontology interchangeability [6].

Zubeida recently started her PhD at UCT with me as advisor, on ontology modularity, but in case you have feedback on the work, suggestions, or perhaps also new mappings to/from your favourite foundational ontology, feel free to contact her (or me)!

p.s.: Engineering news has an item about the awards, and so will CSIR have one.

p.p.s.: The minimum requirements for the award was:
-Published more than one paper in a peer reviewed publication
-Excellent behavioural attributes as attested by fellow team members such as work ethic and developing a good personal and professional relationships and an active contribution as a team member
-Above average performance score
-The studies must have been completed on a record time
-Excellent academic achievement


[1] Khan, Z., Keet, C.M. The foundational ontology library ROMULUS. 3rd International Conference on Model & Data Engineering (MEDI’13). A. Cuzzocrea and S. Maabout (Eds.) September 25-27, 2013, Amantea, Calabria, Italy. Springer LNCS 8216, 200-211.

[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. (poster/demo)

[3] Khan, Z., Keet, C.M. Addressing issues in foundational ontology mediation. 5th International Conference on Knowledge Engineering and Ontology Development (KEOD’13), Vilamoura, Portugal, 19-22 September. Filipe, J. and Dietz, J. (Eds.), SCITEPRESS, pp5-16.

[4] Khan, Z.C., Keet, C.M. Foundational ontology mediation in ROMULUS. invited extended version of the KEOD’13 paper, to be published in Springer CCIS.

[5] Khan, Z., Keet, C.M. ONSET: Automated Foundational Ontology Selection and Explanation. 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW’12), A. ten Teije et al. (Eds.). Oct 8-12, Galway, Ireland. Springer, Lecture Notes in Artificial Intelligence LNAI 7603, 237-251.

[6] Khan, Z.C., Keet, C.M. Feasibility of automated foundational ontology interchangeability. 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14). K. Janowicz et al. (Eds.). 24-28 Nov, 2014, Linkoping, Sweden. Springer LNAI 8876, 225-237.

Results of the OWL feature popularity contest at OWLED 2014

One of the events on the OWLED 2014 programme–co-located with ISWC2014–was the OWL feature popularity contest, with as dual purpose to get a feel of possible improvements to the OWL 2 standard and to generate lively discussions (though the latter happened throughout the workshop already anyway). The PC co-chair, Valentina Tamma, and I had collected some questions ourselves and we had solicited suggestions for question from the participants beforehand, and we used a ‘software-based clicker’ (audience response system) during the session so that participants could vote and see results instantly. The remainder of this posts contains the questions and the results. We left the questions open, so you still can vote by going to and fill in the number shown in the left-hand bottom in the screenshots, and try to skew the outcome your way (voting is anonymous). I’ll check the results again in two weeks…

1.The first question referred back to discussions from around 2007 during the standardization process of OWL 2: Several rather distinct features were discussed for OWL 2 that didn’t make it into the standard; do you (still) want any or all of them, if you ever did?

  • n-ary object properties, with n>2
  • constraints among different data properties, be this of the same object or different objects
  • unique name assumption
  • all of them!
  • I don’t really miss any of them

The results, below, show some preference for constraints among data properties, and overall a mild preference to at least have some of them, rather than none.

Voting results of question 1

Voting results of question 1

2. Is there any common pattern for which you would propose syntactic sugar?

  • Strict partial ordering
  • Disjoint transitive roles
  • Witnessed universal/closure: adding existentially quantified to a universal (Carral et al., OWLED14)
  • Witnessed universal/closure: adding universally quantified to an existential (raised in bio-ontologies literature)
  • Specific patterns; e.g., episodes
  • Nothing really

The results, below, are a bit divided. Carral et al.’s paper presented the day before seems to have done some good convincing, given the three votes, and the strict partial ordering, i.e., a pattern for parthood also received some votes, but about half of the respondents weren’t particularly interested in such things.

