Querying a bioinformatic data sources registry with concept lattices
Identifieur interne :
000537 ( PascalFrancis/Corpus );
précédent :
000536;
suivant :
000538
Querying a bioinformatic data sources registry with concept lattices
Auteurs : Nizar Messai ;
Marie-Dominique Devignes ;
Amedeo Napoli ;
Malika Smaïl-TabboneSource :
-
Lecture notes in computer science [ 0302-9743 ] ; 2005.
RBID : Pascal:05-0356881
Descripteurs français
- Pascal (Inist)
- Ingénierie connaissances,
Sémantique,
Représentation connaissance,
Interrogation base donnée,
Bioinformatique,
Treillis,
Internet,
Base donnée,
Méthode formelle,
Donnée binaire,
Métadonnée,
Ontologie,
Réseau web,
Documentation,
Analyse conceptuelle,
Classification hiérarchique,
Méthode raffinement.
English descriptors
- KwdEn :
- Binary data,
Bioinformatics,
Conceptual analysis,
Database,
Database query,
Documentation,
Formal method,
Hierarchical classification,
Internet,
Knowledge engineering,
Knowledge representation,
Lattice,
Metadata,
Ontology,
Refinement method,
Semantics,
World wide web.
Abstract
Bioinformatic data sources available on the web are multiple and heterogenous. The lack of documentation and the difficulty of interaction with these data banks require users competence in both informatics and biological fields for an optimal use of sources contents that remain rather under exploited. In this paper we present an approach based on formal concept analysis to classify and search relevant bioinformatic data sources for a given user query. It consists in building the concept lattice from the binary relation between bioinformatic data sources and their associated metadata. The concept built from a given user query is then merged into the concept lattice. The result is given by the extraction of the set of sources belonging to the extents of the query concept subsumers in the resulting concept lattice. The sources ranking is given by the concept specificity order in the concept lattice. An improvement of the approach consists in automatic refinement of the query thanks to domain ontologies. Two forms of refinement are possible by generalisation and by specialisation.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
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| A05 | | | | @2 3596 |
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| A08 | 01 | 1 | ENG | @1 Querying a bioinformatic data sources registry with concept lattices |
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| A09 | 01 | 1 | ENG | @1 Conceptual structures : common semantics for sharing knowledge : Kassel, 17-22 July 2005 |
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| A11 | 01 | 1 | | @1 MESSAI (Nizar) |
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| A11 | 02 | 1 | | @1 DEVIGNES (Marie-Dominique) |
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| A11 | 03 | 1 | | @1 NAPOLI (Amedeo) |
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| A11 | 04 | 1 | | @1 SMAÏL-TABBONE (Malika) |
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| A12 | 01 | 1 | | @1 DAU (Frithjof) @9 ed. |
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| A12 | 02 | 1 | | @1 MUGNIER (Marie-Laure) @9 ed. |
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| A14 | 01 | | | @1 UMR 7503 LORIA, BP 239 @2 54506 Vandœuvre-lès-Nancy @3 FRA @Z 1 aut. @Z 2 aut. @Z 3 aut. @Z 4 aut. |
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| C01 | 01 | | ENG | @0 Bioinformatic data sources available on the web are multiple and heterogenous. The lack of documentation and the difficulty of interaction with these data banks require users competence in both informatics and biological fields for an optimal use of sources contents that remain rather under exploited. In this paper we present an approach based on formal concept analysis to classify and search relevant bioinformatic data sources for a given user query. It consists in building the concept lattice from the binary relation between bioinformatic data sources and their associated metadata. The concept built from a given user query is then merged into the concept lattice. The result is given by the extraction of the set of sources belonging to the extents of the query concept subsumers in the resulting concept lattice. The sources ranking is given by the concept specificity order in the concept lattice. An improvement of the approach consists in automatic refinement of the query thanks to domain ontologies. Two forms of refinement are possible by generalisation and by specialisation. |
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| C03 | 17 | X | FRE | @0 Méthode raffinement @5 23 |
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| N21 | | | | @1 248 |
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| pR |
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Format Inist (serveur)
