Marie-Christine Rousset

Laboratoire d'Informatique de Grenoble

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Serge Abiteboul

Institut national de recherche en informatique et en automatique

Philippe Rigaux

Conservatoire National des Arts et Métiers

Pierre Senellart

École Normale Supérieure - PSL

Ioana Manolescu

Inria Saclay - Île de France

Alexandre Termier

Institut de Recherche en Informatique et Systèmes Aléatoires

Fabrice Jouanot

Laboratoire d'Informatique de Grenoble

Jérôme Euzenat

Institut national de recherche en informatique et en automatique

Alon Y. Levy

Dalhousie University

Joanna Jongwane

Institut national de recherche en informatique et en automatique

Nathalie Pernelle

Université Sorbonne Paris Nord

Cooperative Institutions

Association for Computing Machinery

151

Penn Center for AIDS Research

149

Centre National de la Recherche Scientifique

116

Laboratoire d'Informatique de Grenoble

Institut national de recherche en informatique et en automatique

Université Grenoble Alpes

University of Utah

Institut polytechnique de Grenoble

Université Paris-Sud

Laboratoire de Recherche en Informatique

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Towards a Framework for Semantic Exploration of Frequent Patterns

HAL (Le Centre pour la Communication Scientifique Directe) (2013)

Behrooz Omidvar Tehrani Sihem Amer-Yahia Alexandre Termier Aur ́ elie Bertaux Éric Gaussier

Mining frequent patterns is an essential task in discovering hidden correlations in datasets. Although frequent patterns unveil valuable information, there are some challenges which limits their usability. First, the number of possible patterns is often very large which hinders their eff ective exploration. Second, patterns with many items are hard to read and the analyst may be unable to understand their meaning. In addition, the only available information about patterns is their support, a very coarse piece of information. In this paper, we are particularly interested in mining datasets that reflect usage patterns of users moving in space and time and for whom demographics attributes are available (age, occupation, etc). Such characteristics are typical of data collected from smart phones, whose analysis has critical business applications nowadays. We propose pattern exploration primitives, abstraction and refinement, that use hand-crafted taxonomies on time, space and user demographics. We show on two real datasets, Nokia and MovieLens, how the use of such taxonomies reduces the size of the pattern space and how demographics enable their semantic exploration. This work opens new perspectives in the semantic exploration of frequent patterns that reflect the behavior of di fferent user communities.

MovieLens

Demographics

Abstraction

Semantic space

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Citations (5)

Des métadonnées aux connaissances et ontologies

HAL (Le Centre pour la Communication Scientifique Directe) (2017)

Marie-Christine Rousset

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Citations (0)

Backward Reasoning in Aboxes for Query Answering.

KRDB (1999)

Marie-Christine Rousset

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Citations (9)

À propos des algorithmes

HAL (Le Centre pour la Communication Scientifique Directe) (2013)

Marie-Christine Rousset Joanna Jongwane

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Idée reçue : Un ordinateur, c’est une machine intelligente

HAL (Le Centre pour la Communication Scientifique Directe) (2008)

Marie-Christine Rousset

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Data Extraction, Transformation and Integration Guided by an Ontology

IGI Global eBooks (2010)

Chantal Reynaud Nathalie Pernelle Marie-Christine Rousset

This chapter deals with integration of XML heterogeneous information sources into a data warehouse with data defined in terms of a global abstract schema or ontology. The authors present an approach supporting the acquisition of data from a set of external sources available for an application of interest including data extraction, data transformation and data integration or reconciliation. The integration middleware that the authors propose extracts data from external XML sources which are relevant according to an RDFS+ ontology, transforms returned XML data into RDF facts conformed to the ontology and reconciles RDF data in order to resolve possible redundancies.

RDF Schema

Data extraction

XML Schema (W3C)

10.4018/978-1-60566-756-0.ch002

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Citations (1)

Trust in networks of ontologies and alignments

Knowledge and Information Systems (2013)

Manuel Atencia Mustafa Al-Bakri Marie-Christine Rousset

10.1007/s10115-013-0708-9

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Citations (6)

Revision of Rule Bases.

Fatma Bouali Stéphane Loiseau Marie-Christine Rousset

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Citations (10)

À propos des algorithmes

Marie-Christine Rousset Joanna Jongwane

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Citations (0)

Inferring Same-As Facts from Linked Data: An Iterative Import-by-Query Approach

Proceedings of the AAAI Conference on Artificial Intelligence (2015)

Mustafa Al-Bakri Manuel Atencia Steffen Lalande Marie-Christine Rousset

In this paper we model the problem of data linkage in Linked Data as a reasoning problem on possibly decentralized data. We describe a novel import-by-query algorithm that alternates steps of sub-query rewriting and of tailored querying the Linked Data cloud in order to import data as specific as possible for inferring or contradicting given target same-as facts. Experiments conducted on a real-world dataset have demonstrated the feasibility of this approach and its usefulness in practice for data linkage and disambiguation.

Linkage (software)

Real world data

10.1609/aaai.v29i1.9174

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Citations (11)