yago-sumo: integrating yago into the suggested upper merged ontology

IntroductionApproach

Conclusion

Integrating YAGO into theSuggested Upper Merged Ontology

G. de Melo1, F. Suchanek1, A. Pease2

1: Max Planck Institute for Informatics, Germany2: Articulate Software, USA

2008-11-03

G. de Melo, F. Suchanek, A. Pease Integrating YAGO into theSuggested Upper Merged Ontology

Conclusion

Ontologies and KBsSUMOExtending OntologiesYAGO

Outline

1 IntroductionOntologies and KBsSUMOExtending OntologiesYAGO

2 ApproachIncorporationClass InformationStatements

3 ConclusionOngoing WorkSummary

Conclusion

Introduction

Ontologies/KBs: providebackground knowledge forintelligent applications

Schism:formal ontologies vs. large KBs

Goal: Large-scale formal ontology

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Introduction

formal ontologies: complex axioms(e.g. in FOL), but quite small

large-scale KBs (e.g. based onWikipedia): only simple facts

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Introduction

combine the best of both worlds!

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Introduction

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 conceptsfor applications requiring basic “common sense”

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Introduction

open source

origins: IEEE standard upper ontology groupcore owned by IEEE (basically Public Domain), portions GPLe.g.: OpenCyc doesn’t include axioms of commercial Cyc

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Introduction

open source

peer review, community of experts and usersformal verification with ATP systems

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Introduction

open source

OWL without additional rules is not very expressiveKIF variant standardized as ISO/IEC IS 24707:2007(Common Logic)

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Introduction: Why Axiomatic Ontologies?

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Introduction: Why Axiomatic Ontologies?

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Introduction

SUMO Example

(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.

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Introduction

Structure of SUMO

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Introduction

additional domain ontologies

however, SUMO is mainly an upper ontology

not enough instances and ground facts

e.g. for geography, finance, transportation

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Introduction

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Introduction

e.g. people, cities, books

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Introduction

Extending Ontologies: Possible Approaches

Manual work

Information extraction from corpora / the Web

Import from existing databases

slow process, low coverageSemantic Wikis not yet accepted enough

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Introduction

Manual work

low accuracynot canonical / in line with upper ontology

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Introduction

Manual work

feasible, but not universal enough

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Introduction

combine entities and facts from Wikipedia with an upperontology

original YAGO: WordNet for the upper level

New goal: integrate with SUMO

excellent coverage: around 2 million entitiesmillions of facts about themhigh quality: e.g. birth dates of people, location of cities

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Introduction

mainly a lexical knowledge basee.g. hyponymic relationships do not strictly imply subsumptionslack of formal axioms

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Introduction

so the class information actually is meaningful

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IncorporationClass InformationStatements

Outline

Conclusion

Incorporation

Idea: most Wikipedia articles become new entities

Semi-automatic matching: although SUMO contains onlyfew instances, some degree of overlap exists

use weighted string similarity measureadditional manual validation

−→ equivalence table

Entity 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 ...”)

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Incorporation

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Incorporation

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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 categoriesencoding class membership

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Class Information

categorization not transitivemembers of subcategories often unrelated to parent category

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Class Information

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Class Information

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Class Information

shallowparsing: noungroup parser toidentifyheadword

heuristic:ignorecategories withheadword insingular form

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Class Information

shallowparsing: noungroup parser toidentifyheadword

heuristic:ignorecategories withheadword insingular form

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Class Information

check WordNet for premodifier + headword or headword only

disambiguate using frequency information

result: relationship to WordNet-derived class

e.g. “American singer” or “singer”

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Class Information

Conclusion

Class Information

American singers of German origin

becomes linked as a subclass to theWordNet-derived class Person

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Class Information

Voting Procedure

problem:

regular polysemy, Wikipedia articles simultaneously coverseveral metonymically related sensese.g. Brown University is both a College and aGroupOfPeople

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 brokenarbitrarily)