| NO : | PASCAL 05-0356881 INIST |
|---|
| ET : | Querying a bioinformatic data sources registry with concept lattices |
|---|
| AU : | MESSAI (Nizar); DEVIGNES (Marie-Dominique); NAPOLI (Amedeo); SMAÏL-TABBONE (Malika); DAU (Frithjof); MUGNIER (Marie-Laure); STUMME (Gerd) |
|---|
| AF : | UMR 7503 LORIA, BP 239/54506 Vandœuvre-lès-Nancy/France (1 aut., 2 aut., 3 aut., 4 aut.) |
|---|
| DT : | Publication en série; Congrès; Niveau analytique |
|---|
| SO : | Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2005; Vol. 3596; Pp. 323-336; Bibl. 26 ref. |
|---|
| LA : | Anglais |
|---|
| EA : | Bioinformatic data sources available on the web are multiple and heterogenous. The lack of documentation and the difficulty of interaction with these data banks require users competence in both informatics and biological fields for an optimal use of sources contents that remain rather under exploited. In this paper we present an approach based on formal concept analysis to classify and search relevant bioinformatic data sources for a given user query. It consists in building the concept lattice from the binary relation between bioinformatic data sources and their associated metadata. The concept built from a given user query is then merged into the concept lattice. The result is given by the extraction of the set of sources belonging to the extents of the query concept subsumers in the resulting concept lattice. The sources ranking is given by the concept specificity order in the concept lattice. An improvement of the approach consists in automatic refinement of the query thanks to domain ontologies. Two forms of refinement are possible by generalisation and by specialisation. |
|---|
| CC : | 001D02C02 |
|---|
| FD : | Ingénierie connaissances; Sémantique; Représentation connaissance; Interrogation base donnée; Bioinformatique; Treillis; Internet; Base donnée; Méthode formelle; Donnée binaire; Métadonnée; Ontologie; Réseau web; Documentation; Analyse conceptuelle; Classification hiérarchique; Méthode raffinement |
|---|
| ED : | Knowledge engineering; Semantics; Knowledge representation; Database query; Bioinformatics; Lattice; Internet; Database; Formal method; Binary data; Metadata; Ontology; World wide web; Documentation; Conceptual analysis; Hierarchical classification; Refinement method |
|---|
| SD : | Semántica; Representación conocimientos; Interrogación base datos; Bioinformática; Enrejado; Internet; Base dato; Método formal; Dato binario; Metadatos; Ontología; Red WWW; Documentación; Análisis conceptual; Clasificación jerarquizada; Método afinamiento |
|---|
| LO : | INIST-16343.354000124499490210 |
|---|
| ID : | 05-0356881 |
|---|
Links to Exploration step
Pascal:05-0356881
Le document en format XML
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<term>Bioinformatics</term>
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<term>Database</term>
<term>Database query</term>
<term>Documentation</term>
<term>Formal method</term>
<term>Hierarchical classification</term>
<term>Internet</term>
<term>Knowledge engineering</term>
<term>Knowledge representation</term>
<term>Lattice</term>
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<term>Ontology</term>
<term>Refinement method</term>
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</keywords><front><div type="abstract" xml:lang="en">Bioinformatic data sources available on the web are multiple and heterogenous. The lack of documentation and the difficulty of interaction with these data banks require users competence in both informatics and biological fields for an optimal use of sources contents that remain rather under exploited. In this paper we present an approach based on formal concept analysis to classify and search relevant bioinformatic data sources for a given user query. It consists in building the concept lattice from the binary relation between bioinformatic data sources and their associated metadata. The concept built from a given user query is then merged into the concept lattice. The result is given by the extraction of the set of sources belonging to the extents of the query concept subsumers in the resulting concept lattice. The sources ranking is given by the concept specificity order in the concept lattice. An improvement of the approach consists in automatic refinement of the query thanks to domain ontologies. Two forms of refinement are possible by generalisation and by specialisation.</div>
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</fC03><fC03 i1="07" i2="X" l="ENG"><s0>Internet</s0>
<s5>09</s5>
</fC03><fC03 i1="07" i2="X" l="SPA"><s0>Internet</s0>
<s5>09</s5>
</fC03><fC03 i1="08" i2="X" l="FRE"><s0>Base donnée</s0>
<s5>10</s5>
</fC03><fC03 i1="08" i2="X" l="ENG"><s0>Database</s0>
<s5>10</s5>
</fC03><fC03 i1="08" i2="X" l="SPA"><s0>Base dato</s0>
