Visualising Inconsistency and Incompleteness in RDF Gene Expression Data using FCA
The integration of data from different data sources can result to the existence of inconsistent or incomplete data (IID). IID can undermine the validity of information retrieved from an integrated dataset. There is therefore a need to identify these anomalies. This work presents SPARQL queries that...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Nwagwu, Honour Chika [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Umfang: |
1 Online-Ressource |
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| Übergeordnetes Werk: |
Enthalten in: International journal of conceptual structures and smart applications - Hershey, Pa. : IGI Global, 2013, 2(2014), 1, Seite 68-82 |
|---|---|
| Übergeordnetes Werk: |
volume:2 ; year:2014 ; number:1 ; pages:68-82 |
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| DOI / URN: |
10.4018/ijcssa.2014010105 |
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NLEJ251794369 |
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| 520 | |a The integration of data from different data sources can result to the existence of inconsistent or incomplete data (IID). IID can undermine the validity of information retrieved from an integrated dataset. There is therefore a need to identify these anomalies. This work presents SPARQL queries that retrieve from an EMAGE dataset, information which are inconsistent or incomplete. Also, it will be shown how Formal Concept Analysis (FCA) tools notably FcaBedrock and Concept Explorer can be applied to identify and visualise IID existing in these retrieved information. Although, instances of IID can exist in most data formats, the investigation is focused on RDF dataset | ||
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10.4018/ijcssa.2014010105 doi (DE-627)NLEJ251794369 (VZGNL)10.4018/ijcssa.2014010105 DE-627 ger DE-627 rakwb eng Nwagwu, Honour Chika verfasserin aut Visualising Inconsistency and Incompleteness in RDF Gene Expression Data using FCA 2014 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The integration of data from different data sources can result to the existence of inconsistent or incomplete data (IID). IID can undermine the validity of information retrieved from an integrated dataset. There is therefore a need to identify these anomalies. This work presents SPARQL queries that retrieve from an EMAGE dataset, information which are inconsistent or incomplete. Also, it will be shown how Formal Concept Analysis (FCA) tools notably FcaBedrock and Concept Explorer can be applied to identify and visualise IID existing in these retrieved information. Although, instances of IID can exist in most data formats, the investigation is focused on RDF dataset Formal Concept Analysis (FCA) Formal Context Incomplete Data Inconsistent Data Mutually Exclusive Recordset Enthalten in International journal of conceptual structures and smart applications Hershey, Pa. : IGI Global, 2013 2(2014), 1, Seite 68-82 Online-Ressource (DE-627)NLEJ251794180 (DE-600)2754445-X (DE-576)400525976 2166-7306 nnns volume:2 year:2014 number:1 pages:68-82 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijcssa.2014010105 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijcssa.2014010105&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 2 2014 1 68-82 |
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10.4018/ijcssa.2014010105 doi (DE-627)NLEJ251794369 (VZGNL)10.4018/ijcssa.2014010105 DE-627 ger DE-627 rakwb eng Nwagwu, Honour Chika verfasserin aut Visualising Inconsistency and Incompleteness in RDF Gene Expression Data using FCA 2014 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The integration of data from different data sources can result to the existence of inconsistent or incomplete data (IID). IID can undermine the validity of information retrieved from an integrated dataset. There is therefore a need to identify these anomalies. This work presents SPARQL queries that retrieve from an EMAGE dataset, information which are inconsistent or incomplete. Also, it will be shown how Formal Concept Analysis (FCA) tools notably FcaBedrock and Concept Explorer can be applied to identify and visualise IID existing in these retrieved information. Although, instances of IID can exist in most data formats, the investigation is focused on RDF dataset Formal Concept Analysis (FCA) Formal Context Incomplete Data Inconsistent Data Mutually Exclusive Recordset Enthalten in International journal of conceptual structures and smart applications Hershey, Pa. : IGI Global, 2013 2(2014), 1, Seite 68-82 Online-Ressource (DE-627)NLEJ251794180 (DE-600)2754445-X (DE-576)400525976 2166-7306 nnns volume:2 year:2014 number:1 pages:68-82 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijcssa.2014010105 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijcssa.2014010105&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 2 2014 1 68-82 |
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Visualising Inconsistency and Incompleteness in RDF Gene Expression Data using FCA |
