Semantic interoperability of conceptual data modelling languages: FaCIL

Software systems aren’t getting any less complex to design, implement, and maintain, which applies to both the numerous diverse components and the myriad of people involved in the development processes. Even a straightforward configuration of a data­base back-end and an object-oriented front-end tool requires coordination among database analysts, programmers, HCI people, and increasing involvement of domain experts and stakeholders. They each may prefer, and have different competencies in, certain specific design mechanisms; e.g., one may want EER for the database design, UML diagrams for the front-end app, and perhaps structured natural language sentences with SBVR or ORM for expressing the business rules. This requires multi-modal modelling in a plurality of paradigms. This would then need to be supported by hybrid tools that offer interoperability among those modelling languages, since such heterogeneity won’t go away any time soon, or ever.

Example of possible interactions between the various developers of a software system and the models they may be using.

It is far from trivial to have these people work together whilst maintaining their preferred view of a unified system’s design, let alone doing all this design in one system. In fact, there’s no such tool that can seamlessly render such varied models across multiple modelling languages whilst preserving the semantics. At best, there’s either only theory that aims to do that, or only a subset of the respective languages’ features, or a subset of the required combinations. Well, more precisely, until our efforts. We set out to fill this gap in functionality, both in a theoretically sound way and implemented as proof-of-concept to demonstrate its feasibility. The latest progress was recently published in the paper entitled A framework for interoperability with hybrid tools in the Journal of Intelligent Information Systems [1], in collaboration with Germán Braun and Pablo Fillottrani.

First, we propose the Framework for semantiC Interoperability of conceptual data modelling Languages, FaCIL, which serves as the core orchestration mechanism for hybrid modelling tools with relations between components and a workflow that uses them. At its centre, it has a metamodel that is used for the interchange between the various conceptual models represented in different languages and it has sets of rules to and from the metamodel (and at the metamodel level) to ensure the semantics is preserved when transforming a model in one language into a model in a different language and such that edits to one model automatically propagate correctly to the model in another language. In addition, thanks to the metamodel-based approach, logic-based reconstructions of the modelling languages also have become easier to manage, and so a path to automated reasoning is integrated in FaCIL as well.

This generic multi-modal modelling interoperability framework FaCIL was instantiated with a metamodel for UML Class Diagrams, EER, and ORM2 interoperability specifically [2] (introduced in 2015), called the KF metamodel [3] with its relevant rules (initial and implemented ones), an English controlled natural language, and a logic-based reconstruction into a fragment of OWL (orchestration graphically from the paper). This enables a range of different user interactions in the modelling process, of which an example of a possible workflow is shown in the following figure.

A sample workflow in the hybrid setting, showing interactions between visual conceptual data models (i.e., in their diagram version) and in their (pseudo-)natural language versions, with updates propagating to the others automatically. At the start (top), there’s a visual model in one’s preferred language from which a KF runtime model is generated. From there, it can go in various directions: verbalise, convert, or modify it. If the latter, then the KF runtime model is also updated and the changes are propagated to the other versions of the model, as often as needed. The elements in yellow/green/blue are thanks to FaCIL and the white ones are the usual tasks in the traditional one-off one-language modelling setting.

These theoretical foundations were implemented in the web-based crowd 2.0 tool (with source code). crowd 2.0 is the first hybrid tool of its kind, tying together all the pieces such that now, instead of partial or full manual model management of transformations and updates in multiple disparate tools, these tasks can be carried out automatically in one application and therewith also allow diverse developers and stakeholders to work from a shared single system.

We also describe a use case scenario for it – on Covid-19, as pretty much all of the work for this paper was done during the worse-than-today’s stage of the pandemic – that has lots of screenshots from the tool in action, both in the paper (starting here, with details halfway in this section) and more online.

Besides evaluating the framework with an instantiation, a proof-of-concept implementation of that instantiation, and a use case, it was also assessed against the reference framework for conceptual data modelling of Delcambre and co-authors [4] and shown to meet those requirements. Finally, crowd 2.0’s features were assessed against five relevant tools, considering the key requirements for hybrid tools, and shown to compare favourable against them (see Table 2 in the paper).

Distinct advantages can be summed up as follows, from those 26 pages of the paper, where the, in my opinion, most useful ones are underlined here, and the most promising ones to solve another set of related problems with conceptual data modelling (in one fell swoop!) in italics:

• One system for related tasks, including visual and text-based modelling in multiple modelling languages, automated transformations and update propagation between the models, as well as verification of the model on coherence and consistency.
• Any visual and text-based conceptual model interaction with the logic has to be maintained only in one place rather than for each conceptual modelling and controlled natural language separately;
• A controlled natural language can be specified on the KF metamodel elements so that it then can be applied throughout the models regardless the visual language and therewith eliminating duplicate work of re-specifications for each modelling language and fragment thereof;
• Any further model management, especially in the case of large models, such as abstraction and modularisation, can be specified either on the logic or on the KF metamodel in one place and propagate to other models accordingly, rather than re-inventing or reworking the algorithms for each language over and over again;
• The modular design of the framework allows for extensions of each component, including more variants of visual languages, more controlled languages in your natural language of choice, or different logic-based reconstructions.

Of course, more can be done to make it even better, but it is a milestone of sorts: research into the  theoretical foundations of this particular line or research had commenced 10 years ago with the DST/MINCyT-funded bi-lateral project on ontology-driven unification of conceptual data modelling languages. Back then, we fantasised that, with more theory, we might get something like this sometime in the future. And we did.

References

[1] Germán Braun, Pablo Fillottrani, and C Maria Keet. A framework for interoperability with hybrid tools. Journal of Intelligent Information Systems, in print since 29 July 2022.

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

[3] Fillottrani, P.R., Keet, C.M. KF metamodel formalization. Technical Report, Arxiv.org http://arxiv.org/abs/1412.6545. Dec 19, 2014. 26p.

[4] Delcambre, L. M. L., Liddle, S. W., Pastor, O., & Storey, V. C. (2018). A reference framework for conceptual modeling. In: 37th International Conference on Conceptual Modeling (ER’18). LNCS. Springer, vol. 11157, 27–42.

