DOGMA-MESS: A Tool for Fact-Oriented Collaborative Ontology Evolution ⋆ Pieter De Leenheer and Christophe Debruyne Semantics Technology and Applications Research Laboratory (STARLab) Vrije Universiteit Brussel, Pleinlaan 2, Brussels 5, Belgium Abstract. Ontologies being shared formal specifications of a domain, are an im- portant lever for developing meaningful internet systems. However, the problem is not in what ontologies are, but how they become operationally relevant and sus- tainable over longer periods of time. Fact-oriented and layered approaches such as DOGMA have been successful in facilitating domain experts in representing and understanding semantically stable ontologies, while emphasising reusability and scalability. DOGMA-MESS, extending DOGMA, is a collaborative ontol- ogy evolution methodology that supports stakeholders in iteratively interpreting and modeling their common ontologies in their own terminology and context, and feeding back these results to the owning community. In this paper we extend DOGMA Studio with a set of collaborative ontology evolution support modules. 1 Introduction Ontologies, being formal, computer-based specifications of shared conceptualisations of the worlds under discussion, are an important lever for developing meaningful com- munication between people and internet systems [10, 9]. However, the problem is not in what ontologies are, but how they become community-grounded resources of semantics, and at the same time be made operationally relevant and sustainable over longer periods of time. The state of the art in ontology evolution regards change as a pain that must be technically alleviated by presuming a project-like practice where ontologies are cre- ated and deployed in discrete steps [6]. The requirements for the “ontology project” are usually deduced from the technical web service requirements that were solo-designed by a single application developer, rather than collaboratively grounding them directly in the community. In the DOGMA framework [13], fact-oriented approaches such as NIAM/ORM [21,11] have been proven useful for engineering ontologies. A key char- acteristic here is that the analysis of information is based on natural language facts. This brings the advantage that “layman” domain experts are facilitated in building, in- terpreting, and understanding attribute-free, hence semantically stable ontologies, using their own terminology. DOGMA-MESS is a teachable and repeatable collaborative on- tology evolution methodology that supports stakeholders in interpreting and modeling ⋆ We would like to thank Stijn Christiaens for his valuable comments on the usability of the tool. The research described in this paper was partially sponsored by the EC projects FP6 IST PROLIX (FP6-IST-027905).