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The integration of data from different data sources can result to the existence of inconsistent or incomplete data (IID). IID can undermine the validity of information retrieved from an integrated dataset. There is therefore a need to identify these anomalies. This work presents SPARQL queries that retrieve from an EMAGE dataset, information which are inconsistent or incomplete. Also, it will be shown how Formal Concept Analysis (FCA) tools notably FcaBedrock and Concept Explorer can be applied to identify and visualise IID existing in these retrieved information. Although, instances of IID can exist in most data formats, the investigation is focused on RDF dataset |
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The integration of data from different data sources can result to the existence of inconsistent or incomplete data (IID). IID can undermine the validity of information retrieved from an integrated dataset. There is therefore a need to identify these anomalies. This work presents SPARQL queries that retrieve from an EMAGE dataset, information which are inconsistent or incomplete. Also, it will be shown how Formal Concept Analysis (FCA) tools notably FcaBedrock and Concept Explorer can be applied to identify and visualise IID existing in these retrieved information. Although, instances of IID can exist in most data formats, the investigation is focused on RDF dataset |
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The integration of data from different data sources can result to the existence of inconsistent or incomplete data (IID). IID can undermine the validity of information retrieved from an integrated dataset. There is therefore a need to identify these anomalies. This work presents SPARQL queries that retrieve from an EMAGE dataset, information which are inconsistent or incomplete. Also, it will be shown how Formal Concept Analysis (FCA) tools notably FcaBedrock and Concept Explorer can be applied to identify and visualise IID existing in these retrieved information. Although, instances of IID can exist in most data formats, the investigation is focused on RDF dataset |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ251794369</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231205143844.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231128s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4018/ijcssa.2014010105</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ251794369</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(VZGNL)10.4018/ijcssa.2014010105</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Nwagwu, Honour Chika</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Visualising Inconsistency and Incompleteness in RDF Gene Expression Data using FCA</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The integration of data from different data sources can result to the existence of inconsistent or incomplete data (IID). IID can undermine the validity of information retrieved from an integrated dataset. There is therefore a need to identify these anomalies. This work presents SPARQL queries that retrieve from an EMAGE dataset, information which are inconsistent or incomplete. Also, it will be shown how Formal Concept Analysis (FCA) tools notably FcaBedrock and Concept Explorer can be applied to identify and visualise IID existing in these retrieved information. Although, instances of IID can exist in most data formats, the investigation is focused on RDF dataset</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Formal Concept Analysis (FCA)</subfield><subfield code="a">Formal Context</subfield><subfield code="a">Incomplete Data</subfield><subfield code="a">Inconsistent Data</subfield><subfield code="a">Mutually Exclusive</subfield><subfield code="a">Recordset</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of conceptual structures and smart applications</subfield><subfield code="d">Hershey, Pa. : IGI Global, 2013</subfield><subfield code="g">2(2014), 1, Seite 68-82</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ251794180</subfield><subfield code="w">(DE-600)2754445-X</subfield><subfield code="w">(DE-576)400525976</subfield><subfield code="x">2166-7306</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:2</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:68-82</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijcssa.2014010105</subfield><subfield code="m">X:IGIG</subfield><subfield code="x">Verlag</subfield><subfield code="z">Deutschlandweit zugänglich</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijcssa.2014010105&buylink=true</subfield><subfield code="3">Abstract</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-GIS</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">2</subfield><subfield code="j">2014</subfield><subfield code="e">1</subfield><subfield code="h">68-82</subfield></datafield></record></collection>
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