Advertisement

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 20^th 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)

Fruitful ADBIS’15 in Poitiers

The 19th Conference on Advances in Databases and Information Systems (ADBIS’15) just finished yesterday. It was an enjoyable and well-organised conference in the lovely town of Poitiers, France. Thanks to the general chair, Ladjel Bellatreche, and the participants I had the pleasure to meet up with, listen to, and receive feedback from. The remainder of this post mainly recaps the keynotes and some of the presentations.

 

Keynotes

The conference featured two keynotes, one by Serge Abiteboul and on by Jens Dittrich, both distinguished scientists in databases. Abiteboul presented the multi-year project on Webdamlog that ended up as a ‘personal information management system’, which is a simple term that hides the complexity that happens behind the scenes. (PIMS is informally explained here). It breaks with the paradigm of centralised text (e.g., Facebook) to distributed knowledge. To achieve that, one has to analyse what’s happening and construct the knowledge from that, exchange knowledge, and reason and infer knowledge. This requires distributed reasoning, exchanging facts and rules, and taking care of access control. It is being realised with a datalog-style language but that then also can handle a non-local knowledge base. That is, there’s both solid theory and implementation (going by the presentation; I haven’t had time to check it out).

The main part of the cool keynote talk by Dittrich was on ‘the case for small data management’. From the who-wants-to-be-a-millionaire style popquiz question asking us to guess the typical size of a web database, it appeared to be only in the MBs (which most of us overestimated), and sort of explains why MySQL [that doesn’t scale well] is used rather widely. This results in a mismatch between problem size and tools. Another popquiz question answer: the 100MB RDF can just as well be handled efficiently by python, apparently. Interesting factoids, and one that has/should have as consequence we should be looking perhaps more into ‘small data’. He presented his work on PDbF as an example of that small data management. Very briefly, and based on my scribbles from the talk: its an enhanced pdf where you can access the raw data behind the graphs in the paper as well (it is embedded in it, with OLAP engine for posing the same and other queries), has a html rendering so you can hover over the graphs, and some more visualisation. If there’s software associated with the paper, it can go into the whole thing as well. Overall, that makes the data dynamic, manageable, traceable (from figure back to raw data), and re-analysable. The last part of his talk was on his experiences with the flipped classroom (more here; in German), but that was not nearly as fun as his analysis and criticism of the “big data” hype. I can’t recall exactly his plain English terms for the “four V4”, but the ‘lots of crappy XML data that changes’ remained of it in my memory bank (it was similar to the first 5 minutes of another keynote talk he gave).

 

Sessions

Sure, despite the notes on big data, there were presentations in the sessions that could be categorised under ‘big data’. Among others, Ajantha Dahanayake presented a paper on a proposal for requirements engineering for big data [1]. Big data people tend to assume it is just there already for them to play with. But how did it get there, how to collect good data? The presentation outlined a scenario-based backwards analysis, so that one can reduce unnecessary or garbage data collection. Dahanayake also has a tool for it. Besides the requirements analysis for big data, there’s also querying the data and the desire to optimize it so as to keep having fast responses despite its large size. A solution to that was presented by Reuben Ndindi, whose paper also won the best paper award of the conference [2] (for the Malawians at CS@UCT: yes, the Reuben you know). It was scheduled in the very last session on Friday and my note-taking had grinded to a halt. If my memory serves me well, they make a metric database out of a regular database, compute the distances between the values, and evaluate the query on that, so as to obtain a good approximation of the true answer. There’s both the theoretical foundation and an experimental validation of the approach. In the end, it’s faster.

Data and schema evolution research is alive and well, as were time series and temporal aspects. Due to parallel sessions and my time constraints writing this post, I’ll mention only two on the evolution; one because it was a very good talk, the other because of the results of the experiments. Kai Herrmann presented the CoDEL language for database evolution [3]. A database and the application that uses it change (e.g., adding an attribute, splitting a table), which requires quite lengthy scripts with lots of SQL statements to execute. CoDEL does it with fewer statements, and the language has the good quality of being relationally complete [3]. Lesley Wevers approached the problem from a more practical angle and restricted to online databases. For instance, Wikipedia does make updates to their database schema, but they wouldn’t want to have Wikipedia go offline for that duration. How long does it take for which operation, in which RDBMS, and will it only slow down during the schema update, or block any use of the database entirely? The results obtained with MySQL, PostgreSQL and Oracle are a bit of a mixed bag [4]. It generated a lively debate during the presentation regarding the test set-up, what one would have expected the results to be, and the duration of blocking. There’s some work to do there yet.

The presentation of the paper I co-authored with Pablo Fillottrani [5] (informally described here) was scheduled for that dreaded 9am slot the morning after the social dinner. Notwithstanding, quite a few participants did show up, and they showed interest. The questions and comments had to do with earlier work we used as input (the metamodel), qualifying quality of the conceptual model, and that all too familiar sense of disappointment that so few language features were used widely in publicly available conceptual models (the silver lining of excellent prospects of runtime usage of conceptual models notwithstanding). Why this is so, I don’t know, though I have my guesses.

 

And the other things that make conference useful and fun to go to

In short: Networking, meeting up again with colleagues not seen for a while (ranging from a few months [Robert Wrembel] to some 8 years [Nadeem Iftikhar] and in between [a.o., Martin Rezk, Bernhard Thalheim]), meeting new people, exchanging ideas, and the social events.

2008 was the last time I’d been in France, for EMMSAD’08, where, looking back now, I coincidentally presented a paper also on conceptual modelling languages and logic [6], but one that looked at comprehensive feature coverage and comparing languages rather than unifying. It was good to be back in France, and it was nice to realise my understanding and speaking skills in French aren’t as rusty as I thought they were. The travels from South Africa are rather long, but definitely worthwhile. And it gives me time to write blog posts killing time on the airport.

 

References

(note: most papers don’t show up at Google scholar yet, hence, no links; they are on the Springer website, though)

[1] Noufa Al-Najran and Ajantha Dahanayake. A Requirements Specification Framework for Big Data Collection and Capture. ADBIS’15. Morzy et al. (Eds.). Springer LNCS vol. 9282, .

[2] Boris Cule, Floris Geerts and Reuben Ndindi. Space-bounded query approximation. ADBIS’15. Morzy et al. (Eds.). Springer LNCS vol. 9282, 397-414.

