1 / 10

Uncertainty reasoning for Linked Data

Uncertainty reasoning for Linked Data. Dave Reynolds. 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?

eleazar
Download Presentation

Uncertainty reasoning for Linked Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Uncertainty reasoning for Linked Data Dave Reynolds

  2. 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 Uncertainty reasoning for linked data

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

  4. Uncertainty in linked data1. Misalignment of instance matches • link datasets by resolving co-references and publishing links • links published as owl:sameAs (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

  5. 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. <http://dbpedia.org/resource/London> dbo:populationMetro 12300000; dbp:populationMetro “12,300,000 to 13,945,000”; dbo:populationTotal 7556900;owl:sameAs <http://www.okkam.org/ens/id...>. <http://www.okkam.org/ens/id...> :population 7421209.

  6. Uncertainty in linked data3. Other issues • Misalignment of models • e.g. freebase/dbpedia links generated (temporary) problems :Musician owl:equivalentClass :Person • Source reliability • not unique to linked data but amplifies it

  7. 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 void:LinkSet [a ur:WeightedLink; ur:target <…>; ur:match <…>; ur:weight 0.7] … a ur:UncertainLinkSet ur:matchAlorithm alg:JaroStringMatch .

  8. 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 ur:ImpreciseValue :sampleValue [:value 7556900; :source :dbpedia; :context :year2009]; :sampleValue [:value 7421209; :source :okkam; :context :year2008]; :estimatedValue 7500000] .

  9. 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 void:DataSet ur:argGraph void:DataSet ur:ComputedDataSet ur:Combinator ur:Difference Union

  10. 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

More Related