Stoffel, Kilian

2018, Baset, Selena, Stoffel, Kilian

Ontologies have gone lengths in various areas of knowledge engineering, yet they are falling short of reaching an equal position as formal domain models in the landscape of enterprise software development. In this paper, we present an approach for integrating ontologies into the code space of conventional software. We argue that the limited adoption of ontologies in software development is partially due to the lack of imperative programming capabilities. We propose extending ontologies with procedural extensions by expressing them in an executable form. Finally, we discuss the advantages of this representation and the possibilities for further improvements.

2015-6-24, Calistru, Tudor, Cotofrei, Paul, Stoffel, Kilian

2013-5-20, Albertetti, Fabrizio, Stoffel, Kilian

2011, Cotofrei, Paul, Stoffel, Kilian

2018, De Santo, Alessio, Cotofrei, Paul, Stoffel, Kilian

2015-3-22, Raschip, Madalina, Croitoru, Cornelius, Stoffel, Kilian

2012-9-12, Grossrieder, Lionel, Albertetti, Fabrizio, Stoffel, Kilian, Ribaux, Olivier, Ioset, Sylvain

In this research, we attempt to study the contribution of data mining techniques in crime analysis and intelligence. It is an interdisciplinary project, combining forensic, criminological and computational methods. We search to develop a frame in which data mining techniques, driven by crime analysis and forensic processes, take an active part to data interpretation and information analysis (in order to extract knowledge). Realized in collaboration with the cantonal police forces of Vaud in Switzerland, the first phase of this project consists to focus on residential burglary data. The sample is provided by the Concept Intercantonal de Coordination Opérationnelle et Préventive (CICOP) database, which is the regional center for crime analysis in French-speaking part of Switzerland. The CICOP analysts use phenomenon codes to define a particular crime situation. These CICOP codes are directly related to the situational approaches in criminology. Concretely, we have three main purposes: residential burglary classification, new phenomena discovery, and series or trends detection. That brings, in first hand, to formalize processes identified in crime analysis with the help of a standard notation called Business Process Modeling Notation (BPMN). Then, different data mining techniques are tested on data, and assessed by confronting them with phenomena identified by police forces analysts. Finally, we make a criminological analysis on the results to check the consistency with main situational theories in criminology. Accuracy and results relevance exam is an important step, because the different data mining algorithms can generate trivial and unexplainable rules. We note then the need of a human interpretation, and in this case, of a criminological interpretation. The first results are hopeful and classification algorithms are effective to classify residential burglaries like CICOP analysts did it.

2016, Baset, Selena, Stoffel, Kilian

Despite meriting the growing consensus between researchers and practitioners of ontology modeling, the Web Ontology Language OWL still has a modest presence in the communities of “traditional” web developers and software engineers. This resulted in hoarding the semantic web field in a rather small circle of people with a certain profile of expertise. In this paper we present OntoJIT, our novel approach toward a democratized semantic web where we bring OWL ontologies into the comfort-zone of end-application developers. We focus particularly on parsing OWL source files into executable ontologies in an object oriented programming paradigm. We finally demonstrate the dynamic code-base created as the result of parsing some reference OWL DL ontologies.

2013-7-29, Calistru, Tudor, Cotofrei, Paul, Stoffel, Kilian

2012-5-13, Grossrieder, Lionel, Albertetti, Fabrizio, Stoffel, Kilian, Ribaux, Olivier, Ioset, Sylvain