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IntroductionApproachConclusionIntegrating YAGO into theSuggested Upper Merged OntologyG. de Melo1, F. Suchanek1, A. Pease2...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOOutline1 IntroductionOntologies and KBsSUMOExt...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionOntologies/KBs: providebackground ...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionOntologies/KBs: providebackground ...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionOntologies/KBs: providebackground ...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMOSuggested Upper Merged Ontolog...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMOSuggested Upper Merged Ontolog...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMOSuggested Upper Merged Ontolog...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMOSuggested Upper Merged Ontolog...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroduction: Why Axiomatic Ontologies?G. de M...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroduction: Why Axiomatic Ontologies?G. de M...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMO Example(=>(and(parent ?CHILD ...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionStructure of SUMOG. de Melo, F. Su...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMOadditional domain ontologiesho...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMOadditional domain ontologiesho...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMOadditional domain ontologiesho...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionExtending Ontologies: Possible App...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionExtending Ontologies: Possible App...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionExtending Ontologies: Possible App...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionYAGOcombine entities and facts fro...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionYAGOcombine entities and facts fro...
IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionYAGOcombine entities and facts fro...
IntroductionApproachConclusionIncorporationClass InformationStatementsOutline1 IntroductionOntologies and KBsSUMOExtending...
IntroductionApproachConclusionIncorporationClass InformationStatementsIncorporationIdea: most Wikipedia articles become ne...
IntroductionApproachConclusionIncorporationClass InformationStatementsIncorporationIdea: most Wikipedia articles become ne...
IntroductionApproachConclusionIncorporationClass InformationStatementsIncorporationIdea: most Wikipedia articles become ne...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGO: From Wikipedia to WordNetgoal...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGO: From Wikipedia to WordNetgoal...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGO: From Wikipedia to WordNetgoal...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGO: From Wikipedia to WordNetgoal...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGOshallowparsing: noungroup parse...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGOshallowparsing: noungroup parse...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGO: From Wikipedia to WordNetchec...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGO: From Wikipedia to WordNetchec...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGO: From Wikipedia to WordNetchec...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationVoting Procedureproblem:regular pol...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationVoting Procedureproblem:regular pol...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationFrom WordNet toSUMOgood news:existi...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationFrom WordNet toSUMOin some cases, t...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationFrom WordNet toSUMOin many cases, t...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationFrom WordNet to SUMOin further case...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationEvaluationlots of heuristics, multi...
IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationEvaluationlots of heuristics, multi...
IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsInformation ExtractionYAGO uses manual rul...
IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsInformation ExtractionYAGO uses manual rul...
IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsInformation ExtractionYAGO uses manual rul...
IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsInformation ExtractionYAGO uses manual rul...
IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsSUMO Integrationmapping rulesnew relations...
IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsSUMO Integrationmapping rulesnew relations...
IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsSUMO Integrationmapping rulesnew relations...
IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsStatements with Literalsproper encoding of...
IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsStatements with Literalsproper encoding of...
IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsAdditional Groundingstatements of the form...
IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsAdditional Groundingstatements of the form...
IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsAdditional Groundingstatements of the form...
IntroductionApproachConclusionOngoing WorkSummaryOutline1 IntroductionOntologies and KBsSUMOExtending OntologiesYAGO2 Appr...
IntroductionApproachConclusionOngoing WorkSummaryOngoing WorkOngoing WorkTPTP transformation for reasoningSUMO problems in...
IntroductionApproachConclusionOngoing WorkSummaryOngoing WorkOngoing WorkTPTP transformation for reasoningSUMO problems in...
IntroductionApproachConclusionOngoing WorkSummaryOngoing WorkOngoing WorkTPTP transformation for reasoningSUMO problems in...
IntroductionApproachConclusionOngoing WorkSummarySummarySummarySUMO: axiomatic representation of common sense knowledgebut...
IntroductionApproachConclusionOngoing WorkSummarySummarySummarySUMO: axiomatic representation of common sense knowledgebut...