Voting results of question 2

Voting results of question 2

3. Ignoring practicalities on (in)feasibility, which of the following set of features would you like to see OWL to be extended with most?

  • Temporal
  • Fuzzy and Probabilistic
  • Rough sets
  • I’m not interested in any of these extensions

The results show that some temporal extension is the clear winner, which practically isn’t going to be easy to do, unfortunately, because even minor temporal extensions cause dramatic jumps in complexity. Other suggestions for extensions made during the discussion were more on data properties (again) and a way to deal with measurement units.

Voting results of question 3

Voting results of question 3

4. Which paradigm do you prefer in order to model / modify your ontologies in an ODE?

  • Controlled natural language
  • Diagram-based tool
  • Online collaborative tool
  • Dedicated ontology editor
  • Text editor
  • No preference
  • It depends on the task

Results again in the figure below. The interesting aspect is, perhaps, that there was no one who had no preference, and no one preferred a diagram-based tool. Mostly, it depends on the task, then some tool that caters for collaborative ontology development.

Voting results of question 4

Voting results of question 4

5. There are four standardised optional syntaxes in OWL 2. If due to time/resource constraints, tool compatibilities, etc., not all optional syntaxes could be accommodated for in an “OWL 3.0”, which could be discontinued, according to you, if any?

  • Functional style
  • Turtle
  • Manchester
  • They all should stay

The latter option, that they all should stay, was selected most among the participants, though not by a majority of voters, and I’m sure it would have ended up differently with more participants (based on discussions afterward). Note: by now, the voting was shown ‘live’ as the responses came in cf. the earlier hide-and-show.

Voting results of question 5

Voting results of question 5

6. Turning around the question phrasing: Which feature do you like less?

  • Property chains
  • Key
  • Transitivity
  • The restrictions limiting the interactions between the different property characteristics (thus preventing certain patterns)
  • They are all useful to a greater or lesser extent

Options B and D generated a lively debate, but the results show clearly that the participants who voted wanted to keep them all.

Voting results of question 6

Voting results of question 6

7. Which of the following OP characteristics features do you consider most important when developing an ontology?

  • reflexivity
  • irreflexivity
  • symmetry
  • asymmetry
  • antisymmetry
  • transitivity
  • acyclicity

This last question appeared a no-brainer among the choices, with a unanimous transitivity above all. It was raised whether functional ought to have been included, which we intentionally had not done, for it’s a different kind of constraint (cardinality/multiplicity) than the properties of properties. The results most likely would have looked quite different if we did.

Voting results of question 7

Voting results of question 7

The results were supposed to be on the OWLED community page, but I have from reliable source (the general chair of OWLED14, Bijan Parsia) that the software doesn’t seem to be very friendly and feature rich, hence a quick post here. You can read Bijan’s live blogging of the presentations at OWLED there as well. The proceedings of the workshop are online as CEUR-WS vol. 1265.

Considering some stuff—scientifically

Yay, now I can say “I look into stuff” and actually be precise about what I have been working on (and get it published, too!), rather than just oversimplifying into vagaries about some of my research topics. The final title of the paper I settled on is not as funny as proposing a ‘pointless theory’ [1], though: it’s a Core Ontology of Macroscopic Stuff [2], which has been accepted at the 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14).

The ‘stuff’, in philosophical terms, are those things that are in natural language indicated typically with mass nouns, being those things you can’t count other than in quantities, like gold, water, whipping cream, agar, milk, and so on. The motivation to look into that was both for practical and theoretical reasons. For instance, you are working in the food industry and thus have to be concerned with traceability of ingredients, so you will have to know which (bulk) ingredients originate from where. Then, if something goes wrong—say, an E. coli infection in a product for consumption—then it would be doable to find the source of the microbial contamination. Most people might not realize what happens in the production process; e.g., some quantity of milk comes from a dairy farm, and in the food processing plant, some components of a portion of the milk is separated into parts (whey separated from the cheese-in-the-making, fat for butter and the remainder buttermilk). To talk about parts and portions of such stuffs requires one to know about those stuffs, and how to model it, so there can be some computerized tracking system for swift responses.

