Mounira Harzallah

The Unified Enterprise Modelling Language—Overview and further work

The Unified Enterprise Modelling Language (UEML) aims at supporting integrated use of enterprise and IS models expressed using different languages. To achieve this aim, UEML offers a hub through which modelling languages can be connected, thereby paving the way for also connecting the models expressed in those languages. This paper motivates and presents the most central parts of the UEML approach: a structured path to describing enterprise and IS modelling constructs; a common ontology to interrelate construct descriptions at the semantic level; a correspondence analysis approach to estimate semantic construct similarity; a quality framework to aid selection of languages; a meta-meta model to integrate the different parts of the approach; and a set of tools to aid its use and evolution. The paper also discusses the benefits of UEML and points to paths for further work.

Analysis and modeling of individual competencies: toward better management of human resources

A general approach is presented for the development of competence management information systems to enable competence management at all business control levels (i.e., strategic, tactical, and operational). Based on a state of the art review of competence definitions, a model named competence, resource, aspect, and individual (CRAI) for human resource competence is presented. For the sake of computerization, the CRAI model is fully defined using the set theory. It structures and formalizes the concept of competence and provides guidelines for its deployment in businesses when building a companys specific competence information system. Furthermore, the CRAI model is also analyzed on a number of queries frequently dealt with in competence management. To make them independent of any specific technology, these queries have also been formalized using the set theory. Finally, an extract of a full-scale industrial case study, on which the CRAI model has been tested, illustrates concepts discussed in the paper.

An ontology for enterprise and information systems modelling

The Unified Enterprise Modelling Language (UEML) aims to support precise semantic definition of a wide variety of enterprise- and IS-modelling languages. In the longer run, it is also intended as a hub for integrated use of enterprise and information system (IS) models expressed in different languages. To achieve this, UEML provides a common ontology that interrelates the semantics of many existing modelling languages. This paper presents the motivation and background for the UEML work. It then presents the structure and contents of UEMLs evolving common ontology, the Unified Enterprise Modelling Ontology (UEMO), which has been established through analyses of 130 modelling constructs from a selection of 10 enterprise- and IS-modelling languages. It goes on to discuss the current state of UEMO and its further evolution. Finally, conclusions and paths for further work are offered.

The Unified Enterprise Modelling Language – Overview and Further Work

The Unified Enterprise Modelling Language (UEML) aims to support integrated use of enterprise and IS models expressed in a variety of languages. To achieve this aim, UEML provides a hub through which different languages can be connected, thereby paving the way for connecting the models expressed in those languages. UEML offers a structured approach to describing enterprise and IS modelling constructs, a common ontology to interrelate construct descriptions at the semantic level, a correspondence analysis approach to estimate semantic construct similarity, a quality framework to aid selection of languages, a meta-meta model to organise the UEML and a set of tools to aid its use and evolution. This paper presents an overview of UEML and points to paths for further work.

A Tree-Based Similarity for Evaluating Concept Proximities in an Ontology

The problem of evaluating semantic similarity in a network structure knows a noticeable renewal of interest linked to the importance of the ontologies in the semantic Web. Different semantic measures have been proposed in the literature to evaluate the strength of the semantic link between two concepts or two groups of concepts within either two different ontologies or the same ontology. This paper presents a theoretical study synthesis of some semantic measures based on an ontology restricted to subsumption links. We outline some limitations of these measures and introduce a new one: the Proportion of Shared Specificity. This measure which does not depend on an external corpus, takes into account the density of links in the graph between two concepts. A numerical comparison of the different measures has been made on different large size samples from WordNet.

OntoMas: a tutoring system dedicated to ontology matching

Ontology matching is now a core question in most of the applications that require semantic interoperability. To deal with this problem, a lot of methods, classified according to different criteria, are currently developed. However, choosing the most relevant method in a particular context is not an easy task since it requires to know all the methods and their intrinsic properties. The objective of the OntoMas1 tutoring system (Ontology Matching Assistant) is (1) to propose an architecture and to develop an effective knowledge-based system dedicated to a fine-grained description and a classification of the current matching methods and (2) to provide functionalities dedicated to the definition of advices and explanations (for the end-user), in order to facilitate both the choice of the most suitable method for a particular matching problem and the learning of this new domain: ontology matching.

Incorporating IDEF3 into the Unified Enterprise Modelling Language

The UEML approach is comprising an ontological approach to representation of modeling languages; innovative ideas are i) ontology is not fixed but evolve, ii) ontological analysis outcome is a standardized meta-model of a language, mapping abstract syntax artifacts on ontological artifacts, Hi) automated mechanisms for understanding similarities between language constructs can be applied. The paper shows how the promoted ontological analysis can be applied to IDET3 language.

Methods and Tools for Automatic Construction of Ontologies from Textual Resources: A Framework for Comparison and Its Application

Over the recent years, several approaches and tools for the automatic construction of ontologies from textual resources have been proposed. This paper provides a comparative analysis of four well known approaches and related tools among existing ones. The selected approaches and related tools indeed cover all the steps of the ontology construction process. In the first part of the paper, we introduce Methontology and related task i.e. a well-known reference methodology designed for the manual construction of ontology; then, according to Methontology, we analyze and classify detailed subtasks required by those approaches. Based on this uniform classification, we provide a very detailed comparison of those approaches: we explain the main techniques and introduce tools used in the various subtasks of each approach and we highlight the main similarities and differences between the techniques used in comparable subtasks belonging to distinct approaches. In the second part of the paper, we introduce various measures for evaluating tools effectiveness wrt a manually constructed ontology. Then, we evaluate and compare the key tools supporting those approaches by using the provided measures and a specific set of textual resources.

New perspectives in ontological analysis: Guidelines and rules for incorporating modelling languages into UEML

Ontological analysis of modelling languages has been mainly used for evaluating quality of modelling language w.r.t. one specific upper ontology. Generally speaking this evaluation has been done by identifying the coverage of the modelling language constructs w.r.t. the ontology and vice-versa. However, a quite limited support has been developed for performing the ontological analysis task. Specifically, various ontologies used for ontological analysis are not associated to a machine readable format; the coverage of modelling language constructs is mostly provided by informal tables mapping one construct on to one ontological concept; the way in which this coverage task is undertaken is poorly specified (resulting in distinct results for distinct experts involved), and finally, preventing any ontology enrichment for dealing with some specialised language constructs. This limited support also prevents application of ontological analysis outcomes to problems and domains dealing with interoperability, integration and integrated usage of enterprise and IS models, which is today one of the key aspects for making interoperable, maintainable and evolvable inter and intra enterprise software systems. The paper provides an overview of the Unified Enterprise Modelling Language (UEML) approach, which introduces advanced support to ontological analysis of modelling languages. The paper is specifically focused on the task of ontological analysis of modelling languages (named incorporation of modelling languages) by introducing and explaining several guidelines and rules for driving the task: therefore, not all the aspects of the UEML approach will be discussed through the paper. The guidelines and rules are illustrated by incorporation of three selected modelling constructs from IDEF3, a well known language for specifying enterprise processes.

A formal model for assessing individual competence in enterprises

Competence management plays a key role in tuning organization performances, be they industrial or administrative organizations, because it recognizes the human role at the heart of systems. After considerations of competence exploitation domains, the paper provides a formal model of human competencies developed from an industrial engineering perspective for industrial enterprises but applicable to a wide range of organizations. Fundamental concepts of competence are formalized and structured in an operational model, called CRAI (competency-resource-aspect-individual). The model, compliant with modern enterprise modeling paradigms, has been designed in such a way that it can answer usual queries needed for competence management.