[3] Kai Herrmann, Hannes Voigt, Andreas Behrend and Wolfgang Lehner. CoDEL – A Relationally Complete Language for Database Evolution. ADBIS’15. Morzy et al. (Eds.). Springer LNCS vol. 9282, 63-76.

[4] Lesley Wevers, Matthijs Hofstra, Menno Tammens, Marieke Huisman and Maurice van Keulen. Analysis of the Blocking Behaviour of Schema Transformations in Relational Database Systems. ADBIS’15. Morzy et al. (Eds.). Springer LNCS vol. 9282, 169-183.

[5] Pablo R. Fillottrani and C. Maria Keet. Evidence-based Languages for Conceptual Data Modelling Profiles. ADBIS’15. Morzy et al. (Eds.). Springer LNCS vol. 9282, 215-229.

[6] C. Maria Keet. A formal comparison of conceptual data modeling languages. EMMSAD’08. CEUR-WS Vol-337, 25-39.

The ontology-driven unifying metamodel of UML class diagrams, ER, EER, ORM, and ORM2

Metamodelling of conceptual data modelling languages is nothing new, and one may wonder why one would need yet another one. But you do, if you want to develop complex systems or integrate various legacy sources (which South Africa is going to invest more money in) and automate at least some parts of it. For instance: you want to link up the business rules modelled in ORM, the EER diagram of the database, and the UML class diagram that was developed for the application layer. Are the, say, Student entity types across the models really the same kind of thing? And UML’s attribute StudentID vs. the one in the EER diagram? Or EER’s EmployeesDependent weak entity type with the ORM business rule that states that “each dependent of an employee is identified by EmployeeID an the Dependent’s Name?

Ascertaining the correctness of such inter-model assertions in different languages does not require a comparison and contrast of their differences, but a way to harmonise or unify them. Some such models already exist, but they take subsets of the languages, whereas all those features do appear in actual models [1] (described here informally). Our metamodel, in contrast, aims to capture all constructs of the aforementioned languages and the constraints that hold between them, and generalize in an ontology-driven way so that the integrated metamodel subsumes the structural, static elements of them (i.e., the integrated metamodel has as them as fragments). Besides some updates to the earlier metamodel fragment presented in [2,3], the current version [4,5] also includes the metamodel fragment of their constraints (though omits temporal aspects and derived constraints). The metamodel and its explanation can be found in the paper in An ontology-driven unifying metamodel of UML Class Diagrams, EER, and ORM2 [4] that I co-authored with Pablo Fillottrani, and which was recently accepted in Data & Knowledge Engineering.

Methodologically, the unifying metamodel presented in An ontology-driven unifying metamodel of UML Class Diagrams, EER, and ORM2 [4], is ontological rather than formal (cf. all other known works). On that ‘ontology-driven approach’, here is meant the use of insights from Ontology (philosophy) and ontologies (in computing) to enhance the quality of a conceptual data model and obtain that ‘glue stuff’ to unify the metamodels of the languages. The DKE paper describes all that, such as: on the nature of the UML association/ORM fact type (different wording, same ontological commitment), attributes with and without data types, the plethora of identification constraints (weak entity types, reference modes, etc.), where can one reuse an ‘attribute’ if at all, and more. The main benefit of this approach is being able to cope with the larger amount of elements that are present in those languages, and it shows that, in the details, the overlap in features across the languages is rather small: 4 among the set of 23 types of relationship, role, and entity type are essentially the same across the languages (see figure below), and 6 of the 49 types of constraints. The metamodel is stable for the modelling languages covered. It is represented in UML for ease of communication, but, as mentioned earlier, it also has been formalised in the meantime [5].

Types of elements in the languages; black-shaded: entity is present in all three language families (UML, EER, ORM); dark grey: on two of the three; light grey: in one; while-filled: in none, but we added the more general entities to ‘glue’ things together. (Source: [4])

Metamodel fragment with some constraints among some of the entities. (Source [4])

The DKE paper also puts it in a broader context with examples, model analyses using the harmonised terminology, and a use case scenario that demonstrates the usefulness of the metamodel for inter-model assertions.

While the 24-page paper is rather comprehensive, research results wouldn’t live up to it if it didn’t uncover new questions. Some of them have been, and are being, answered in the meantime, such as its use for classifying models and comparing their characteristics [1,6] (blogged about here and here) and a rule-based approach to validating inter-model assertions [7] (informally here). Although the 3-year funded project on the Ontology-driven unification of conceptual data modelling languages—which surely contributed to realising this paper—just finished officially, we’re not done yet, or: more is in the pipeline. To be continued…

 

References

[1] Keet, C.M., Fillottrani, P.R. An analysis and characterisation of publicly available conceptual models. 34th International Conference on Conceptual Modeling (ER’15). Springer LNCS. 19-22 Oct, Stockholm, Sweden. (in press)

[2] Keet, C.M., Fillottrani, P.R. Toward an ontology-driven unifying metamodel for UML Class Diagrams, EER, and ORM2. 32nd International Conference on Conceptual Modeling (ER’13). W. Ng, V.C. Storey, and J. Trujillo (Eds.). Springer LNCS 8217, 313-326. 11-13 November, 2013, Hong Kong.

[3] Keet, C.M., Fillottrani, P.R. Structural entities of an ontology-driven unifying metamodel for UML, EER, and ORM2. 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, 188-199.

[4] Keet, C.M., Fillottrani, P.R. An ontology-driven unifying metamodel of UML Class Diagrams, EER, and ORM2. Data & Knowledge Engineering. 2015. DOI: 10.1016/j.datak.2015.07.004. (in press)

[5] Fillottrani, P.R., Keet, C.M. KF metamodel Formalization. Technical Report, Arxiv.org http://arxiv.org/abs/1412.6545. Dec 19, 2014. 26p.

[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). Springer LNCS. Poitiers, France, Sept 8-11, 2015. (in press)

[7] 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 LNCS 8620, 52-66. August 18-20, 2014, Prague, Czech Republic.

