On-demand relational concept analysis
Formal Concept Analysis (FCA) and its associated conceptual structures are used to support exploratory search through conceptual navigation. Relational Concept Analysis (RCA) is an extension of Formal Concept Analysis to process relational datasets. RCA and its multiple interconnected structures represent good candidates to support exploratory search in relational datasets, as they are enabling navigation within a structure as well as between the connected structures. However, building the entire structures does not present an efficient solution to explore a small localised area of the dataset, to retrieve the closest alternatives to a given query. In these cases, generating only a concept and its neighbour concepts at each navigation step appears as a less costly alternative. In this paper, we propose an algorithm to compute a concept, and its neighbourhood, in connected concept lattices. The concepts are generated directly from the relational context family, and possess both formal and relational attributes. The algorithm takes into account two RCA scaling operators and it is implemented in the RCAExplore tool.
Main Authors: | , , , , , |
---|---|
Format: | conference_item biblioteca |
Language: | eng |
Published: |
Springer
|
Subjects: | U10 - Informatique, mathématiques et statistiques, C30 - Documentation et information, méthode statistique, informatique, analyse de données, recherche de l'information, logiciel, http://aims.fao.org/aos/agrovoc/c_7377, http://aims.fao.org/aos/agrovoc/c_27769, http://aims.fao.org/aos/agrovoc/c_15962, http://aims.fao.org/aos/agrovoc/c_3863, http://aims.fao.org/aos/agrovoc/c_24008, |
Online Access: | http://agritrop.cirad.fr/593471/ http://agritrop.cirad.fr/593471/7/ID593471.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
dig-cirad-fr-593471 |
---|---|
record_format |
koha |
spelling |
dig-cirad-fr-5934712024-01-29T02:16:42Z http://agritrop.cirad.fr/593471/ http://agritrop.cirad.fr/593471/ On-demand relational concept analysis. Bazin Alexandre, Carbonnel Jessie, Huchard Marianne, Kahn Giacomo, Keip Priscilla, Ouzerdine Amirouche. 2019. In : Formal concept analysis: 15th International Conference, ICFCA 2019 Frankfurt, Germany, June 25–28, 2019 Proceedings. Cristea Diana (ed.), Le Ber Florence (ed.), Sertkaya Baris (ed.). Cham : Springer, 155-172. (Lecture Notes in Artificial Intelligence, 11511) ISBN 978-3-030-21461-6 International Conference on Formal Concept Analysis (ICFCA 2019). 15, Francfort, Allemagne, 25 Juin 2019/28 Juin 2019.https://doi.org/10.1007/978-3-030-21462-3_11 <https://doi.org/10.1007/978-3-030-21462-3_11> On-demand relational concept analysis Bazin, Alexandre Carbonnel, Jessie Huchard, Marianne Kahn, Giacomo Keip, Priscilla Ouzerdine, Amirouche eng 2019 Springer Formal concept analysis: 15th International Conference, ICFCA 2019 Frankfurt, Germany, June 25–28, 2019 Proceedings U10 - Informatique, mathématiques et statistiques C30 - Documentation et information méthode statistique informatique analyse de données recherche de l'information logiciel http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_27769 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_3863 http://aims.fao.org/aos/agrovoc/c_24008 Formal Concept Analysis (FCA) and its associated conceptual structures are used to support exploratory search through conceptual navigation. Relational Concept Analysis (RCA) is an extension of Formal Concept Analysis to process relational datasets. RCA and its multiple interconnected structures represent good candidates to support exploratory search in relational datasets, as they are enabling navigation within a structure as well as between the connected structures. However, building the entire structures does not present an efficient solution to explore a small localised area of the dataset, to retrieve the closest alternatives to a given query. In these cases, generating only a concept and its neighbour concepts at each navigation step appears as a less costly alternative. In this paper, we propose an algorithm to compute a concept, and its neighbourhood, in connected concept lattices. The concepts are generated directly from the relational context family, and possess both formal and relational attributes. The algorithm takes into account two RCA scaling operators and it is implemented in the RCAExplore tool. conference_item info:eu-repo/semantics/conferenceObject Conference info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/593471/7/ID593471.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1007/978-3-030-21462-3_11 10.1007/978-3-030-21462-3_11 http://catalogue-bibliotheques.cirad.fr/cgi-bin/koha/opac-detail.pl?biblionumber=220471 info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-21462-3_11 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1007/978-3-030-21462-3_11 |
