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Your search keyword '"Chahira Touati"' showing total 3 results
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3 results on '"Chahira Touati"'

1. A CP-based approach for mining sequential patterns with quantities

Author

Amina Kemmar, Chahira Touati, and Yahia Lebbah

Subjects
Sequential pattern mining, quantitative sequences, constraint programming, constraints., Electronic computers. Computer science, QA75.5-76.95
Abstract

This paper addresses the problem of mining sequential patterns (SPM) from data represented as a set of sequences. In this work, we are interested in sequences of items in which each item is associated with its quantity. To the best of our knowledge, existing approaches don’t allow to handle this kind of sequences under constraints. In the other hand, several proposals show the efficiency of constraint programming (CP) to solve SPM problem dealing with several kind of constraints. However, in this paper, we propose the global constraint QSPM which is an extension of the two CP-based approaches proposed in [5] and [7]. Experiments on real-life datasets show the efficiency of our approach allowing to specify many constraints like size, membership and regular expression constraints.

Published
2023
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3. An efficient model for extracting an optimal alignment with multiple cardinalities in ontology alignment

Author

Yahia Lebbah, Chahira Touati, and Moussa Benaissa

Subjects
Theoretical computer science, Similarity (geometry), Computer science, Process ontology, Ontology-based data integration, 02 engineering and technology, Library and Information Sciences, computer.software_genre, Synthetic data, Computer Science Applications, Cardinality, Hungarian algorithm, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Domain knowledge, 020201 artificial intelligence & image processing, Data mining, computer, Ontology alignment, Information Systems
Abstract

Ontologies have been created to solve the data heterogeneity problem on the web and to share domain knowledge between systems. However, these ontologies have become themselves a source of heterogeneity. The ontology alignment is an effective solution to this problem, which is to discover the semantic correspondences between the ontologies entities, so it becomes a crucial task. This paper proposes an efficient flow-based algorithm which maximises the global similarity objective function subject to multiple cardinality constraints. Our approach has been evaluated on a variety of synthetic and real data, and compared with currently used algorithms e.g. Hungarian and Karp algorithms. The obtained results show the efficiency of our approach, especially in the case of rectangular matrices, which is the most frequent case in practice real ontologies.

Published
2016

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