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The   Anna   Karenina   problem   in   vocabulary   alignment:“Happy alignments are all alike; every unhappy alignment is ...
OAEI VLCR track• 2008: 1 participant• 2009: 2 participants• 2010, 2011                         1
OAEI Library track•   2008: 3 participants•   2009: 1 participant•   2010, 2011•   2012: It’s back!                       ...
OAEI Directory track     from: Results of the Ontology Alignment Evaluation Initiative 2010     JérômeEuzenat, Alfio Ferra...
Observations• Current systems are complex  reasoning engines that combine  multiple strategies in some “smart” way• This “...
Bad news, good news• Bad news:  – alignments fail for different reasons every time  – solving this is an AI-complete probl...
Evaluation• Current evaluation protocol    – is not suited for evaluating interactive features    – has abstracted away al...
Example: AAT to WordNet• aat:restorer    altLabels: restaurateur (fr), Restaurator (de) , hersteller (nl), ...    scopeNot...
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Anna Karenina in Ontology Matching

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my opening statement for the om2012 panel

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Anna Karenina in Ontology Matching

  1. 1. The Anna Karenina problem in vocabulary alignment:“Happy alignments are all alike; every unhappy alignment is unhappy in its own way”Jacco van Ossenbruggen Panel at the Ontology MatchingCWI & VU University Amsterdam workshop, ISWC 2012
  2. 2. OAEI VLCR track• 2008: 1 participant• 2009: 2 participants• 2010, 2011 1
  3. 3. OAEI Library track• 2008: 3 participants• 2009: 1 participant• 2010, 2011• 2012: It’s back! 2
  4. 4. OAEI Directory track from: Results of the Ontology Alignment Evaluation Initiative 2010 JérômeEuzenat, Alfio Ferrara, Christian Meilicke, Juan Pane, François Scharffe, PavelShvaiko, HeinerStuckenschmidt, OndřejŠváb-Zamazal, VojtěchSvátek and CássiaTrojahn dos Santos 3
  5. 5. Observations• Current systems are complex reasoning engines that combine multiple strategies in some “smart” way• This “smartness” has major drawbacks: – does not scale on large vocabularies – hard to predict if it will work for your data – hard to explain results afterwards: what went wrong, why & how to fix it 4
  6. 6. Bad news, good news• Bad news: – alignments fail for different reasons every time – solving this is an AI-complete problem – requires knowledge that is in the heads of the domain experts, not in the data• Good news: – with experts on board, it is not that difficult – we can even do large datasets interactively – users are willing to spend time to get it right 5
  7. 7. Evaluation• Current evaluation protocol – is not suited for evaluating interactive features – has abstracted away all human parties involved • ontology publishers • application developers • application users – ignores that ontology publishers are often willing to spend serious time & effort on alignment processhttp://semanticweb.cs.vu.nl/lod/tpdl2011/ 6
  8. 8. Example: AAT to WordNet• aat:restorer altLabels: restaurateur (fr), Restaurator (de) , hersteller (nl), ... scopeNote: Those engaged in making changes to an object or structure so that it will closely approximate its state at a specific time in its history. (...) When changes made are to prevent further deterioration, see "preservationists." More generally, for those who undertake treatment, preventive care, and research directed toward long-term safekeeping of cultural and natural heritage, see "conservators."• wn:restorer synonyms: refinisher, renovator, restorer, preserver gloss: a skilled worker who is employed to restore or refinish buildings or antique furniture.http://semanticweb.cs.vu.nl/lod/tpdl2011/ 7

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