Formalization of the unifying metamodel of UML, EER, and ORM

Last year Pablo Fillottrani and I introduced an ontology-driven unifying metamodel of the static, structural, entities of UML Class Diagrams (v2.4.1), ER, EER, ORM, and ORM2 in [1,2], which was informally motivated and described here. This now also includes the constraints and we have formalised it in First Order Predicate Logic to put some precision to the UML Class Diagram fragments and their associated textual constraints, which is described in the technical report of the metamodel formalization [3]. Besides having such precision for the sake of it, it is also useful for automated checking of inter-model assertions and computing model transformations, which we illustrated in our RuleML’14 paper earlier this year [4] (related blog post).

The ‘bummer’ of the formalization is that it probably requires an undecidable language, due to having formulae with five variables, counting quantifiers, and ternary predicates (see section 2.11 of the tech report for details). To facilitate various possible uses nevertheless, we therefore also made a slightly simpler OWL version of it (the modelling decisions are described in Section 3 of the technical report). Having that OWL version, it was easy to also generate a verbalisation of the OWL version of the metamodel (thanks to SWAT NL Tools) so as to facilitate reading of the ontology by the casually interested reader and the very interested one who doesn’t like logic.

Although our DST/MINCyT-funded South Africa-Argentina scientific collaboration project (entitled Ontology-driven unification of conceptual data modelling languages) is officially in its last few months by now, more results are in the pipeline, which I hope to report on shortly.

References

[1] Keet, C.M., Fillottrani, P.R. Toward an ontology-driven unifying metamodel for UML Class Diagrams, EER, and ORM2. 32nd International Conference on Conceptual Modeling (ER’13). 11-13 November, 2013, Hong Kong. Springer LNCS vol 8217, 313-326.

[2] Keet, C.M., Fillottrani, P.R. Structural entities of an ontology-driven unifying metamodel for UML, EER, and ORM2. 3rd International Conference on Model & Data Engineering (MEDI’13). September 25-27, 2013, Amantea, Calabria, Italy. Springer LNCS (in print).

[3] Fillottrani, P.R., Keet, C.M. KF metamodel formalization. Technical report, Arxiv.org http://arxiv.org/abs/1412.6545. Dec 19, 2014, 26p.

[4] 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 LNCS 8620, 52-66. August 18-20, 2014, Prague, Czech Republic.

A metamodel-driven approach for linking conceptual data models

Interoperability among applications and components of large complex software is still a bit of a nightmare and a with-your-hands-in-the-mud scenario that no-one looks forward to—people look forward to already having linked them up, so they can pose queries across departmental and institutional boundaries, or even across the different data sets within a research unit to advance their data analysis and discover new things.

Sure, ontologies can help with that, but you have to develop one if none is available, and sometimes it’s not even necessary. For instance, you have an ER diagram for the database and a UML model for the business layer. How do you link up those two?

Superficially, this looks easy: an ER entity type matches up with a UML class, and an ER relationship with an UML association. The devil is in the details, however. To name just a few examples: how are you supposed to match a UML qualified association, an ER weak entity type, or an ORM join-subset constraint, to any of the others?

Within the South Africa – Argentina bilateral collaboration project (scope), we set out to solve such things. Although we first planned to ‘simply’ formalize the most common conceptual data modelling languages (ER, UML, and ORM families), we quickly found out we needed not just an ‘inventory’ of terms used in each language matched to one in the other languages, but also under what conditions these entities can be used, hence, we needed a proper metamodel. This we published last year at ER’13 and MEDI’13 [1,2], which I blogged about last year. In the meantime, we not only have finalized the metamodel for the constraints, but also formalized the metamodel, and a journal article describing all this is close to being submitted.

But a metamodel alone doesn’t link up the conceptual data models. To achieve that, we, Pablo Follottrani and I, devised a metamodel-driven approach for conceptual model interoperability, which uses a formalised metamodel with a set of modular rules to mediate the linking and transformation of elements in the conceptual models represented in different languages. This also simplifies the verification of inter-model assertions and model conversion. Its description has recently been accepted as a full paper at the 8^th International Web Rule Symposium 2014 (RuleML’14) [3], which I’ll present in Prague on 18 August.

To be able to assert a link between two entities in different models and evaluate automatically (or at least: systematically) whether it is a valid assertion and what it entails, you have to know i) what type of entities they are, ii) whether they are the same, and if not, whether one can be transformed into the other for that particular selection. So, to be able to have those valid inter-model assertions, an approach is needed for transforming one or more elements of a model in one language into another. The general idea of that is shown in the following picture, and explained briefly afterward.

Overview of the approach to transform a model represented in language A to one in language B, illustrated with some sample data from UML to ORM2 (Fig 1 in [3])

We have three input items (top of the figure, with the ovals), then a set of algorithms and rules (on the right), and two outputs (bottom, green). The conceptual model is provided by the user, the formalized metamodel is available and a selection of it is included in the RuleML’14 paper [3], and the “vocabulary containing a terminology comparison” was published in ER’13 [1]. Our RuleML paper [3] zooms in on those rules for the algorithms, availing of the formalized metamodel and vocabulary. To give a taste of that (more below): the algorithm has to know that a UML class in the diagram can be mapped 1:1 to a ORM entity type, and that there is some rule or set of rules to transform a UML attribute to an ORM value type.

This is also can be used for the inter-model assertions, albeit in a slightly modified way overall, which is depicted below. Here we use not only the formalised metamodel and the algorithms, but also which entities have 1:1 mappings, which are equivalent but need several steps (called transformations), and which once can only be approximated (requiring user input), and it can be run in both directions from one fragment to the other (one direction is chosen arbitrarily).

Overview for checking the inter-model assertions, and some sample data, checking whether the UML Flower is the same as the ORM Flower (Fig. 2 in [3]).

The rules themselves are not directly from one entity in one model to another entity in another, as that would become too messy, isn’t really scalable, and would have lots of repetitions. We use the more efficient way of declaring rules for mapping a conceptual data model entity into its corresponding entity in the metamodel, do any mapping, transformation, or approximation there in the metamodel, and then map it into the matching entity in the other conceptual data model. The rules for the main entities are described in the paper: those for object types, relationships, roles, attributes, and value types, and how one can use those to build up more complex ones for validation of intermodal assertions.