<s5>10</s5>
</fC03><fC03 i1="09" i2="X" l="FRE"><s0>Méthode formelle</s0>
<s5>11</s5>
</fC03><fC03 i1="09" i2="X" l="ENG"><s0>Formal method</s0>
<s5>11</s5>
</fC03><fC03 i1="09" i2="X" l="SPA"><s0>Método formal</s0>
<s5>11</s5>
</fC03><fC03 i1="10" i2="X" l="FRE"><s0>Donnée binaire</s0>
<s5>12</s5>
</fC03><fC03 i1="10" i2="X" l="ENG"><s0>Binary data</s0>
<s5>12</s5>
</fC03><fC03 i1="10" i2="X" l="SPA"><s0>Dato binario</s0>
<s5>12</s5>
</fC03><fC03 i1="11" i2="X" l="FRE"><s0>Métadonnée</s0>
<s5>13</s5>
</fC03><fC03 i1="11" i2="X" l="ENG"><s0>Metadata</s0>
<s5>13</s5>
</fC03><fC03 i1="11" i2="X" l="SPA"><s0>Metadatos</s0>
<s5>13</s5>
</fC03><fC03 i1="12" i2="X" l="FRE"><s0>Ontologie</s0>
<s5>14</s5>
</fC03><fC03 i1="12" i2="X" l="ENG"><s0>Ontology</s0>
<s5>14</s5>
</fC03><fC03 i1="12" i2="X" l="SPA"><s0>Ontología</s0>
<s5>14</s5>
</fC03><fC03 i1="13" i2="X" l="FRE"><s0>Réseau web</s0>
<s5>18</s5>
</fC03><fC03 i1="13" i2="X" l="ENG"><s0>World wide web</s0>
<s5>18</s5>
</fC03><fC03 i1="13" i2="X" l="SPA"><s0>Red WWW</s0>
<s5>18</s5>
</fC03><fC03 i1="14" i2="X" l="FRE"><s0>Documentation</s0>
<s5>19</s5>
</fC03><fC03 i1="14" i2="X" l="ENG"><s0>Documentation</s0>
<s5>19</s5>
</fC03><fC03 i1="14" i2="X" l="SPA"><s0>Documentación</s0>
<s5>19</s5>
</fC03><fC03 i1="15" i2="X" l="FRE"><s0>Analyse conceptuelle</s0>
<s5>20</s5>
</fC03><fC03 i1="15" i2="X" l="ENG"><s0>Conceptual analysis</s0>
<s5>20</s5>
</fC03><fC03 i1="15" i2="X" l="SPA"><s0>Análisis conceptual</s0>
<s5>20</s5>
</fC03><fC03 i1="16" i2="X" l="FRE"><s0>Classification hiérarchique</s0>
<s5>21</s5>
</fC03><fC03 i1="16" i2="X" l="ENG"><s0>Hierarchical classification</s0>
<s5>21</s5>
</fC03><fC03 i1="16" i2="X" l="SPA"><s0>Clasificación jerarquizada</s0>
<s5>21</s5>
</fC03><fC03 i1="17" i2="X" l="FRE"><s0>Méthode raffinement</s0>
<s5>23</s5>
</fC03><fC03 i1="17" i2="X" l="ENG"><s0>Refinement method</s0>
<s5>23</s5>
</fC03><fC03 i1="17" i2="X" l="SPA"><s0>Método afinamiento</s0>
<s5>23</s5>
</fC03><fN21><s1>248</s1>
</fN21><fN44 i1="01"><s1>OTO</s1>
</fN44><fN82><s1>OTO</s1>
</fN82><pR><fA30 i1="01" i2="1" l="ENG"><s1>ICCS : international conference on conceptual structures</s1>
<s2>13</s2>
<s3>Kassel DEU</s3>
<s4>2005-07-17</s4>
</fA30><server><NO>PASCAL 05-0356881 INIST</NO>
<ET>Querying a bioinformatic data sources registry with concept lattices</ET>
<AU>MESSAI (Nizar); DEVIGNES (Marie-Dominique); NAPOLI (Amedeo); SMAÏL-TABBONE (Malika); DAU (Frithjof); MUGNIER (Marie-Laure); STUMME (Gerd)</AU>
<AF>UMR 7503 LORIA, BP 239/54506 Vandœuvre-lès-Nancy/France (1 aut., 2 aut., 3 aut., 4 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
<SO>Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2005; Vol. 3596; Pp. 323-336; Bibl. 26 ref.</SO>
<LA>Anglais</LA>
<EA>Bioinformatic data sources available on the web are multiple and heterogenous. The lack of documentation and the difficulty of interaction with these data banks require users competence in both informatics and biological fields for an optimal use of sources contents that remain rather under exploited. In this paper we present an approach based on formal concept analysis to classify and search relevant bioinformatic data sources for a given user query. It consists in building the concept lattice from the binary relation between bioinformatic data sources and their associated metadata. The concept built from a given user query is then merged into the concept lattice. The result is given by the extraction of the set of sources belonging to the extents of the query concept subsumers in the resulting concept lattice. The sources ranking is given by the concept specificity order in the concept lattice. An improvement of the approach consists in automatic refinement of the query thanks to domain ontologies. Two forms of refinement are possible by generalisation and by specialisation.</EA>
<CC>001D02C02</CC>
<FD>Ingénierie connaissances; Sémantique; Représentation connaissance; Interrogation base donnée; Bioinformatique; Treillis; Internet; Base donnée; Méthode formelle; Donnée binaire; Métadonnée; Ontologie; Réseau web; Documentation; Analyse conceptuelle; Classification hiérarchique; Méthode raffinement</FD>
<ED>Knowledge engineering; Semantics; Knowledge representation; Database query; Bioinformatics; Lattice; Internet; Database; Formal method; Binary data; Metadata; Ontology; World wide web; Documentation; Conceptual analysis; Hierarchical classification; Refinement method</ED>
<SD>Semántica; Representación conocimientos; Interrogación base datos; Bioinformática; Enrejado; Internet; Base dato; Método formal; Dato binario; Metadatos; Ontología; Red WWW; Documentación; Análisis conceptual; Clasificación jerarquizada; Método afinamiento</SD>
<LO>INIST-16343.354000124499490210</LO>
<ID>05-0356881</ID>
</server>
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