On the theoretical side, philosophers were talking about hypothetical cases of sending molecules of mixtures to Venus and the Moon, which isn’t practically usable, in particular because it was glossing over some important details, like that milk is an emulsion and thus has a ‘minimum portion’ for it to remain an emulsion involving many molecules. Foundational ontologies, which I like for their modeling guidance, didn’t come to the rescue either; e.g., DOLCE has Amount of Matter for stuffs but stops there, BFO has none of it. Domain ontologies for food, but also in other areas, such as ecology and biomedicine, each have their own way of modelling stuff, be this by source, usage, or whatever, making things incompatible because several criteria are used. So, there was quite a gap. The core ontology of macroscopic stuff aims to bridge this gap.

This stuff ontology contains categories of stuff and is formalised in OWL. There are distinctions between pure stuff and mixtures, and differences among the mixtures, e.g., true solutions vs colloids among homogeneous mixtures, and solid heterogeneous mixtures vs. suspension among heterogeneous mixtures, and each one with a set of defining criteria. So, Milk is an Emulsion by its very essence, regardless if you want to assign it a role that it is a beverage (Envo ontology) or an animal-associated habitat (MEO ontology), Blood is a Sol (type of colloid), and (table) Sugar a StructuredPureStuff. A basic alignment of the relations involved is possible with the stuff ontology as well regarding granules, grains, and sub-stuffs (used in cyc and biotop, among others).

The ontology both refines the DOLCE and BFO foundational ontologies and it resolves the main type of interoperability issues with stuffs in domain ontologies, thereby also contributing to better ontology quality. To make the ontology usable, modelling guidelines are provided, with examples of inferences, a decision diagram, outline of a template, and illustrations solving the principal interoperability issues among domain ontologies (scroll down to the last part of the paper). The decision diagram, which also gives an informal idea of what’s in the stuff ontology, is depicted below.

Decision diagram to select the principal kind of stuff (Source: [2])

Decision diagram to select the principal kind of stuff (Source: [2])

You can access the stuff ontology on its own, as well as versions linked to DOLCE and BFO. I’ll be presenting it in Sweden at EKAW late November.

p.s.: come to think of it, maybe I should have called it smugly “a real ontology of substance”… (substance being another term used for stuff/matter)


[1] Borgo S., Guarino N., and Masolo C.. A Pointless Theory of Space Based On Strong Connection and Congruence, in L. Carlucci Aiello, J. Doyle (eds.), in Proceedings of the Fifth International Conference on Principles of Knowledge Representation and Reasoning (KR’96), Morgan Kaufmann, Cambridge Massachusetts (USA), 5-8 November 1996, pp. 220-229.

[2] Keet, C.M. A Core Ontology of Macroscopic Stuff. 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14). 24-28 Nov, 2014, Linkoping, Sweden. Springer LNAI. (accepted)

Preliminary results on multilingual ontologies in Bantu languages

As the avid reader of this blog may remember, I wrote about isiZulu verbalization of ontologies before, which presupposed that there was some way in which the isiZulu terms were stored in the ontology, but it did not say anything about those details. In addition, with the 11 official languages in South Africa, some multilingualism may have to be catered for as well. Multilingual ontologies—be it for localization or internationalization of ontologies—is a hot topic: lots of results are becoming available and one of the linguistic models for multilingual ontologies, ontolex-lemon, is a W3C Community Group result (specs). We, being my co-author Catherine Chavula and I, now have now some first insights into that for Bantu languages, which are described in the paper Is Lemon Sufficient for Building Multilingual Ontologies for Bantu Languages? that was accepted recently at the 11th OWL: Experiences and Directions Workshop (OWLED’14), where I’ll present the paper (Riva del Garda, Italy, Oct 17-18, 2014).

