Uncertainty reasoning for Linked Data

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Uncertainty reasoning for Linked Data

• Dave Reynolds

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Uncertainty reasoning for linked data

• Linked data - a strikingly successful model for
  exploiting semantic web technology
• exhibits uncertainty related issues ambiguity,
  misalignment, reliability
• what approach could we take address this?
• without losing the simplicity which has enabled
  significant adoption

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Linked data

1. Use URIs as names for things
2. Use HTTP URIs so that people can look up those
   names.
3. When someone looks up a URI, provide useful
   information, using the standards (RDF, SPARQL)
4. Include links to other URIs. so that they can
   discover more things

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Uncertainty in linked data1. Misalignment of
instance matches

• link datasets by resolving co-references and
  publishing links
• links published as owlsameAs (all or nothing)
• match errors
• match uncertainties not accessible
• erroneous assumptions (e.g. clinical trial
  example)
• can partly address by use of skos mapping
  vocabulary

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Uncertainty in linked data2. Ambiguity from
merging datasets

• datasets have different assumptions, definitions,
  context (esp. time) for different measures
• leads to multiple different values
• E.g.
• lthttp//dbpedia.org/resource/Londongt
  dbopopulationMetro 12300000 dbppopulationMet
  ro 12,300,000 to 13,945,000 dbopopulationTota
  l 7556900owlsameAs lthttp//www.okkam.org/ens/i
  d...gt.
• lthttp//www.okkam.org/ens/id...gt population
  7421209.

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Uncertainty in linked data3. Other issues

• Misalignment of models
• e.g. freebase/dbpedia links generated (temporary)
  problems Musician owlequivalentClass Person
• Source reliability
• not unique to linked data but amplifies it

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Mitigation approaches?1. Weighted link vocabulary

• Develop a simple, common vocabulary for
  expressing uncertain co-reference links
• Clients or intermediates can choose how to match
  the link evidence to equivalence assertions

voidLinkSet
a urWeightedLink urtarget ltgt
urmatch ltgt urweight 0.7
a urUncertainLinkSet urmatchAlorithm
algJaroStringMatch .
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Mitigation approaches?2. Imprecise value
vocabulary

• Develop a simple, common vocabulary for
  expressing imprecise values that can arise from
  known measurement uncertainty or merge ambiguity

London population a urImpreciseValue sa
mpleValue value 7556900 source dbpedia
context year2009 sampleValue value
7421209 source okkam context
year2008 estimatedValue 7500000 .
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Mitigation approaches?3. Override graphs

• Allow clients to chose which parts of merged data
  sources they adopt (trust) and publish that
  decision
• Allow clients to publish deltas to public
  datasets correcting merge or other artefacts
  per-link and per-assertion granularity

voidDataSet
urargGraph
voidDataSet
urComputedDataSet
urCombinator
urDifference
Union
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Conclusion

• multiple issues in ambiguity and uncertainty in
  linked data
• proposed problems and solutions illustrative
  rather than definitive
• low hanging fruit
• area ripe for contribution