Conclusion

Class Information

Voting Procedure

problem:

regular polysemy, Wikipedia articles simultaneously coverseveral metonymically related sensese.g. Brown University is both a College and aGroupOfPeople

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 brokenarbitrarily)

Conclusion

Class Information

From WordNet toSUMO

good news:

existing manuallyestablished WordNet-SUMO-mappings

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Class Information

From WordNet toSUMO

in some cases, thesemappings provide anequivalent SUMOclass

−→ directly use theSUMO class instead ofthe WordNet oneE.g. Human instead ofWordNet’s “person”

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Class Information

From WordNet toSUMO

in many cases, themappings provide asuper-class

−→ create newWordNet-based class,make it a subclass ofSUMO class

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Class Information

From WordNet to SUMO

in 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.

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Class Information

Evaluation

lots of heuristics, multiple steps

yet: accuracy of 92.67%± 2.98% (evaluation of most specificgenuine SUMO parents for new instances)

Conclusion

Class Information

Evaluation

lots of heuristics, multiple steps

yet: accuracy of 92.67%± 2.98% (evaluation of most specificgenuine SUMO parents for new instances)

Conclusion

Statements

Information Extraction

YAGO uses manual rules and heuristics to extract informationabout 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 thestatements are accurate

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Statements

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Statements

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Statements

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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)

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Statements

SUMO Integration

mapping rules

(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))))

Conclusion

Statements

SUMO Integration

mapping rules

(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))))

Conclusion

Statements

Statements with Literals

proper encoding of literals with units:e.g. (MeasureFn 3.0 SquareMeter)

date ranges are recast(exists ?YEARNO ?MONTHNO ?YEARNO

(birthdate HerveyDeStanton

(DayFn ?DAYNO

(MonthFn ?MONTHNO

(YearFn ?YEARNO))))

(greaterThanOrEqualTo ?YEARNO 1270)

(lessThanOrEqualTo ?YEARNO 1279)))

Conclusion

Statements

Statements with Literals

proper encoding of literals with units:e.g. (MeasureFn 3.0 SquareMeter)

date ranges are recast(exists ?YEARNO ?MONTHNO ?YEARNO

(birthdate HerveyDeStanton

(DayFn ?DAYNO

(MonthFn ?MONTHNO

(YearFn ?YEARNO))))

(greaterThanOrEqualTo ?YEARNO 1270)

(lessThanOrEqualTo ?YEARNO 1279)))

Conclusion

Statements

Additional Grounding

statements of the form(representsInLanguage

"Immanuel Kant" ImmanuelKant EnglishLanguage)

produce a greater level of formal grounding of the semanticsof term names

when names are ambiguous, providing such symbolic stringsfor multiple languages can further reduce the range of possibleinterpretations

classes are better-specified due to their extensionalcharacterization

Conclusion

Statements

Conclusion

Statements

Conclusion

Ongoing WorkSummary

Outline

Conclusion

Ongoing WorkSummary

Ongoing Work

TPTP transformation for reasoning

SUMO problems in CADE competitions

ATP systems for large-scale reasoning

Conclusion

Ongoing WorkSummary

Ongoing Work

Conclusion

Ongoing WorkSummary

Ongoing Work

Conclusion

Ongoing WorkSummary

Summary

SUMO: axiomatic representation of common sense knowledgebut lack of simple encyclopedic facts

YAGO methodology: add entities and statements about themfrom Wikipedia

semi-automatic techniques, basic amount of manual work

−→ formal ontology with around two million entities andseveral million statements and axioms

SUMO is catapulted from an upper level ontology to afull-fledged all-purpose KB

Open source, available online:http://www.demelo.org/yagosumo/

Conclusion

Ongoing WorkSummary

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Ongoing WorkSummary

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Ongoing WorkSummary

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Ongoing WorkSummary

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Ongoing WorkSummary

Summary

yago-sumo: integrating yago into the suggested upper merged ontology

Technology

upper ontologynot

owllarge formal ontology

largescale formal ontologyg

axiomatic ontologies

large kbsgoal

public domain

ground factse

ground factsg