This metamodel-mediated approach to the mappings is one nice advantage of having a metamodel, but one possibly could have gotten away with just having an ‘inventory’ of the entities, not all the extra effort with a full metamodel. But there are benefits to having that metamodel, in particular when actually validating mappings: it can drive the validation of mappings and the generation of model transformations thanks to the constraints declared in the metamodel. How this can work, is illustrated in the following example, showing one example of how the “process mapping assertions using the transformation algorithms” in the centre-part of Fig. 2, above, works out.

Example. Take i) two models, let’s call them Ma and Mb, ii) an inter-model assertion, e.g., a UML association Ra and an ORM fact type Rb, ii) the look-up list with the mappings, transformations, approximations, and the non-mappable elements, and iii) the formalised metamodel. Then the model elements of Ma and Mb are classified in terms of the metamodel, so that the mapping validation process can start. Let us illustrate that for some Ra to Rb (or vv.) mapping of two relationships.

1. For the vocabulary table, we see that UML association and ORM fact type correspond to Relationship in the metamodel, and enjoy a 1:1 mapping. The ruleset that will be commenced with are R1 from UML to the metamodel and 2R to ORM’s fact type (see rules in the paper).
2. The R1 and 2R rules refer to Role and Object type in the metamodel. Now things become interesting. The metamodel has represented that each Relationship has at least two Roles, which there are, and each one causes the role-rules to be evaluated, with Ro1 of Ra’s two association ends into the metamodel’s Role and 2Ro to ORM’s roles (‘2Ro’ etc. are the abbreviations of the rules; see paper [3] for details).
3. The metamodel asserts that Role must participate in the rolePlaying relationship and thus that it has a participating Object type (possibly a subtype thereof) and, optionally, a Cardinality constraint. Luckily, they have 1:1 mappings.
4. This, in turn causes the rules for classes to be evaluated. From the classes, we see in the metamodel that each Object type must have at least one Identification constraint that involves one or more attributes or value types (which one it is has, been determined by the original classification). This also then has to be mapped using the rules specified.

This whole sequence was set in motion thanks to the mandatory constraints in the metamodel, having gone Relationship to Role to Object type to Single identification (that, in turn, consults Attribute and Datatype for the UML to ORM example here). The ‘chain reaction’ becomes longer with more elaborate participating entities, such as a Nested object type.

Overall, the whole orchestration is no trivial matter, requiring all those inputs, and it won’t be implemented in one codefest on a single rainy Sunday afternoon. Nevertheless, the prospect for semantically good (correct) inter-model assertions across conceptual data modeling languages and automated validation thereof is now certainly a step closer to becoming a reality.

References

[1] Keet, C.M., Fillottrani, P.R. Toward an ontology-driven unifying metamodel for UML Class Diagrams, EER, and ORM2. 32nd International Conference on Conceptual Modeling (ER’13). W. Ng, V.C. Storey, and J. Trujillo (Eds.). Springer LNCS 8217, 313-326. 11-13 November, 2013, Hong Kong.

[2] Keet, C.M., Fillottrani, P.R. Structural entities of an ontology-driven unifying metamodel for UML, EER, and ORM2. 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, 188-199.

[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 LNCS 8620, 52-66. August 18-20, 2014, Prague, Czech Republic.

Book chapter on conceptual data modeling for biology published

Just a quick note that my book chapter on “Ontology-driven formal conceptual data modeling for biological data analysis” finally has been published in the Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological Data (edited by Mourad Elloumi and Albert Y. Zomaya). A summary of the chapter’s contents is described in an earlier blog post from little over two years ago, and I’ve put the preprint online.

The whole book is an impressive 1192 pages consisting of 48 chapters of about 25 pages each, which are grouped into three main sections. The first section, Biological data pre-processing, has four parts: biological data management, biological data modeling (which includes my chapter), biological feature extraction, and biological feature selection. The second section, biological data mining, has six parts: Regression Analysis of Biological Data, Biological Data Clustering, Biological Data Classification, Association Rules Learning from Biological Data, Text Mining and Application to Biological Data, and High-Performance Computing for Biological Data Mining. The third section, biological data post-processing, has only one part: biological knowledge integration and visualization. (check the detailed table of contents). Happy reading!

Notes on a successful ER 2013 conference

Unlike two other conferences earlier this year, the 32nd International Conference on Conceptual Modeling (ER’13) in Hong Kong, held 11-13 Nov, was a success: good presentations, inspiring discussions, new ideas, follow-ups, and an enjoyable crowd. As a bonus, the paper Pablo Fillottrani and I wrote on metamodelling [1] was nominated for best paper award. I’ve posted about our paper earlier, so I will highlight some of the other papers.

There were two sessions on ontology-driven conceptual modelling, of which one ran concurrent with the reasoning over conceptual data models. It was a tough choice, but in the end I attended both ontology-based conceptual modelling sessions. Skimming and reading through the three reasoning papers from John Mylopoulos and co-authors, they covered reasoning with decision-theoretic goals, reasoning with business plans, and the third was about automated reasoning for regulatory compliance, like in law and for answering questions such as ‘given situation S, what are alternative ways to comply with law L?’ [2]. Regarding the latter, there are models of the law represented in the Nomos 2 modelling language, which were formalized and sent to the automated reasoner, being the off-the-shelf Datalog-based reasoner DLV. It was demonstrated that it is actually feasible to do this, taking into account scalability. These are encouraging results for automated reasoning with such conceptual models.

The ontology-based modeling papers were varied. There were some fundamental results on a first extension of the UFO foundational ontology for conceptual data modeling of events [3], presented by Giancarlo Guizzardi, that has been used successfully in other projects, and our ontology-driven metamodelling, also using philosophy directly (notably, the positionalism of relations and quality properties) [1]. A ‘merger’ of ontology, information systems, and linked data was presented by Chiara Renso who talked about the Baquara ontology to help conceptual analysis of movement of people talking about some entity at a certain location [4], which won the best paper award. A use case of improving a conceptual data model using UFO was presented by Oscar Pastor [5], using an earlier developed conceptual model of the human genome. Not that I agree with Gene being a “collective”, but, overall, it gives a clear example how a model may be enhanced and indeed lays bare underlying assumptions and understanding that are missed in ‘plain’ conceptual modelling.

