A5103913523- Semantic Web and Ontologies
- Service-Oriented Architecture and Web Services
- Business Process Modeling and Analysis
- Natural Language Processing Techniques
- Model-Driven Software Engineering Techniques
- Topic Modeling
- Collaboration in agile enterprises
- Advanced Text Analysis Techniques
- Information Technology and Learning
- Biomedical Text Mining and Ontologies
- Traffic Prediction and Management Techniques
- Business Strategy and Innovation
- Competitive and Knowledge Intelligence
- Information Technology Governance and Strategy
- French Language Learning Methods
- Education, sociology, and vocational training
- Transportation Planning and Optimization
- Advanced Graph Neural Networks
- Management, Economics, and Public Policy
- Human Mobility and Location-Based Analysis
- Text and Document Classification Technologies
- Health, Medicine and Society
- Wine Industry and Tourism
- Employer Branding and e-HRM
- Manufacturing Process and Optimization
Laboratoire des Sciences du Numérique de Nantes
2018-2024
Nantes Université
2011-2024
École Polytechnique
2020
Laboratoire d'informatique de Nantes Atlantique
2006-2018
Centre National de la Recherche Scientifique
2012-2013
École Nationale d'Ingénieurs de Metz
2000
The Unified Enterprise Modelling Language (UEML) aims to support precise semantic definition of a wide variety enterprise- and IS-modelling languages. In the longer run, it is also intended as hub for integrated use enterprise information
This paper presents the project CommOnCV, which aims at dealing with problematics of e---recruitment by considering a new approach based on competency management. The idea consists allowing job seeker (respectively recruiter to identify and formally represent competencies underlying its Curriculum Vitae offer). These competencies, allow make explicit knowledge, skills, abilities, traits motives acquired person required for job), are then used refine matching process between "supply demand"....
Ontologies are powerful semantic models applied for various purposes such as improving system interoperability, information retrieval, question answering, etc. However, building domain ontologies remains a challenging task humans, especially when the concepts and properties large or evolving, also they built from large-scale textual data. Machine learning allows to automate of texts. In particular, clustering techniques have promising ability on concept formation by identifying cluster...
Nowadays, modular domain ontology, where each module represents a subdomain of the ontology domain, facilitates reuse information and provides users with domain-specific knowledge. In this paper, we focus on taxonomy learning from text, collects terms same topic insights, in parallel manage to discover hypernym 'related' relations among those collected terms.However, it is difficult automatically fit into modules relations.We propose employ twice trainedLDAto partition termsof subdomain,...
The UEML approach is comprising an ontological to representation of modeling languages; innovative ideas are i) ontology not fixed but evolve, ii) analysis outcome a standardized meta-model language, mapping abstract syntax artifacts on artifacts, Hi) automated mechanisms for understanding similarities between language constructs can be applied. paper shows how the promoted applied IDET3 language.
This paper aims to use term clustering build a modular ontology according core from domain-specific text. The acquisition of semantic knowledge focuses on noun phrase appearing with the same syntactic roles in relation verb or its preposition combination sentence. construction this co-occurrence matrix context helps feature space phrases, which is then transformed several encoding representations including selection and dimensionality reduction. In addition, content has also been presented...