Purpose – The purpose of this paper is to present the MEMORAe project, the goal of which is to offer an alternative to the loss of competencies and knowledge in an organization.Design/methodology/approach – Within the project MEMORAe, interest was focused on the capitalization of knowledge and competencies in the context of an organization. The E‐MEMORAe environment was developed based on the concept of learning organizational memory. This environment is dedicated to be used by a semantic learning organization as support for competency‐based training. It is evaluated in this context.Findings – In the E‐MEMORAe environment, learning content is indexed by knowledge and competencies organized by means of ontologies. Learners can acquire thise knowledge and these competencies by doing different tasks, accessing different contents. In the memory, competencies are defined via the knowledge they enable to be put into practice.Practical implications – It is known that some industrial communities of practice are i...

Competencies management and learning organizational memory

https://www.hal.inserm.fr/inserm-00344293/file/jbi2008.pdf

Towards an ontology for sharing medical images and regions of interest in neuroimaging

There is an assumption that ontology developers will use a top-down approach by using a foundational ontology, because it purportedly speeds up ontology development and improves quality and interoperability of the domain ontology. Informal assessment of these assumptions reveals ambiguous results that are not only open to different interpretations but also such that foundational ontology usage is not foreseen in most methodologies. Therefore, we investigated these assumptions in a controlled experiment. After a lecture about DOLCE, BFO, and partwhole relations, one-third chose to start domain ontology development with an OWLized foundational ontology. On average, those who commenced with a foundational ontology added more new classes and class axioms, and significantly less object properties than those who started from scratch. No ontology contained errors regarding part-of vs. is-a. The comprehensive results show that the 'cost' incurred spending time getting acquainted with a foundational ontology compared to starting from scratch was more than made up for in size, understandability, and interoperability already within the limited time frame of the experiment.

https://link.springer.com/content/pdf/10.1007%2F978-3-642-21034-1_22.pdf

The use of foundational ontologies in ontology development: an empirical assessment

This article presents a formal ontology which accounts for the general nature of artefacts. The objective is to help structure application ontologies in areas where specific artefacts are present - in other words, virtually any area of activity. The conceptualization relies on recent philosophical and psychological research on artefacts, having resulted in a largely consensual theoretical basis. Furthermore, this ontology of artefacts extends the foundational DOLCE ontology and supplements its axiomatization. The conceptual primitives are as follows: artificial entity, intentional production of entities, (state of) capacity, capacity to play a role in actions of a given type, function and functional object. These primitives enable artefacts in general to be characterized as intentionally and successfully produced entities, and isolate an important subclass of “technical” artefacts to which a function is ascribed. Lastly, we emphasize the novelty of this ontology by comparing it with other works with similar objectives.

A formal ontology of artefacts

ion These are modules that are simpler or lighter than an original ontology due to hiding or deleting some detail. This hiding or deleting can be done systematically in different ways. We describe four options. First, one could create an abstraction module by targeting certain axioms; e.g., axioms that relate classes using object properties are removed. This results in a module with a decreased horizontal structure. Alternatively, one can create vocabulary abstraction modules, resulting from removing a certain type of entity from an ontology; e.g., the removal of individuals from an ontology. A computationally easier way of creating abstraction modules are high-level abstraction modules: entities that exist at a lower level in the hierarchy of the ontology, i.e., the more specific entities, are to 216 Chapter 11. Ontology modularisation be removed. This results in a module with the higher-level entities of the ontology. Conversely, a more refined and elaborate way is to create weighted modules, where some entities are deemed more important than others in an ontology. The importance may be specified by the developer’s preference in preserving entities. Expressiveness There are modules that are created by altering a module’s language expressive power. This then has an effect on what is represented in the ontology. There are two types: sub-language and feature modules. For sub-language modules, a module is created based on only prioritising the features of a sublanguage e.g., only preserving the axioms of an ontology that are OWL 2 EL. An example is OpenGalen [RRZvdH03], which is a module of GALEN in OWL 2 EL for the purpose of testing the lightweight ELK [KKS12] reasoner for ontologies within OWL 2 EL expressiveness. With feature modules, a module is created by limiting the language features but not based on a specific sub-language. For instance, the removal of cardinality constraints for simplification purposes.

An Introduction to Ontology Engineering

This report describes a new version of the OntoSpec methodology for ontology building Defined by the LaRIA Knowledge Engineering Team (University of Picardie Jules Verne, Amiens, France), OntoSpec aims at helping builders to model ontological knowledge (upstream of formal representation) The methodology relies on a set of rigorously-defined modelling primitives and principles Its application leads to the elaboration of a semi-informal ontology, which is independent of knowledge representation languages We recently enriched the OntoSpec methodology by endowing it with a new resource, the DOLCE top-level ontology defined at the LOA (IST-CNR, Trento, Italy) The goal of this integration is to provide modellers with additional help in structuring application ontologies, while maintaining independence vis-a-vis formal representation languages In this report, we first provide an overview of the OntoSpec methodology's general principles and then describe the DOLCE re-engineering process A complete version of DOLCE-OS (ie a specification of DOLCE in the semi-informal OntoSpec language) is presented in an appendix

https://hal.archives-ouvertes.fr/hal-00012203/document

Integration of the DOLCE top-level ontology into the OntoSpec methodology

Les travaux que nous menons visent a definir une approche centree-ontologie pour la construction de modeles d'expertise de la methode CommonKADS. Cette approche, nommee OntoKADS, repose sur une ontologie generale de la resolution de problemes, distinguant deux niveaux de conceptualisation : a un premier niveau " objet ", un ensemble de concepts rendent compte de classes de raisonnements et d'entites d'un domaine manipulees par ces raisonnements ; a un niveau " meta ", correspondant au niveau des primitives de modelisation de CommonKADS, un second ensemble de concepts rendent compte de classes de modeles de raisonnements. Dans cet article, nous focalisons notre presentation d'OntoKADS sur l'ontologie et plus particulierement sur les roles, cette primitive situee a l'interface des connaissances du domaine et de raisonnement, et dont le statut ontologique a fait l'objet de nombreux debats. Nous proposons tout d'abord un cadre ontologique coherent et global, permettant de rendre compte de cette primitive. Nous montrons ensuite comment la nouvelle caracterisation obtenue pour cette primitive permet de definir de nouvelles regles pour la construction de modeles d'expertise.

https://hal.inria.fr/hal-01026249/document

Gilles Kassel

Papers

Integration of the DOLCE top-level ontology into the OntoSpec methodology

OntoKADS : une ontologie générale de la résolution de problèmes.