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YAGO-SUMO: Integrating YAGO into the Suggested Upper Merged Ontology

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Introduction
Approach
Conclusion
Integrating YAGO into the
Suggested Upper Merged Ontology
G. de Melo1, F. Suchanek1, A. P...

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Introduction
Approach
Conclusion
Ontologies and KBs
SUMO
Extending Ontologies
YAGO
Outline
1 Introduction
Ontologies and K...

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Introduction
Approach
Conclusion
Ontologies and KBs
SUMO
Extending Ontologies
YAGO
Introduction
Ontologies/KBs: provide
ba...

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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.

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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YAGO-SUMO: Integrating YAGO into the Suggested Upper Merged Ontology

  1. 1. Introduction Approach Conclusion Integrating YAGO into the Suggested Upper Merged Ontology G. de Melo1, F. Suchanek1, A. Pease2 1: Max Planck Institute for Informatics, Germany 2: Articulate Software, USA 2008-11-03 G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  2. 2. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Outline 1 Introduction Ontologies and KBs SUMO Extending Ontologies YAGO 2 Approach Incorporation Class Information Statements 3 Conclusion Ongoing Work Summary G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  3. 3. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction Ontologies/KBs: provide background knowledge for intelligent applications Schism: formal ontologies vs. large KBs Goal: Large-scale formal ontology G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  4. 4. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction Ontologies/KBs: provide background knowledge for intelligent applications Schism: formal ontologies vs. large KBs Goal: Large-scale formal ontology formal ontologies: complex axioms (e.g. in FOL), but quite small large-scale KBs (e.g. based on Wikipedia): only simple facts G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  5. 5. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction Ontologies/KBs: provide background knowledge for intelligent applications Schism: formal ontologies vs. large KBs Goal: Large-scale formal ontology combine the best of both worlds! G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  6. 6. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction SUMO Suggested Upper Merged Ontology open source based on KIF rather than e.g. OWL large formal ontology (20,000 terms, 70,000 axioms) axiomatization of general and domain-specific concepts for applications requiring basic “common sense” G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  7. 7. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction SUMO Suggested Upper Merged Ontology open source based on KIF rather than e.g. OWL origins: IEEE standard upper ontology group core owned by IEEE (basically Public Domain), portions GPL e.g.: OpenCyc doesn’t include axioms of commercial Cyc G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  8. 8. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction SUMO Suggested Upper Merged Ontology open source based on KIF rather than e.g. OWL peer review, community of experts and users formal verification with ATP systems G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  9. 9. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction SUMO Suggested Upper Merged Ontology open source based on KIF rather than e.g. OWL OWL without additional rules is not very expressive KIF 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. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction: Why Axiomatic Ontologies? G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  11. 11. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction: Why Axiomatic Ontologies? G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  12. 12. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction SUMO 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. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction Structure of SUMO G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  14. 14. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction SUMO additional domain ontologies however, SUMO is mainly an upper ontology not enough instances and ground facts e.g. for geography, finance, transportation G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  15. 15. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction SUMO additional domain ontologies however, SUMO is mainly an upper ontology not enough instances and ground facts G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  16. 16. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction SUMO additional domain ontologies however, SUMO is mainly an upper ontology not enough instances and ground facts e.g. people, cities, books G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  17. 17. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction Extending Ontologies: Possible Approaches Manual work Information extraction from corpora / the Web Import from existing databases slow process, low coverage Semantic Wikis not yet accepted enough G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  18. 18. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction Extending Ontologies: Possible Approaches Manual work Information extraction from corpora / the Web Import from existing databases low accuracy not canonical / in line with upper ontology G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  19. 19. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction Extending Ontologies: Possible Approaches Manual work Information extraction from corpora / the Web Import from existing databases feasible, but not universal enough G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  20. 20. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction YAGO combine entities and facts from Wikipedia with an upper ontology original YAGO: WordNet for the upper level New goal: integrate with SUMO excellent coverage: around 2 million entities millions of facts about them high quality: e.g. birth dates of people, location of cities G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  21. 21. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction YAGO combine entities and facts from Wikipedia with an upper ontology original YAGO: WordNet for the upper level New goal: integrate with SUMO mainly a lexical knowledge base e.g. hyponymic relationships do not strictly imply subsumptions lack of formal axioms G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  22. 22. Introduction Approach Conclusion Ontologies and KBs SUMO Extending Ontologies YAGO Introduction YAGO combine entities and facts from Wikipedia with an upper ontology original YAGO: WordNet for the upper level New goal: integrate with SUMO so the class information actually is meaningful G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  23. 23. Introduction Approach Conclusion Incorporation Class Information Statements Outline 1 Introduction Ontologies and KBs SUMO Extending Ontologies YAGO 2 Approach Incorporation Class Information Statements 3 Conclusion Ongoing Work Summary G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  24. 24. Introduction Approach Conclusion Incorporation Class Information Statements Incorporation Idea: most Wikipedia articles become new entities Semi-automatic matching: although SUMO contains only few instances, some degree of overlap exists use weighted string similarity measure additional manual validation −→ equivalence table Entity Generation: produce a new unique term name for Wikipedia article not listed in equivalence table, subject to the following desiderata: prevent clashes with SUMO or other entities conciseness abide 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. Introduction Approach Conclusion Incorporation Class Information Statements Incorporation Idea: most Wikipedia articles become new entities Semi-automatic matching: although SUMO contains only few instances, some degree of overlap exists use weighted string similarity measure additional manual validation −→ equivalence table Entity Generation: produce a new unique term name for Wikipedia article not listed in equivalence table, subject to the following desiderata: prevent clashes with SUMO or other entities conciseness abide 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. Introduction Approach Conclusion Incorporation Class Information Statements Incorporation Idea: most Wikipedia articles become new entities Semi-automatic matching: although SUMO contains only few instances, some degree of overlap exists use weighted string similarity measure additional manual validation −→ equivalence table Entity Generation: produce a new unique term name for Wikipedia article not listed in equivalence table, subject to the following desiderata: prevent clashes with SUMO or other entities conciseness abide 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. Introduction Approach Conclusion Incorporation Class Information Statements Class Information YAGO: From Wikipedia to WordNet goal: each entity should have class membership information use Wikipedia category system, however cannot use it directly first link categories to WordNet, then map to SUMO requirement: distinguish thematic categories from categories encoding class membership G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  28. 28. Introduction Approach Conclusion Incorporation Class Information Statements Class Information YAGO: From Wikipedia to WordNet goal: each entity should have class membership information use Wikipedia category system, however cannot use it directly first link categories to WordNet, then map to SUMO requirement: distinguish thematic categories from categories encoding class membership categorization not transitive members of subcategories often unrelated to parent category G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  29. 29. Introduction Approach Conclusion Incorporation Class Information Statements Class Information YAGO: From Wikipedia to WordNet goal: each entity should have class membership information use Wikipedia category system, however cannot use it directly first link categories to WordNet, then map to SUMO requirement: distinguish thematic categories from categories encoding class membership G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  30. 30. Introduction Approach Conclusion Incorporation Class Information Statements Class Information YAGO: From Wikipedia to WordNet goal: each entity should have class membership information use Wikipedia category system, however cannot use it directly first link categories to WordNet, then map to SUMO requirement: distinguish thematic categories from categories encoding class membership G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  31. 31. Introduction Approach Conclusion Incorporation Class Information Statements Class Information YAGO shallow parsing: noun group parser to identify headword heuristic: ignore categories with headword in singular form G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  32. 32. Introduction Approach Conclusion Incorporation Class Information Statements Class Information YAGO shallow parsing: noun group parser to identify headword heuristic: ignore categories with headword in singular form G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  33. 33. Introduction Approach Conclusion Incorporation Class Information Statements Class Information YAGO: From Wikipedia to WordNet check WordNet for premodifier + headword or headword only disambiguate using frequency information result: relationship to WordNet-derived class e.g. “American singer” or “singer” G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  34. 34. Introduction Approach Conclusion Incorporation Class Information Statements Class Information YAGO: From Wikipedia to WordNet check WordNet for premodifier + headword or headword only disambiguate using frequency information result: relationship to WordNet-derived class G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  35. 35. Introduction Approach Conclusion Incorporation Class Information Statements Class Information YAGO: From Wikipedia to WordNet check WordNet for premodifier + headword or headword only disambiguate using frequency information result: relationship to WordNet-derived class American singers of German origin becomes linked as a subclass to the WordNet-derived class Person G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  36. 