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Quality Reasoning in the Semantic Web
1. Quality Reasoning in the Semantic Web
Chris Baillie, Pete Edwards and Edoardo Pignotti
• Evaluating data quality – a challenge for agents (human
and machine)
• Data quality is a measure of “fitness for use”
• To evaluate quality an agent must examine the context
surrounding data (Linked Data)
• Part of this context should describe data provenance:
– The entities, activities, and agents associated with data
c.baillie@abdn.ac.uk
3. Research Questions
1. Is it possible to reason about quality in the
Web of Linked Sensor Data?
2. Is it possible to reason about quality using
the provenance of sensor observations?
3. Can the provenance of existing quality scores
facilitate new quality assessments?
c.baillie@abdn.ac.uk
4. Work To Date
• Developed a quality reasoning framework
enabling:
– Linked Data representation of sensor observations
using W3C Semantic Sensor Network ontology
– Document observation provenance with W3C PROV
ontology
– Definition of quality requirements using SPARQL rules
– Generation of quality scores via SPIN reasoner
• Deployed as part of a RTPIS (GetThere)
– See my poster for more info or dotrural.ac.uk/irp
c.baillie@abdn.ac.uk
5. Target Results
• Demonstrate that it is feasible to evaluate the
quality of linked data (DONE)
• Demonstrate that the presence of provenance
enhances quality assessment (QA)
– Empirical studies involving benchmark cases with and
without provenance
• Demonstrate that we can re-use existing quality
assessment results based on their provenance.
– Empirical studies examining quality score re-use;
measure impact on QA computational overhead
c.baillie@abdn.ac.uk
6. Reasoning about Quality
CON ST RU CT {
_:b0 a QualityS core .
_:b0 score ? .
qs
_:b0 dqm:ruleViolation _:b1 .
_:b1 a dqm:RequirementViolation .
_:b1 dqm:affectedInstance ? instance
} W H ERE {
?instance a prov:Entity .
?instance prov:wasDerivedFrom ? . obs
spin:rule N OT EX IST S {
? prov:wasDerivedFrom ?
obs entity . }
? _:distanceFromRoute ?
obs distance .
Relevance
LET (? := (1 - (?
qs distance / 100))) .
Requirement }
"Requirement1"
dqm:basedOn
dqm:Requirement
dqm:QualityScore dqm:ruleViolation Violation
"QualityScore1" "RelevanceViolation1"
dqm:plainScore dqm:affectedInstance
0 prov:Entity
"Obser vation2"
"Observation2"
Editor's Notes
Consider a crowd-sourcing applicationthat enables users to provide location information on public transport.We can enhance the context around user observations using, e.g. the W3C SSN ontology.This is enough to perform quality assessment, and we’d probably conclude that this observation is quite high quality.Describing observation provenance using, e.g. W3C PROV ontology enables us to examine how the observation was created.It was actually derived from some other observation, highlighted in green, that has been forced back onto the expected route by a map matching algorithm.Considering new context this as part of a quality assessment, it is likely that Observation 2 is lower quality than we initially concluded.