The answer to the question in the title of the paper is a ‘not quite’. To justify that, we first identify the requirements for building Bantu lexica, be it in lemon format or another, with a focus in the paper on Chichewa (a language spoken widely in Malawi) and a bit on isiZulu. The Bantu noun class system is challenging, especially when taken together with verb conjugation that is necessary for the OWL object properties. Noun classes are used to group nouns together, like masculine and feminine in some languages, but then based on semantic criteria, like whether the noun refer)s to a person, an animal, a long thin object, etc. Bantu languages have somewhere between 10 and 23 noun classes and they affect word forms. This in itself requires some creativity for creating a lexicon for an ontology, but the issue is exacerbated when considering the verbs, which are used to name object properties in an OLW ontology.

The common ontology development suggestion to put a verb in 3rd person singular to name the object property, which won’t work that easily for Bantu languages, however: the noun class of the noun (of the OWL class) that plays the subject (or: the first class in, say, an all-some axiom) determines how a verb is conjugated. For instance, if a person (in noun class 1) eats something, it is udla (in isiZulu), whereas when a giraffe (in noun class 9 in isiZulu) eats something, it is idla. In lemon, this would amount to an awful lot of rules snuck into each lemon lexicon, hand-crafted for each OWL class where it applies (i.e., for those axioms in which a particular object property appears), and thus also with a lot of duplication, which is undesirable. Even when you know that the domain and range will be one OWL class (e.g., always person), the entry—using the lemon Morphology module—is non-trivial (fig 5 in the paper shows it for foaf:knows in Chichewa).
Annotating an ontology with noun classes and lemon is possible, but not immediately with an ‘out of the box’ lemon. The reason for this is that there was no linguistic resource that actually had sufficient information on the noun class system. So we had to develop a small noun class ontology so that it can be used in conjunction with other linguistic resources such as LexInfo. This is described in some detail in the paper. An example of the Chichewa nc:1 and nc:2 morphology using lemon rules is as follows:

fig3owledTo put lemon to the test with this ncs ontology, Catherine made a version of FOAF in Chichewa using lemon, and did part of the GoodRelations ontology as well (available here). The foaf:person in Chichewa entry in the lexicon, which uses lemon, LexInfo, and the ncs ontology looks like this:

fig4owledThe paper closes with some open issues that will have to be addressed to increase usability of lemon and ‘Bantu ontologies’, and we’re working on some of them (to be continued…).

The presentation of this paper and 10 other full presentations, 2 short presentations, several posters and demos, and two invited talks (by Nicola Guarino and Claudia d’Amato) are on the programme of OWLED’14. Registration is open, and I hope to see you there!

On ‘swapping’ your foundational ontology to increase interoperability

Over the past few years, I’ve been putting some effort into methods and tools and some data collection and analysis that would aid the use of foundational (top-level) ontologies in ontology engineering, such as DOLCE, GFO, and BFO, and some of its relations (mainly part-whole relations). Tools include the Ontology Selection and Explanation Tool to choose the most suitable foundational ontology [1] and OntoPartS [2] and OntoPartS-2 [3] for software-supported modeling of part-whole relations, and experimentally validating using a foundational ontology does make a difference [4]. The latest addition is SUGOI—Software Used to Gain Ontology Interchangeability, initiated by Zubeida Khan’s idea mentioned in her (cum laude) MSc thesis, which I supervised.

In the meantime, SUGOI has been implemented, and we have used it to answer principally two questions:

  1. Is it feasible to automatically generate links between ontology Oa and foundational ontology Oy, given Oa is linked to Ox? Say, I have an ontology linked to BFO, then can I swap BFO for DOLCE?
  2. If there are issues with the former, what is causing it? Or: in praxis, which entities of Ox are typically used for mappings with domain ontologies that may not be present, or present in an incompatible way, in Oy? Or: if not, then why not?

We tested this with 16 ontologies that are linked to a foundational ontology, and the results have just been accepted at the 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14) [5].

