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Stoffel, Kilian

Nom
Stoffel, Kilian
Affiliation principale
Fonction
Professeur ordinaire
Email
Kilian.STOFFEL@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/593

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  • Publication
    Métadonnées seulement
    Procedural extensions for executable ontologies in conventional software development
    (Belfast, North Ireland: World Scientific, 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.
  • Publication
    Métadonnées seulement
    OntoJIT: Parsing Native OWL DL into Executable Ontologies in an Object Oriented Paradigm
    (Bologna, Italy: Springer, Cham, 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.
  • Publication
    Métadonnées seulement
  • Publication
    Métadonnées seulement
    Business process modelling for academic virtual organizations
    (Boston: Springer, 2008)
    Cotofrei, Paul 
    ;
    Stoffel, Kilian 
    The increasing mobility due to Bologna process forces the academic partners to increase the inter-operability of their administrative processes, by interacting through a collaborative networks and therefore acting as an academic virtual organization. To facilitate the communication and the comprehension of the administrative processes between the components of the CN, the Business Process Modelling Notation (BPMN) is proposed in this paper as a standard graphical model for administrative processes and transactions. The adaptability of this standard for academic processes and the difficulties of "translating" the actual administrative models (legal texts) in BPMN diagrams are analysed.
  • Publication
    Métadonnées seulement
    First-Order Logic Based Formalism for Temporal Data Mining
    (Berlin: Springer-Verlag, 2005)
    Cotofrei, Paul 
    ;
    Stoffel, Kilian 
    In this article we define a formalism for a methodology that has as purpose the discovery of knowledge, represented in the form of general Horn clauses, inferred from databases with a temporal dimension. To obtain what we called temporal rules, a discretisation phase that extracts events from raw data is applied first, followed by an induction phase, which constructs classification trees from these events. The theoretical framework we proposed, based on first-order temporal logic, permits us to define the main notions (event, temporal rule, constraint) in a formal way. The concept of consistent linear time structure allows us to introduce the notions of general interpretation and of confidence. These notions open the possibility to use statistical approaches in the design of algorithms for inferring higher order temporal rules, denoted temporal meta-rules.
  • Publication
    Métadonnées seulement
    Higher Order Temporal Rules
    (Berlin: Springer-Verlag Berlin, 2003)
    Cotofrei, Paul 
    ;
    Stoffel, Kilian 
    ;
    Sloot, Peter
    ;
    Abramson, David
    ;
    Bogdanov, Alexander
    ;
    Dongarra, Jack
    ;
    Zomaya, Albert
    ;
    Gorbachev, Yuri
    The theoretical framework we proposed, based on first-order temporal logic, permits to define the main notions used in temporal data mining (event, temporal rule) in a formal way. The concept of consistent linear time structure allows us to introduce the notions of general interpretation and of confidence. These notions open the possibility to use statistical approaches in the design of algorithms for inferring higher order temporal rules, denoted temporal meta-rules.
  • Publication
    Métadonnées seulement
    An advanced stemming algorithm for creating concept signatures of medical terms
    (Godalming: Springer-Verlag London Ltd, 2002)
    Kurz, Thorsten
    ;
    Stoffel, Kilian 
    ;
    Bramer, Max
    ;
    Coenen, Frans
    ;
    Preece, Alun
    We present a stemming algorithm that does not only remove the endings of words, but also separates prefixes and suffixes from the remaining stem. The output of this algorithm creates more precise concept signatures for indexing and classifying documents. The algorithm has been successfully tested with prefix and suffix lists for medical terms.
  • Publication
    Métadonnées seulement
    Selecting optimal split-functions for large datasets
    (Godalming: Springer-Verlag London Ltd, 2001)
    Stoffel, Kilian 
    ;
    Raileanu, Laura Elena
    ;
    Bramer, Max
    ;
    Preece, Alun
    ;
    Coenen, Frans
    Decision tree induction has become one of the most popular methods for classification and prediction. The key step in the process of inferring decision trees is finding the right criteria for splitting the training set into smaller and smaller subsets so that, ideally, all elements of a subset finally belong to one class. These split criteria can be defined in different ways (e.g. minimizing impurity of a subset, or minimizing entropy in a subset), and therefore they emphasize different properties of the inferred tree, such as size or classification accuracy. In this paper we analyze if the split functions introduced in a statistical and machine learning context are also well suited for a KDD context. We selected two well known split functions, namely Gini Index (CART) and Information Gain (C4.5) and introduced our own family of split functions and tested them on 9,000 data sets of different sizes (from 200 to 20, 000 tuples). The tests have shown that the two popular functions are very sensitive to the variation of the training set sizes and therefore the quality of the inferred trees is highly dependent on the training set size. At the same time however, we were able to show that the simplest members of the introduced family of split functions behave in a very predictable way and, furthermore, the created trees were superior to the trees inferred using the Gini Index or the Information Gain based on our evaluation criteria.