IntroductionApproachConclusionOngoing WorkSummarySummarySummarySUMO: axiomatic representation of common sense knowledgebut...
IntroductionApproachConclusionOngoing WorkSummarySummarySummarySUMO: axiomatic representation of common sense knowledgebut...
IntroductionApproachConclusionOngoing WorkSummarySummarySummarySUMO: axiomatic representation of common sense knowledgebut...
IntroductionApproachConclusionOngoing WorkSummarySummarySummarySUMO: axiomatic representation of common sense knowledgebut...
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YAGO-SUMO: Integrating YAGO into the Suggested Upper Merged Ontology

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The YAGO-SUMO integration incorporates millions of entities from YAGO, which is based on Wikipedia and WordNet, into the Suggested Upper Merged Ontology (SUMO), a highly axiomatized formal upper ontology. With the combined force of the two ontologies, an enormous, unprecedented corpus of formalized world knowledge is available for automated processing and reasoning, providing information about millions of entities such as people, cities, organizations, and companies.

Compared to the original YAGO, more advanced reasoning is possible due to the axiomatic knowledge delivered by SUMO. A reasoner can conclude e.g. that a child of a human must also be a human and cannot be born before its parents, or that two people sharing the same parents must be siblings.

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Transcript of "YAGO-SUMO: Integrating YAGO into the Suggested Upper Merged Ontology"

  1. 1. IntroductionApproachConclusionIntegrating YAGO into theSuggested Upper Merged OntologyG. de Melo1, F. Suchanek1, A. Pease21: Max Planck Institute for Informatics, Germany2: Articulate Software, USA2008-11-03G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  2. 2. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOOutline1 IntroductionOntologies and KBsSUMOExtending OntologiesYAGO2 ApproachIncorporationClass InformationStatements3 ConclusionOngoing WorkSummaryG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  3. 3. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionOntologies/KBs: providebackground knowledge forintelligent applicationsSchism:formal ontologies vs. large KBsGoal: Large-scale formal ontologyG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  4. 4. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionOntologies/KBs: providebackground knowledge forintelligent applicationsSchism:formal ontologies vs. large KBsGoal: Large-scale formal ontologyformal ontologies: complex axioms(e.g. in FOL), but quite smalllarge-scale KBs (e.g. based onWikipedia): only simple factsG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  5. 5. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionOntologies/KBs: providebackground knowledge forintelligent applicationsSchism:formal ontologies vs. large KBsGoal: Large-scale formal ontologycombine the best of both worlds!G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  6. 6. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMOSuggested Upper Merged Ontologyopen sourcebased on KIF rather than e.g. OWLlarge formal ontology (20,000 terms, 70,000 axioms)axiomatization of general and domain-specific conceptsfor applications requiring basic “common sense”G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  7. 7. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMOSuggested Upper Merged Ontologyopen sourcebased on KIF rather than e.g. OWLorigins: IEEE standard upper ontology groupcore owned by IEEE (basically Public Domain), portions GPLe.g.: OpenCyc doesn’t include axioms of commercial CycG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  8. 8. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMOSuggested Upper Merged Ontologyopen sourcebased on KIF rather than e.g. OWLpeer review, community of experts and usersformal verification with ATP systemsG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  9. 9. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMOSuggested Upper Merged Ontologyopen sourcebased on KIF rather than e.g. OWLOWL without additional rules is not very expressiveKIF variant standardized as ISO/IEC IS 24707:2007(Common Logic)G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  10. 10. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroduction: Why Axiomatic Ontologies?G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  11. 11. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroduction: Why Axiomatic Ontologies?G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  12. 12. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMO Example(=>(and(parent ?CHILD ?PARENT)(subclass ?CLASS Organism)(instance ?PARENT ?CLASS))(instance ?CHILD ?CLASS))This implies, for example, that a child of a Human is also a Human.G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  13. 13. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionStructure of SUMOG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  14. 14. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMOadditional domain ontologieshowever, SUMO is mainly an upper ontologynot enough instances and ground factse.g. for geography, finance, transportationG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  15. 15. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMOadditional domain ontologieshowever, SUMO is mainly an upper ontologynot enough instances and ground factsG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  16. 16. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionSUMOadditional domain ontologieshowever, SUMO is mainly an upper ontologynot enough instances and ground factse.g. people, cities, booksG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  17. 17. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionExtending Ontologies: Possible ApproachesManual workInformation extraction from corpora / the WebImport from existing databasesslow process, low coverageSemantic Wikis not yet accepted enoughG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  18. 18. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionExtending Ontologies: Possible ApproachesManual workInformation extraction from corpora / the WebImport from existing databaseslow accuracynot canonical / in line with upper ontologyG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  19. 19. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionExtending Ontologies: Possible ApproachesManual workInformation extraction from corpora / the WebImport from existing databasesfeasible, but not universal enoughG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  20. 20. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionYAGOcombine entities and facts from Wikipedia with an upperontologyoriginal YAGO: WordNet for the upper levelNew goal: integrate with SUMOexcellent coverage: around 2 million entitiesmillions of facts about themhigh quality: e.g. birth dates of people, location of citiesG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  21. 21. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionYAGOcombine entities and facts from Wikipedia with an upperontologyoriginal YAGO: WordNet for the upper levelNew goal: integrate with SUMOmainly a lexical knowledge basee.g. hyponymic relationships do not strictly imply subsumptionslack of formal axiomsG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  22. 22. IntroductionApproachConclusionOntologies and KBsSUMOExtending OntologiesYAGOIntroductionYAGOcombine entities and facts from Wikipedia with an upperontologyoriginal YAGO: WordNet for the upper levelNew goal: integrate with SUMOso the class information actually is meaningfulG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  23. 23. IntroductionApproachConclusionIncorporationClass InformationStatementsOutline1 IntroductionOntologies and KBsSUMOExtending OntologiesYAGO2 ApproachIncorporationClass InformationStatements3 ConclusionOngoing WorkSummaryG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  24. 24. IntroductionApproachConclusionIncorporationClass InformationStatementsIncorporationIdea: most Wikipedia articles become new entitiesSemi-automatic matching: although SUMO contains onlyfew instances, some degree of overlap existsuse weighted string similarity measureadditional manual validation−→ equivalence tableEntity Generation: produce a new unique term name forWikipedia article not listed in equivalence table, subject to thefollowing desiderata:prevent clashes with SUMO or other entitiesconcisenessabide to KIF syntax (Wikipedia uses Unicode)must be a proper entity (not: “List of ...”)G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  25. 25. IntroductionApproachConclusionIncorporationClass InformationStatementsIncorporationIdea: most Wikipedia articles become new entitiesSemi-automatic matching: although SUMO contains onlyfew instances, some degree of overlap existsuse weighted string similarity measureadditional manual validation−→ equivalence tableEntity Generation: produce a new unique term name forWikipedia article not listed in equivalence table, subject to thefollowing desiderata:prevent clashes with SUMO or other entitiesconcisenessabide to KIF syntax (Wikipedia uses Unicode)must be a proper entity (not: “List of ...”)G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  26. 26. IntroductionApproachConclusionIncorporationClass InformationStatementsIncorporationIdea: most Wikipedia articles become new entitiesSemi-automatic matching: although SUMO contains onlyfew instances, some degree of overlap existsuse weighted string similarity measureadditional manual validation−→ equivalence tableEntity Generation: produce a new unique term name forWikipedia article not listed in equivalence table, subject to thefollowing desiderata:prevent clashes with SUMO or other entitiesconcisenessabide to KIF syntax (Wikipedia uses Unicode)must be a proper entity (not: “List of ...”)G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  27. 27. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGO: From Wikipedia to WordNetgoal: each entity should have class membership informationuse Wikipedia category system, however cannot use it directlyfirst link categories to WordNet, then map to SUMOrequirement: distinguish thematic categories from categoriesencoding class membershipG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  28. 28. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGO: From Wikipedia to WordNetgoal: each entity should have class membership informationuse Wikipedia category system, however cannot use it directlyfirst link categories to WordNet, then map to SUMOrequirement: distinguish thematic categories from categoriesencoding class membershipcategorization not transitivemembers of subcategories often unrelated to parent categoryG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  29. 29. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGO: From Wikipedia to WordNetgoal: each entity should have class membership informationuse Wikipedia category system, however cannot use it directlyfirst link categories to WordNet, then map to SUMOrequirement: distinguish thematic categories from categoriesencoding class membershipG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  30. 30. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGO: From Wikipedia to WordNetgoal: each entity should have class membership informationuse Wikipedia category system, however cannot use it directlyfirst link categories to WordNet, then map to SUMOrequirement: distinguish thematic categories from categoriesencoding class membershipG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  31. 31. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGOshallowparsing: noungroup parser toidentifyheadwordheuristic:ignorecategories withheadword insingular formG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  32. 32. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGOshallowparsing: noungroup parser toidentifyheadwordheuristic:ignorecategories withheadword insingular formG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  33. 33. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGO: From Wikipedia to WordNetcheck WordNet for premodifier + headword or headword onlydisambiguate using frequency informationresult: relationship to WordNet-derived classe.g. “American singer” or “singer”G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  34. 34. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGO: From Wikipedia to WordNetcheck WordNet for premodifier + headword or headword onlydisambiguate using frequency informationresult: relationship to WordNet-derived classG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  35. 35. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationYAGO: From Wikipedia to WordNetcheck WordNet for premodifier + headword or headword onlydisambiguate using frequency informationresult: relationship to WordNet-derived classAmerican singers of German originbecomes linked as a subclass to theWordNet-derived class PersonG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  36. 36. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationVoting Procedureproblem:regular polysemy, Wikipedia articles simultaneously coverseveral metonymically related sensese.g. Brown University is both a College and aGroupOfPeoplewill cause inconsistencies when the axioms are addedsolution:look at top-level branches for each proposed class (locations,artifacts, abstract entities, etc.)voting procedure to determine most salient branch (ties brokenarbitrarily)G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  37. 37. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationVoting Procedureproblem:regular polysemy, Wikipedia articles simultaneously coverseveral metonymically related sensese.g. Brown University is both a College and aGroupOfPeoplewill cause inconsistencies when the axioms are addedsolution:look at top-level branches for each proposed class (locations,artifacts, abstract entities, etc.)voting procedure to determine most salient branch (ties brokenarbitrarily)G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  38. 38. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationFrom WordNet toSUMOgood news:existing manuallyestablished WordNet-SUMO-mappingsG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  39. 39. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationFrom WordNet toSUMOin some cases, thesemappings provide anequivalent SUMOclass−→ directly use theSUMO class instead ofthe WordNet oneE.g. Human instead ofWordNet’s “person”G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  40. 40. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationFrom WordNet toSUMOin many cases, themappings provide asuper-class−→ create newWordNet-based class,make it a subclass ofSUMO classG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  41. 41. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationFrom WordNet to SUMOin further cases, the mappings yield a property or relation−→ create new WordNet-based class, add axioms of theform(=>(instance ?ENTITY Guitarist)(property ?ENTITY Musician))Then recursively move up WordNet’s class hierarchy addingparent classes, until until a genuine parent class in SUMO isavailable.G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  42. 42. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationEvaluationlots of heuristics, multiple stepsyet: accuracy of 92.67% ± 2.98% (evaluation of most specificgenuine SUMO parents for new instances)G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  43. 