36. Introduction Approach Conclusion Incorporation Class Information Statements Class Information Voting Procedure problem: regular polysemy, Wikipedia articles simultaneously cover several metonymically related senses e.g. Brown University is both a College and a GroupOfPeople will cause inconsistencies when the axioms are added solution: look at top-level branches for each proposed class (locations, artifacts, abstract entities, etc.) voting procedure to determine most salient branch (ties broken arbitrarily) G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  37. 37. Introduction Approach Conclusion Incorporation Class Information Statements Class Information Voting Procedure problem: regular polysemy, Wikipedia articles simultaneously cover several metonymically related senses e.g. Brown University is both a College and a GroupOfPeople will cause inconsistencies when the axioms are added solution: look at top-level branches for each proposed class (locations, artifacts, abstract entities, etc.) voting procedure to determine most salient branch (ties broken arbitrarily) G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  38. 38. Introduction Approach Conclusion Incorporation Class Information Statements Class Information From WordNet to SUMO good news: existing manually established WordNet- SUMO-mappings G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  39. 39. Introduction Approach Conclusion Incorporation Class Information Statements Class Information From WordNet to SUMO in some cases, these mappings provide an equivalent SUMO class −→ directly use the SUMO class instead of the WordNet one E.g. Human instead of WordNet’s “person” G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  40. 40. Introduction Approach Conclusion Incorporation Class Information Statements Class Information From WordNet to SUMO in many cases, the mappings provide a super-class −→ create new WordNet-based class, make it a subclass of SUMO class G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  41. 41. Introduction Approach Conclusion Incorporation Class Information Statements Class Information From WordNet to SUMO in further cases, the mappings yield a property or relation −→ create new WordNet-based class, add axioms of the form (=> (instance ?ENTITY Guitarist) (property ?ENTITY Musician)) Then recursively move up WordNet’s class hierarchy adding parent classes, until until a genuine parent class in SUMO is available. G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  42. 42. Introduction Approach Conclusion Incorporation Class Information Statements Class Information Evaluation lots of heuristics, multiple steps yet: accuracy of 92.67% ± 2.98% (evaluation of most specific genuine SUMO parents for new instances) G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  43. 43. Introduction Approach Conclusion Incorporation Class Information Statements Class Information Evaluation lots of heuristics, multiple steps yet: accuracy of 92.67% ± 2.98% (evaluation of most specific genuine SUMO parents for new instances) G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  44. 44. Introduction Approach Conclusion Incorporation Class Information Statements Statements Information Extraction YAGO uses manual rules and heuristics to extract information about entities from Wikipedia pages mainly based on categories and infoboxes, not on article text, e.g. geographical location, spouse, etc. manual rewriting rules to express facts using SUMO’s terms sample evaluation: for each relation, at least 95% of the statements are accurate G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  45. 45. Introduction Approach Conclusion Incorporation Class Information Statements Statements Information Extraction YAGO uses manual rules and heuristics to extract information about entities from Wikipedia pages mainly based on categories and infoboxes, not on article text, e.g. geographical location, spouse, etc. manual rewriting rules to express facts using SUMO’s terms sample evaluation: for each relation, at least 95% of the statements are accurate G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  46. 46. Introduction Approach Conclusion Incorporation Class Information Statements Statements Information Extraction YAGO uses manual rules and heuristics to extract information about entities from Wikipedia pages mainly based on categories and infoboxes, not on article text, e.g. geographical location, spouse, etc. manual rewriting rules to express facts using SUMO’s terms sample evaluation: for each relation, at least 95% of the statements are accurate G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  47. 47. Introduction Approach Conclusion Incorporation Class Information Statements Statements Information Extraction YAGO uses manual rules and heuristics to extract information about entities from Wikipedia pages mainly based on categories and infoboxes, not on article text, e.g. geographical location, spouse, etc. manual rewriting rules to express facts using SUMO’s terms sample evaluation: for each relation, at least 95% of the statements are accurate G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  48. 48. Introduction Approach Conclusion Incorporation Class Information Statements Statements SUMO Integration mapping rules new relations added to SUMO when necessary incl. additional rules for reasoning extracted fact: X hasCapital Y becomes: (capitalCity Y X) G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  49. 49. Introduction Approach Conclusion Incorporation Class Information Statements Statements SUMO Integration mapping rules new relations added to SUMO when necessary incl. 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. Introduction Approach Conclusion Incorporation Class Information Statements Statements SUMO Integration mapping rules new relations added to SUMO when necessary incl. 