institution |
CIRAD FR |
collection |
DSpace |
country |
Francia |
countrycode |
FR |
component |
Bibliográfico |
access |
En linea |
databasecode |
dig-cirad-fr |
tag |
biblioteca |
region |
Europa del Oeste |
libraryname |
Biblioteca del CIRAD Francia |
language |
eng |
topic |
U10 - Informatique, mathématiques et statistiques C30 - Documentation et information méthode statistique informatique analyse de données recherche de l'information logiciel http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_27769 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_3863 http://aims.fao.org/aos/agrovoc/c_24008 U10 - Informatique, mathématiques et statistiques C30 - Documentation et information méthode statistique informatique analyse de données recherche de l'information logiciel http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_27769 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_3863 http://aims.fao.org/aos/agrovoc/c_24008 |
spellingShingle |
U10 - Informatique, mathématiques et statistiques C30 - Documentation et information méthode statistique informatique analyse de données recherche de l'information logiciel http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_27769 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_3863 http://aims.fao.org/aos/agrovoc/c_24008 U10 - Informatique, mathématiques et statistiques C30 - Documentation et information méthode statistique informatique analyse de données recherche de l'information logiciel http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_27769 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_3863 http://aims.fao.org/aos/agrovoc/c_24008 Bazin, Alexandre Carbonnel, Jessie Huchard, Marianne Kahn, Giacomo Keip, Priscilla Ouzerdine, Amirouche On-demand relational concept analysis |
description |
Formal Concept Analysis (FCA) and its associated conceptual structures are used to support exploratory search through conceptual navigation. Relational Concept Analysis (RCA) is an extension of Formal Concept Analysis to process relational datasets. RCA and its multiple interconnected structures represent good candidates to support exploratory search in relational datasets, as they are enabling navigation within a structure as well as between the connected structures. However, building the entire structures does not present an efficient solution to explore a small localised area of the dataset, to retrieve the closest alternatives to a given query. In these cases, generating only a concept and its neighbour concepts at each navigation step appears as a less costly alternative. In this paper, we propose an algorithm to compute a concept, and its neighbourhood, in connected concept lattices. The concepts are generated directly from the relational context family, and possess both formal and relational attributes. The algorithm takes into account two RCA scaling operators and it is implemented in the RCAExplore tool. |
format |
conference_item |
topic_facet |
U10 - Informatique, mathématiques et statistiques C30 - Documentation et information méthode statistique informatique analyse de données recherche de l'information logiciel http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_27769 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_3863 http://aims.fao.org/aos/agrovoc/c_24008 |
author |
Bazin, Alexandre Carbonnel, Jessie Huchard, Marianne Kahn, Giacomo Keip, Priscilla Ouzerdine, Amirouche |
author_facet |
Bazin, Alexandre Carbonnel, Jessie Huchard, Marianne Kahn, Giacomo Keip, Priscilla Ouzerdine, Amirouche |
author_sort |
Bazin, Alexandre |
title |
On-demand relational concept analysis |
title_short |
On-demand relational concept analysis |
title_full |
On-demand relational concept analysis |
title_fullStr |
On-demand relational concept analysis |
title_full_unstemmed |
On-demand relational concept analysis |
title_sort |
on-demand relational concept analysis |
publisher |
Springer |
url |
http://agritrop.cirad.fr/593471/ http://agritrop.cirad.fr/593471/7/ID593471.pdf |
work_keys_str_mv |
AT bazinalexandre ondemandrelationalconceptanalysis AT carbonneljessie ondemandrelationalconceptanalysis AT huchardmarianne ondemandrelationalconceptanalysis AT kahngiacomo ondemandrelationalconceptanalysis AT keippriscilla ondemandrelationalconceptanalysis AT ouzerdineamirouche ondemandrelationalconceptanalysis |
_version_ |
1792499816066973696 |