Besides ontology-driven conceptual modeling, there were four papers on fundamentals of conceptual modeling. One of the topics was about conceptual modeling and concepts [6], presented by Chris Partridge. To its credit, the paper refines some notions of concepts I wasn’t aware of, but I have grown a bit tired of the concept vs universal debate due to its intense discussions in ontology engineering (see links to debates and reference here). Roman Lukyanenko proposed a new way for conceptual modeling: instead of top-down, go bottom-up and gather the classes and attributes from the crowd using citizen science and free-form annotations without any form of curation [7]. It’s on the other end of the spectrum compared to standard conceptual data modeling, which is a bit too loose to my liking especially because of the lack of curation of proposed terms, but a hybrid certainly may be useful.  Not in this session, but somewhat related, was Tilmann Zäschke’s presentation about optimizing conceptual data models using the actual database [8]. They proposed a method and patterns for updating the conceptual data model based on usage of the database (including path navigation), using DBLP as a case study.

There were two sessions on business process modeling and two sessions on applications, one on network modeling, security, data semantics, and a demo session, several keynotes, workshops, and panels that partially overlapped with other sessions that I don’t have the time for writing up the notes here. I did go to the panel on “open models”, or: why is there open source software, but hardly any open source conceptual models? I plan to get back to this question in a later post.

The food was good, and so were the generous reception and social dinner (eating some sort of a sweet bean soup for desert was a bit curious, though), and it was great to meet again with people I’ve met before and to finally meet several people in person of whom I only had read and cited papers over the years, including Brian Henderson-Sellers, Veda Storey, Sudha Ram, Antoni Olivé, and Peter Chen. Even though ER’14 is in the USA next year (Atlanta), I may give it a try anyway.

References

(note: most of the links point to the version at Springer; search again later or ask the authors for a free copy. In fact, it smells as if this is due to a collaboration between Google Scholar and Springer: when I search for my own paper, whose CRC is online since the blog post about it in August, GS pretends it does not exist either, idem for Zäschke’s paper.)

[1] Keet, C.M., Fillottrani, P.R. Toward an ontology-driven unifying metamodel for UML Class Diagrams, EER, and ORM2. 32nd International Conference on Conceptual Modeling (ER’13). 11-13 November, 2013, Hong Kong. Springer LNCS vol 8217, 313-326.

[2] Siena, A., Ingolfo, A, Perini, A, Susi, A, Mylopoulos, J. Automated reasoning for regulatory compliance. ER’13, Springer LNCS vol 8217, 47-60.

[3] Guizzardi, G., Wagner, G., de Almeida Falbo, R., Guizzardi, R.S.S., Almeida, J.P.A. Towards ontological foundations for the conceptual modeling of events. ER’13, Springer LNCS vol 8217, 327-341.

[4] Fileto, R., Kruger, M., Pelekis, N., Theodoridis, Y., Renso, C. Baquara: a holistic ontological framework for movement analysis using linked data. ER’13, Springer LNCS vol 8217, 342-355.

[5] Martinez Ferrandis, A.M., Pastor Lopez, O., Guizzardi, G.. Applying the principles of an ontology-based approach. ER’13, Springer LNCS vol 8217, 471-478.

[6] Partridge, C., Gonzalez-Perez, C., Henderson-Sellers, B. Are conceptual models concept models? ER’13, Springer LNCS vol 8217, 96-105.

[7] Lukyanenko, R. Parsons, J. Is traditional conceptual modeling becoming obsolete? ER’13, Springer LNCS vol 8217, 61-73.

[8] Zäschke, T., Leone, S., Gmunder, T., Norrie, M.C.. Optimizing conceptual data models through profiling in object databases. ER’13, Springer LNCS vol 8217, 284-297.

Mixed experiences with conferences and traveling

I just made it back to South Africa from the Knowledge Engineering and Ontology Development (KEOD’13) conference in Vilamoura, Portugal, and subsequent Model & Data Engineering (MEDI’13) conference in Amantea, Calabria, Italy. I had two papers at each conference (briefly described here, here, and here, in previous posts) and I suppose I should count myself lucky to have (barely) sufficient research funds to make such a trip.

If I had the choice again but with the foreknowledge of what was in store, I’d skip KEOD’13, regardless of the fact that the Portuguese airline TAP made a bit of a mess of my travel and, jointly with Alitalia, ‘lost’ my baggage for a while (and being unresponsive upon inquiry, and still are). Analysing the twice re-labeled baggage tags once it arrived two days later, though, it appeared to be Alitalia who had let me wait one day more, who did not update on the status of the luggage either, and their lost & found at Lamezia Terme airport could not be bothered to bring it to the hotel, though they ought to have done so. Meanwhile, I’d done a bit of guilt-free shopping in Amantea so as to wear something else than the same clothes I traveled in; and the clothes even fit!

For the remainder of the post: if you want to read about me complaining about a conference, then simply read on, if you want to read a more typical conference report, then scroll to the MEDI’13 section, further below.

KEOD’13

KEOD appeared to be a lucrative business for the organizers rather than a full-fledged conference. It started with an increase in the registration fees after submission, amounting to 525 euro without lunch (another 70 euro) or social dinner (another 75 euro), and another 290 euro for any additional paper. But we were already sucked into it and didn’t want to go through the whole process of resubmitting one of the papers elsewhere, so I ended up forking out 815 euro just for the registration (with a bad Rand->Euro exchange rate to boot). The internet connection was close to zero bits per hour, so that wasn’t taking up the lion’s share of the conference registration either. The ‘welcome reception’—included in the price—consisted of two jugs of ice tea, two jugs of juice, and three bottles of water, with a few snacks—even the alcohol-free welcome receptions in the USA do better than that. Now, I know that top conferences such as KCAP, CIKM and cs. do tend to be pricy, but KEOD is not a top conference, despite the claimed statistics in the proceedings that there was an acceptance rate for the joint IC3K conferences for long papers of 10% and, including regular papers, about 35% overall (the paper with my MSc student, Zubeida Khan [1] was a full/long paper of 12 pages double-column and the other one [2] a regular 8 pages).