43. IntroductionApproachConclusionIncorporationClass InformationStatementsClass InformationEvaluationlots of heuristics, multiple stepsyet: accuracy of 92.67% ± 2.98% (evaluation of most specificgenuine SUMO parents for new instances)G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  44. 44. IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsInformation ExtractionYAGO uses manual rules and heuristics to extract informationabout entities from Wikipedia pagesmainly based on categories and infoboxes, not on article text,e.g. geographical location, spouse, etc.manual rewriting rules to express facts using SUMO’s termssample evaluation: for each relation, at least 95% of thestatements are accurateG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  45. 45. IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsInformation ExtractionYAGO uses manual rules and heuristics to extract informationabout entities from Wikipedia pagesmainly based on categories and infoboxes, not on article text,e.g. geographical location, spouse, etc.manual rewriting rules to express facts using SUMO’s termssample evaluation: for each relation, at least 95% of thestatements are accurateG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  46. 46. IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsInformation ExtractionYAGO uses manual rules and heuristics to extract informationabout entities from Wikipedia pagesmainly based on categories and infoboxes, not on article text,e.g. geographical location, spouse, etc.manual rewriting rules to express facts using SUMO’s termssample evaluation: for each relation, at least 95% of thestatements are accurateG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  47. 47. IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsInformation ExtractionYAGO uses manual rules and heuristics to extract informationabout entities from Wikipedia pagesmainly based on categories and infoboxes, not on article text,e.g. geographical location, spouse, etc.manual rewriting rules to express facts using SUMO’s termssample evaluation: for each relation, at least 95% of thestatements are accurateG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  48. 48. IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsSUMO Integrationmapping rulesnew relations added to SUMO when necessaryincl. additional rules for reasoningextracted fact:X hasCapital Ybecomes:(capitalCity Y X)G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  49. 49. IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsSUMO Integrationmapping rulesnew relations added to SUMO when necessaryincl. additional rules for reasoning(instance establishedOnDate BinaryRelation)(domain 1 establishedOnDate Agent)(domain 2 establishedOnDate TimeInterval)(=> (establishedOnDate ?OBJ ?TIME)(exists (?FOUNDING)(and (instance ?FOUNDING Founding)(result ?FOUNDING ?OBJ)(overlapsTemporally (WhenFn ?FOUNDING) TIME))))G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  50. 50. IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsSUMO Integrationmapping rulesnew relations added to SUMO when necessaryincl. additional rules for reasoning(instance establishedOnDate BinaryRelation)(domain 1 establishedOnDate Agent)(domain 2 establishedOnDate TimeInterval)(=> (establishedOnDate ?OBJ ?TIME)(exists (?FOUNDING)(and (instance ?FOUNDING Founding)(result ?FOUNDING ?OBJ)(overlapsTemporally (WhenFn ?FOUNDING) TIME))))G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  51. 51. IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsStatements with Literalsproper encoding of literals with units:e.g. (MeasureFn 3.0 SquareMeter)date ranges are recast(exists ?YEARNO ?MONTHNO ?YEARNO(and(birthdate HerveyDeStanton(DayFn ?DAYNO(MonthFn ?MONTHNO(YearFn ?YEARNO))))(greaterThanOrEqualTo ?YEARNO 1270)(lessThanOrEqualTo ?YEARNO 1279)))G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  52. 52. IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsStatements with Literalsproper encoding of literals with units:e.g. (MeasureFn 3.0 SquareMeter)date ranges are recast(exists ?YEARNO ?MONTHNO ?YEARNO(and(birthdate HerveyDeStanton(DayFn ?DAYNO(MonthFn ?MONTHNO(YearFn ?YEARNO))))(greaterThanOrEqualTo ?YEARNO 1270)(lessThanOrEqualTo ?YEARNO 1279)))G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  53. 53. IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsAdditional Groundingstatements of the form(representsInLanguage"Immanuel Kant" ImmanuelKant EnglishLanguage)produce a greater level of formal grounding of the semanticsof term nameswhen names are ambiguous, providing such symbolic stringsfor multiple languages can further reduce the range of possibleinterpretationsclasses are better-specified due to their extensionalcharacterizationG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  54. 54. IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsAdditional Groundingstatements of the form(representsInLanguage"Immanuel Kant" ImmanuelKant EnglishLanguage)produce a greater level of formal grounding of the semanticsof term nameswhen names are ambiguous, providing such symbolic stringsfor multiple languages can further reduce the range of possibleinterpretationsclasses are better-specified due to their extensionalcharacterizationG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  55. 