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Paper presentations: UK e-science AHM meeting, 2005

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Paolo Missier
Paolo Missier

The document describes an ontology-based approach to handling information quality in e-science. It presents an initial quality framework that captures scientists' quality requirements and allows defining domain-specific quality characteristics. It introduces a web service that annotates datasets with quality metrics based on how well their elements conform to relevant ontologies, using transcriptomics as an example domain. The approach aims to make quality definitions reusable and the computation of quality measurements over large datasets cost-effective.

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An Ontology-Based Approach to Handling Information Quality in e-Science Paolo Missier, Suzanne Embury, Mark Greenwood School of Computer Science, University of Manchester Alun Preece , Binling Jin Department of Computing Science, University of Aberdeen www.qurator.org Describing the Quality of Curated e-Science Information Resources
Scientists data ,[object Object],[object Object],[object Object],Serious problems arise due to variations in the quality of the data being shared Data sets that are incomplete, inconsistent, or inaccurate can still be useful to those that are aware of these deficiencies, but can be misleading, frustrating and time-consuming for those who are not!
Research in information quality (IQ) ,[object Object],Accuracy Currency Consistency Completeness Conformity Reputation Timeliness Density Conciseness ,[object Object],[object Object],[object Object],[object Object]
Qurator manifesto I ,[object Object],[object Object]
Qurator manifesto II ,[object Object],[object Object]
Approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Transcriptomics example I ,[object Object],* http://mged.sourceforge.net/ontologies/MGEDontology.php In searching for microarray experiment data to use for their own purposes, a particular biologist may specify a quality requirement on the extent to which particular elements of the dataset – called ontology entries – conform to the MGED Ontology
Transcriptomics example II < BioSample identifier =&quot; S:Sample:MEXP:167278 &quot; name =&quot; CH131_1 &quot;> < MaterialType_assn > < OntologyEntry category =&quot; MaterialType &quot; value =&quot; whole_organism &quot; /> </ MaterialType_assn > < Treatments_assnlist > < Treatment order =&quot; 1 &quot; identifier =&quot; T:Sample:MEXP:167278 &quot;> < Action_assn > < OntologyEntry category =&quot; Action &quot; value =&quot; specified_biomaterial_action &quot; /> </ Action_assn > …
Core IQ concepts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Test Process Quality Preference Schema pref-based-on-metric Quality Indicator is-output-of metric-based-on-indicator Quality Metric
Qurator conceptual framework Baseline layer Quality preferences layer Presentation layer Binding layer Data Testing Process Model Quality indicators (any data / metadata) Quality-aware data visualization models Selection of data sources based on their quality Process layer Tasks binding Executable Task Executable Task Schema DB DB Data layer Data binding Schema ,[object Object],[object Object],[object Object],Quality metrics (functions of indicators) Data Model ,[object Object],[object Object],[object Object],Generic Reference IQ Ontology KB of user quality concepts
Ontology / KB fragment QualityPreferenceSchema Quality Metric TestProcess Is-output-of metric-based-on-indicator pref-based-on-metric Quality Indicator QtyProperty Accuracy Property-from-metric Currency … MGED-term-consistency MGED-conformance-schema OE Consistency OntValidatorService MGED-global-consistency pref-based-on-metric instanceOf instanceOf Is-output-of instanceOf instanceOf metric-based-on-indicator
Baseline layer Quality preferences layer Presentation layer Binding layer Data Testing Process Model Quality indicators (any data / metadata) Quality-aware data visualization models Selection of data sources based on their quality Process layer Tasks binding Executable Task Executable Task Schema DB DB Data layer Data binding Schema ,[object Object],[object Object],[object Object],Quality metrics (functions of indicators) Data Model Example : Conformance of OntologyEntry to MGED Example : various types of MGED-Consistency of experiment descriptions Example : Spec for the OntValidator service Example : OntologyEntry part-of Experiment Example : class of “acceptable” experiments, ranking of experiment description based on MGED-consistency Example : Dynamic setting of thresholds and other parameters, on-the-fly filtering ,[object Object],[object Object],[object Object],Generic Reference IQ Ontology KB of user quality concepts
IQ ontology FAQ ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Sample IQ service: OntValidator ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
OntValidator service Web client
OntValidator results page
Sample annotations (raw RDF!)
Sample annotatations (styled as HTML)
Getting Qurator closer to biologists: a Pedro plugin client
Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
www.qurator.org Describing the Quality of Curated e-Science Information Resources Suzanne Embury Paolo Missier Mark Greenwood Andy Brass Brian Warboys Alun Preece Binling Jin Edoardo Pignotti Al Brown David Stead Dawn Field Bela Tiwari Joe Wood The Qurator project is funded by the EPSRC Programme Fundamental Computer Science for e-Science : GR/S67593 & GR/S67609. Qurator logo by Irene Christensen.

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Paper presentations: UK e-science AHM meeting, 2005

  • 1. An Ontology-Based Approach to Handling Information Quality in e-Science Paolo Missier, Suzanne Embury, Mark Greenwood School of Computer Science, University of Manchester Alun Preece , Binling Jin Department of Computing Science, University of Aberdeen www.qurator.org Describing the Quality of Curated e-Science Information Resources
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  • 8. Transcriptomics example II < BioSample identifier =&quot; S:Sample:MEXP:167278 &quot; name =&quot; CH131_1 &quot;> < MaterialType_assn > < OntologyEntry category =&quot; MaterialType &quot; value =&quot; whole_organism &quot; /> </ MaterialType_assn > < Treatments_assnlist > < Treatment order =&quot; 1 &quot; identifier =&quot; T:Sample:MEXP:167278 &quot;> < Action_assn > < OntologyEntry category =&quot; Action &quot; value =&quot; specified_biomaterial_action &quot; /> </ Action_assn > …
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  • 11. Ontology / KB fragment QualityPreferenceSchema Quality Metric TestProcess Is-output-of metric-based-on-indicator pref-based-on-metric Quality Indicator QtyProperty Accuracy Property-from-metric Currency … MGED-term-consistency MGED-conformance-schema OE Consistency OntValidatorService MGED-global-consistency pref-based-on-metric instanceOf instanceOf Is-output-of instanceOf instanceOf metric-based-on-indicator
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  • 19. Getting Qurator closer to biologists: a Pedro plugin client
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  • 21. www.qurator.org Describing the Quality of Curated e-Science Information Resources Suzanne Embury Paolo Missier Mark Greenwood Andy Brass Brian Warboys Alun Preece Binling Jin Edoardo Pignotti Al Brown David Stead Dawn Field Bela Tiwari Joe Wood The Qurator project is funded by the EPSRC Programme Fundamental Computer Science for e-Science : GR/S67593 & GR/S67609. Qurator logo by Irene Christensen.