The titles of the papers certainly sounded interesting, but most of the presentations did not live up to the promise, multiple sessions had at least one paper where the author had not shown up, and there were only about 20-25 KEOD attendees, which is a generous rough headcount of the attendees in the plenary sessions (quite a few who did show up did not stay for the whole conference). Or: at best, it was more like an expensive and not well-organised workshop-level event. The best paper award went to a groupie of one of the two organizers.

Of course, it’s your call to submit for next year’s installment or not, and my opinion is just that. When inquiring with a few people who had attended a previous installment, the papers were then “a bit of a mixed bag”, so maybe only this year was a temporary dip, not indicative of a general trend. Regardless the relatively average low quality, it’s still too expensive.

On the bright side, Tahir Khan, with whom I have a CIKM’13 paper jointly with Chiara Ghidini [3] (topic of a next post), was attending as well, and the three of us (Zubeida, Tahir, and me) have set out some nice tasks for research and a new prospective paper (assuming we’ll get interesting results, that is).

MEDI’13

Regarding MEDI’13, organized by Alfredo Cuzzocrea and Sofian Maabout, there was a last-minute wobble that got ironed out, and the local organization, the quality of the papers and presentations, and the atmosphere at the conference was substantially better. It was a really enjoyable event.

The conferences organized in Italy that I have attended over the years (among others, AI*IA’07, AI*IA’09, and DL’07) were always good with the food and drinks, and MEDI was no exception and they even had additional entertainment with folkloristic music and dance at the welcome reception and (huge) social dinner. And now I know how tartufo is really supposed to taste (sorry Bolzanini, but none of the restaurants and gelateria up there come even close).

So, let me mention some of the presentations and papers that piqued my interest—what I normally write about when writing a blog post about a conference.

The first keynote was given by Gottfried Vossen from the University of Münster on “Model engineering as a social activity: the case of business processes”, which focused on the role of models in computer science, the move from modeling to model engineering (a more structured and rigorous activity than just modelling), the Horus method from modelling, and closing with ideas about inserting crowdsourcing into the process. A side-note in the presentation, but worthwhile linking here anyway, is that there’s now even also algorithm engineering. The second keynote was given by Dimitrios Gunopulos from the University of Athens about “Analyzing massive streaming heterogeneous data: towards a new computing model for computational sustainability”. This talk was about document retrieval, e.g., newspaper articles, after first having established a baseline of the occurrences of certain keywords over time, and then detecting ‘bursts’ of keyword incidences in certain smaller intervals, whose documents then get ranked higher in the retrieval of relevant documents.

What I’ll certainly read in more detail over the upcoming days is the persistent meta-model system paper [4] that enhances the OntoDB/OntoQL system, as our papers were about metamodels [5] and the model repository ROMULUS [6]. Likewise, I’m biased toward reading the paper presented by Zdenek Rybola [7], for it deals with ontology-driven conceptual data modeling with UML using OntoUML, aiming to port the ontology-driven conceptual model toward improvements in implementations. Further, [8] proposes to use Time Petri Nets to verify temporal coherence in a SMIL (synchronized multimedia integration language, from the W3C) multimedia document presentation through the transformation of the SMIL document into TPN.

I was not the only one traveling to MEDI’13 all the way from South Africa: Kobamelo Moremedi, a MSc student at UNISA, presented his work about various possible diagrammatic notations for Z [9]. Staying with the topic of South Africa, and highlighting some more immediate possible relevance and link to societal use of material presented at the conference (cf. talking about foundational ontologies, ETL, architectural documentation, database integrity and the like, where the chain toward practical relevance is a bit longer): Andrea Nucita presented a Markov chain-based model for individual patients and agent-based model for interaction among individuals so as to predict HIV/AIDS epidemiological trends and simulate the various epidemiological scenarios [10]. They used actual clinical data to test their model and it showed that, as one would expect, expanding access to testing and therapy will influence the evolution of the epidemiology toward limiting the spread of HIV/AIDS, therewith also corroborating other works on the same topic.

In closing

Overall, the experience was quite mixed in the relatively long trip. I haven’t written negatively about a conference before—if it wasn’t great, then I didn’t write about it, although not having written about an event does not imply it wasn’t great (e.g., KCAP’13 got cancelled)—but KEOD really was a disappointment on multiple aspects. I don’t know whether it’s in the same league as WORLDCOMP, IASTED, IARIA and the like, as I’ve never attended those, but going by reviews of those events, if the KEOD organisers don’t get their act together and improve upon it, they will be down there in the same league. As for MEDI, if I have enough research money and suitable material, I may submit again for next year’s installment in Cyprus or thereafter, as the quality, relevance, and experience was better than I thought it would be.

References

[1] Khan, Z.C., Keet, C.M. Addressing issues in foundational ontology mediation. Fifth International Conference on Knowledge Engineering and Ontology Development (KEOD’13). 19-22 September, Vilamoura, Portugal.

[2] Keet, C.M., Suárez Figueroa, M.C., and Poveda-Villalón, M. (2013) The current landscape of pitfalls in ontologies. International Conference on Knowledge Engineering and Ontology Development (KEOD’13). 19-22 September, Vilamoura, Portugal.

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

[4] Youness Bazhar, Yassine Ouhammou, Yamine Aït-Ameur, Emmanuel Grolleau, and Stéphane Jean. Persistent Meta-Modeling Systems as Heterogeneous Model Repositories. 3rd International Conference on Model & Data Engineering (MEDI’13). September 25-27, 2013, Amantea, Calabria, Italy. Springer LNCS 8216, 25-37.

[5] Keet, C.M., Fillottrani, P.R. Structural entities of an ontology-driven unifying metamodel for UML, EER, and ORM2. 3rd International Conference on Model & Data Engineering (MEDI’13). September 25-27, 2013, Amantea, Calabria, Italy. Springer LNCS 8216, 188-199.

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

[7] Robert Pergl, Tiago Prince Sales, and Zdenek Rybola. Towards OntoUML for Software Engineering: From Domain Ontology to Implementation Model. 3rd International Conference on Model & Data Engineering (MEDI’13). September 25-27, 2013, Amantea, Calabria, Italy. Springer LNCS 8216, 249-263.