Now, I already know that some of you will say (and, in fact, have said!), this is not feasible at all. Arguments on philosophical distinctions are there, yes, but not all of that appears in an OWL file and in the modeller’s view (see also an earlier post and references therein). Put differently: things are not that clear-cut and black-and-white as it initially may seem. We did observe a basic, or raw, ‘swapping success rate’ from 2% for the PID ontology from the GFO it was aligned to, to BFO, to up to a whopping 82% for the IDO ontology from BFO to either DOLCE or GFO (averaging at 36% for the real ontologies we tested with). Now, there.

So, what’s really happening? The success rate actually depends on several factors. Some entities in, say, BFO, while named differently, do have an equivalent in DOLCE or GFO, that may or may not be in a similar place in the ontology (if not, then you still end up with an inconsistency, which we removed as mapping), others do not. Those mappings have been investigated in detail [6], and, indeed, there aren’t many, but surely there are some. Several domain ontologies have alignments to only a few categories in a foundational ontology, others have more. If there aren’t many links, or predominantly to those for which there exists an equivalence assertion, then your ‘swapping success rate’ (called raw interchangeability in the paper) is high. Thus, it is not that it is not feasible at all.


The interface of the online desktop version of SUGOI.

Sounds obvious when one puts it like that. But what about my ontology, you may wonder. Use SUGOI to find out. The log file shows what’s been done in the process, and does compute those raw interchangeability metrics for you. SUGOI is ‘trivial’ to extend to include foundational ontologies other than DOLCE, BFO, and GFO—just the mapping files have to be added, but it doesn’t really change the algorithm.

We also looked at the data, especially for the ones with a low success rate, to figure out what causes it. It appeared that for those that use DOLCE, they probably do so because it has some nice knowledge about attributive properties that are not represented (BFO) or represented in an incompatible way (GFO) elsewhere. Likewise, those ontologies that were linked to BFO or GFO and for which there was a lower interchangeability to DOLCE, had quite a few links to aspects on roles, which aren’t in DOLCE proper, so that was causing a relatively lower success rate there (more details in the paper). We leave it up to the developers of the respective foundational ontologies to decide whether they wan to fill that ‘gap’ in their respective ontology.

We also checked SUGOI’s output against ontologies that had been aligned manually to more than one foundational ontology by the developers. We could find only two that were: BioTop and the Stuff Ontology. Mainly, we found the odd error in alignment and a few ones missed by manual alignment, but with n=2, those results are quite at the level of interesting anecdote (observing that the plural of anecdote is not data).

Whether you want to swap, or offer your ontology aligned to more than one foundational ontology to increase its interoperability with other ontologies, is, clearly, your choice to make. If you decide to do so, you could do that manually, but SUGOI automates that process for you as much as possible. Both Zubeida and I plan to be at EKAW’14, hopefully also with a demo, so that you not only can test it with your ontology (which you can do already on the SUGOI page already), but also gain some further detailed insights into the algorithm, the mapping files it used, and the consequences for your ontology.


[1] Khan, Z., Keet, C.M. ONSET: Automated Foundational Ontology Selection and Explanation. 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW’12), A. ten Teije et al. (Eds.). Oct 8-12, Galway, Ireland. Springer, LNAI 7603, 237-251.

[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.

[3] 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.

[4] Keet, C.M. The use of foundational ontologies in ontology development: an empirical assessment. 8th Extended Semantic Web Conference (ESWC’11), G. Antoniou et al (Eds.), Heraklion, Crete, Greece, 29 May-2 June, 2011. Springer, Lecture Notes in Computer Science LNCS 6643, 321-335.

[5] Khan, Z.C., Keet, C.M. Feasibility of automated foundational ontology interchangeability. 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14). 24-28 Nov, 2014, Linkoping, Sweden. Springer LNAI. (accepted)

[6] Khan, Z., Keet, C.M. Addressing issues in foundational ontology mediation. 5th International Conference on Knowledge Engineering and Ontology Development (KEOD’13), Vilamoura, Portugal, 19-22 September. Filipe, J. and Dietz, J. (Eds.), SCITEPRESS, pp5-16.


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