55. IntroductionApproachConclusionIncorporationClass InformationStatementsStatementsAdditional Groundingstatements of the form(representsInLanguage"Immanuel Kant" ImmanuelKant EnglishLanguage)produce a greater level of formal grounding of the semanticsof term nameswhen names are ambiguous, providing such symbolic stringsfor multiple languages can further reduce the range of possibleinterpretationsclasses are better-specified due to their extensionalcharacterizationG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  56. 56. IntroductionApproachConclusionOngoing WorkSummaryOutline1 IntroductionOntologies and KBsSUMOExtending OntologiesYAGO2 ApproachIncorporationClass InformationStatements3 ConclusionOngoing WorkSummaryG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  57. 57. IntroductionApproachConclusionOngoing WorkSummaryOngoing WorkOngoing WorkTPTP transformation for reasoningSUMO problems in CADE competitionsATP systems for large-scale reasoningG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  58. 58. IntroductionApproachConclusionOngoing WorkSummaryOngoing WorkOngoing WorkTPTP transformation for reasoningSUMO problems in CADE competitionsATP systems for large-scale reasoningG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  59. 59. IntroductionApproachConclusionOngoing WorkSummaryOngoing WorkOngoing WorkTPTP transformation for reasoningSUMO problems in CADE competitionsATP systems for large-scale reasoningG. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  60. 60. IntroductionApproachConclusionOngoing WorkSummarySummarySummarySUMO: axiomatic representation of common sense knowledgebut lack of simple encyclopedic factsYAGO methodology: add entities and statements about themfrom Wikipediasemi-automatic techniques, basic amount of manual work−→ formal ontology with around two million entities andseveral million statements and axiomsSUMO is catapulted from an upper level ontology to afull-fledged all-purpose KBOpen source, available online:http://www.demelo.org/yagosumo/G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  61. 61. IntroductionApproachConclusionOngoing WorkSummarySummarySummarySUMO: axiomatic representation of common sense knowledgebut lack of simple encyclopedic factsYAGO methodology: add entities and statements about themfrom Wikipediasemi-automatic techniques, basic amount of manual work−→ formal ontology with around two million entities andseveral million statements and axiomsSUMO is catapulted from an upper level ontology to afull-fledged all-purpose KBOpen source, available online:http://www.demelo.org/yagosumo/G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  62. 62. IntroductionApproachConclusionOngoing WorkSummarySummarySummarySUMO: axiomatic representation of common sense knowledgebut lack of simple encyclopedic factsYAGO methodology: add entities and statements about themfrom Wikipediasemi-automatic techniques, basic amount of manual work−→ formal ontology with around two million entities andseveral million statements and axiomsSUMO is catapulted from an upper level ontology to afull-fledged all-purpose KBOpen source, available online:http://www.demelo.org/yagosumo/G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  63. 63. IntroductionApproachConclusionOngoing WorkSummarySummarySummarySUMO: axiomatic representation of common sense knowledgebut lack of simple encyclopedic factsYAGO methodology: add entities and statements about themfrom Wikipediasemi-automatic techniques, basic amount of manual work−→ formal ontology with around two million entities andseveral million statements and axiomsSUMO is catapulted from an upper level ontology to afull-fledged all-purpose KBOpen source, available online:http://www.demelo.org/yagosumo/G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  64. 64. IntroductionApproachConclusionOngoing WorkSummarySummarySummarySUMO: axiomatic representation of common sense knowledgebut lack of simple encyclopedic factsYAGO methodology: add entities and statements about themfrom Wikipediasemi-automatic techniques, basic amount of manual work−→ formal ontology with around two million entities andseveral million statements and axiomsSUMO is catapulted from an upper level ontology to afull-fledged all-purpose KBOpen source, available online:http://www.demelo.org/yagosumo/G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  65. 65. IntroductionApproachConclusionOngoing WorkSummarySummarySummarySUMO: axiomatic representation of common sense knowledgebut lack of simple encyclopedic factsYAGO methodology: add entities and statements about themfrom Wikipediasemi-automatic techniques, basic amount of manual work−→ formal ontology with around two million entities andseveral million statements and axiomsSUMO is catapulted from an upper level ontology to afull-fledged all-purpose KBOpen source, available online:http://www.demelo.org/yagosumo/G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
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