[8] Abdelghani Ghomari, Naceur Belheziel, Fatma-Zohra Mekahlia, and Chabane Djeraba. Towards a Formal Approach for Verifying Temporal Coherence in a SMIL Document Presentation. 3rd International Conference on Model & Data Engineering (MEDI’13). September 25-27, 2013, Amantea, Calabria, Italy. Springer LNCS 8216, 132–146.

[9] Kobamelo Moremedi and John Andrew van der Poll. Transforming Formal Specification Constructs into Diagrammatic Notations. 3rd International Conference on Model & Data Engineering (MEDI’13). September 25-27, 2013, Amantea, Calabria, Italy. Springer LNCS 8216, 212–224.

[10] Andrea Nucita, Giuseppe M. Bernava, Pietro Giglio, Marco Peroni, Michelangelo Bartolo, Stefano Orlando, Maria Cristina Marazzi, and Leonardo Palombi. A Markov Chain Based Model to Predict HIV/AIDS Epidemiological Trends. 3rd International Conference on Model & Data Engineering (MEDI’13). September 25-27, 2013, Amantea, Calabria, Italy. Springer LNCS 8216, 225-236.

Ontologies and conceptual modelling workshop in Pretoria

A first attempt was made in South Africa to get researchers and students together who are interested in, and work on, ontologies, conceptual data modelling, and the interaction between the two, shaped in the form of an interactive Workshop on Ontologies and Conceptual Modelling on 15-16 Nov 2012 in Tshwane/Pretoria (part of the Forum on AI Research (FAIR’12) activities). The participants came from, the University of KwaZulu-Natal, University of South Africa, Fondazione Bruno Kessler, and different research units of CSIR-Meraka (where the workshop was organized and held), and the remainder of the post contains a brief summary of the ongoing and recently competed research that was presented at the workshop.

The focus on the first day of the workshop was principally on the modeling itself, modeling features, and some prospects for reasoning with that represented information and knowledge. I had the honour to start the sessions with the talk of the paper that recently won the best paper award at EKAW’12 on “Detecting and Revising Flaws in OWL Object Property Expressions” [1], which was followed by Zubeida Khan’s talk of our paper at EKAW’12 about ONSET: Automated Foundational Ontology Selection and Explanation [2] that was extended with a brief overview of her MSc thesis on an open ontology repository for foundational ontologies that is near completion. Tahir Khan, who is a visiting PhD student (at UKZN) from Fondazione Bruno Kessler in Trento, gave the third talk within the scope of ontology engineering research. The main part of Tahir’s presentation consisted of an overview of his template-based approach for ontology construction that aims to involve the domain experts in the modeling process of domain ontology development in a more effective way [3]. This was rounded off with a brief overview of one component of this approach, which has to do with being able to select the right DOLCE category when one adds a new class to the ontology and integrating OntoPartS for selecting the appropriate part-whole relation [4] into the template-based approach and its implementation in the MoKi ontology development environment.

There were three talks about representation of and reasoning over defeasible knowledge. Informally, defeasible information representation concerns the ability to represent (and, later, reason over) ‘typical’ or ‘usual’ cases that do have exceptions; e.g., that a human heart is typically positioned left, but in people with sinus inversus, it is positioned on the right-hand side in the chest, and policy rules, such as that, normally, users have access to, say, documents of type x, but black-listed users should be denied access. Giovanni Casini presented recent results on extending the ORM2 conceptual data modeling language with the ability to represent such defeasible information [5], which will be presented also at the Australasian Ontology Workshop in early December. Tommie Meyer focused on the reasoning about it in a Description Logics context ([6] is somewhat related to the talk), whereas Ivan Varzinczak looked at the propositional case with defeasible modalities [7], which will be presented at the TARK’13 conference.

Arina Britz and I also presented fresh-fresh in-submission stage results. Arina gave a presentation about semantic similarities and ‘forgetting’ in propositional logical theories (joint work with Ivan Varzinczak), and I presented a unifying metamodel for UML class diagrams v2.4.1, EER, and ORM2 (joint work with Pablo Fillottrani).

Deshen Moodley gave an overview of the HeAL lab at UKZN and outlined some results from his students Ryan Chrichton (MSc) and Ntsako Maphophe (BSc(honours)). Ryan designed an architecture for software interoperability of health information systems in low-resource settings [8]. Ntsako has developed a web-based ontology development and browsing tool for lightweight ontologies stored in a relational database that was tailored to the use case of a lightweight ontology of software artifacts. Ken Halland presented and discussed his experiences with teaching a distance-learning-based honours-level ontology engineering module at UNISA.

Overall, it was a stimulating and interactive workshop that hopefully can, and will, be repeated next year with an even broader participation than this year’s 16 participants.

References

[1] C. Maria Keet. Detecting and Revising Flaws in OWL Object Property Expressions. Proc. of EKAW’12. Springer LNAI vol 7603, pp2 52-266.

[2] Zubeida Khan and C. Maria Keet. ONSET: Automated Foundational Ontology Selection and Explanation. Proc. of EKAW’12. Springer LNAI vol 7603, pp 237-251.

[3] Tahir Khan. Involving domain experts in ontology construction: a template-based approach. Proc. of ESWC’12 PhD Symposium. 28 May 2012, Heraklion, Crete, Greece. Springer, LNCS 7295, 864-869.

[4] C. Maria Keet, Francis Fernandez-Reyes, and Annette Morales-Gonzalez. Representing mereotopological relations in OWL ontologies with OntoPartS. In: Proc. of ESWC’12, 29-31 May 2012, Heraklion, Crete, Greece. Springer, LNCS 7295, 240-254.

[5] Giovanni Casini and Alessandro Mosca. Defeasible reasoning for ORM. In: Proc. of AOW’12. Dec 4, Sydney, Australia

[6] Moodley, K., Meyer, T., Varzinczak, I. A Defeasible Reasoning Approach for Description Logic Ontologies. Proc. of SAICSIT’12. Pretoria.

[7] Arina Britz and Ivan Varzinczak. Defeasible modalities. Proc. of TARK’13, Chennai, India.

[8] Ryan Crichton, Deshendran Moodley, Anban Pillay, Richard Gakuba and Christopher J Seebregts. An Interoperability Architecture for the Health Information Exchange in Rwanda. In Foundations of Health Information Engineering and Systems. 2012.