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. Introduction Approach Conclusion Incorporation Class Information Statements Statements Statements with Literals proper 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. Introduction Approach Conclusion Incorporation Class Information Statements Statements Statements with Literals proper 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. Introduction Approach Conclusion Incorporation Class Information Statements Statements Additional Grounding statements of the form (representsInLanguage "Immanuel Kant" ImmanuelKant EnglishLanguage) produce a greater level of formal grounding of the semantics of term names when names are ambiguous, providing such symbolic strings for multiple languages can further reduce the range of possible interpretations classes are better-specified due to their extensional characterization G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  54. 54. Introduction Approach Conclusion Incorporation Class Information Statements Statements Additional Grounding statements of the form (representsInLanguage "Immanuel Kant" ImmanuelKant EnglishLanguage) produce a greater level of formal grounding of the semantics of term names when names are ambiguous, providing such symbolic strings for multiple languages can further reduce the range of possible interpretations classes are better-specified due to their extensional characterization G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  55. 55. Introduction Approach Conclusion Incorporation Class Information Statements Statements Additional Grounding statements of the form (representsInLanguage "Immanuel Kant" ImmanuelKant EnglishLanguage) produce a greater level of formal grounding of the semantics of term names when names are ambiguous, providing such symbolic strings for multiple languages can further reduce the range of possible interpretations classes are better-specified due to their extensional characterization G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  56. 56. Introduction Approach Conclusion Ongoing Work Summary Outline 1 Introduction Ontologies and KBs SUMO Extending Ontologies YAGO 2 Approach Incorporation Class Information Statements 3 Conclusion Ongoing Work Summary G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  57. 57. Introduction Approach Conclusion Ongoing Work Summary Ongoing Work Ongoing Work TPTP transformation for reasoning SUMO problems in CADE competitions ATP systems for large-scale reasoning G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  58. 58. Introduction Approach Conclusion Ongoing Work Summary Ongoing Work Ongoing Work TPTP transformation for reasoning SUMO problems in CADE competitions ATP systems for large-scale reasoning G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  59. 59. Introduction Approach Conclusion Ongoing Work Summary Ongoing Work Ongoing Work TPTP transformation for reasoning SUMO problems in CADE competitions ATP systems for large-scale reasoning G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  60. 60. Introduction Approach Conclusion Ongoing Work Summary Summary Summary SUMO: axiomatic representation of common sense knowledge but lack of simple encyclopedic facts YAGO methodology: add entities and statements about them from Wikipedia semi-automatic techniques, basic amount of manual work −→ formal ontology with around two million entities and several million statements and axioms SUMO is catapulted from an upper level ontology to a full-fledged all-purpose KB Open source, available online: http://www.demelo.org/yagosumo/ G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  61. 61. Introduction Approach Conclusion Ongoing Work Summary Summary Summary SUMO: axiomatic representation of common sense knowledge but lack of simple encyclopedic facts YAGO methodology: add entities and statements about them from Wikipedia semi-automatic techniques, basic amount of manual work −→ formal ontology with around two million entities and several million statements and axioms SUMO is catapulted from an upper level ontology to a full-fledged all-purpose KB Open source, available online: http://www.demelo.org/yagosumo/ G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  62. 62. Introduction Approach Conclusion Ongoing Work Summary Summary Summary SUMO: axiomatic representation of common sense knowledge but lack of simple encyclopedic facts YAGO methodology: add entities and statements about them from Wikipedia semi-automatic techniques, basic amount of manual work −→ formal ontology with around two million entities and several million statements and axioms SUMO is catapulted from an upper level ontology to a full-fledged all-purpose KB Open source, available online: http://www.demelo.org/yagosumo/ G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  63. 63. Introduction Approach Conclusion Ongoing Work Summary Summary Summary SUMO: axiomatic representation of common sense knowledge but lack of simple encyclopedic facts YAGO methodology: add entities and statements about them from Wikipedia semi-automatic techniques, basic amount of manual work −→ formal ontology with around two million entities and several million statements and axioms SUMO is catapulted from an upper level ontology to a full-fledged all-purpose KB Open source, available online: http://www.demelo.org/yagosumo/ G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  64. 64. Introduction Approach Conclusion Ongoing Work Summary Summary Summary SUMO: axiomatic representation of common sense knowledge but lack of simple encyclopedic facts YAGO methodology: add entities and statements about them from Wikipedia semi-automatic techniques, basic amount of manual work −→ formal ontology with around two million entities and several million statements and axioms SUMO is catapulted from an upper level ontology to a full-fledged all-purpose KB Open source, available online: http://www.demelo.org/yagosumo/ G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology
  65. 65. Introduction Approach Conclusion Ongoing Work Summary Summary Summary SUMO: axiomatic representation of common sense knowledge but lack of simple encyclopedic facts YAGO methodology: add entities and statements about them from Wikipedia semi-automatic techniques, basic amount of manual work −→ formal ontology with around two million entities and several million statements and axioms SUMO is catapulted from an upper level ontology to a full-fledged all-purpose KB Open 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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