Yet Another Great Ontology

Yet Another Great Ontology
YAGO: Yet Another Great Ontology PhD Defense Fabian M. Suchanek (Max-Planck Institute for Informatics, Saarbrücken)‏ YAGO - A Core of Semantic Knowledge

YAGO - A Core of Semantic Knowledge
Overview Motivation: Why would anybody need Ontologies? Building a Core Ontology: YAGO Extending the Core Ontology: SOFIE YAGO - A Core of Semantic Knowledge

Santa Claus in Need World population YAGO - A Core of Semantic Knowledge

The Search for a Second Santa Claus strong, tall guy , australian Seeking strong, tall Australian man I'm 27, blue eyes, looking for a tall strong Australian man. girls-seek-guys.com/london/ Cached Similar pages YAGO - A Core of Semantic Knowledge

The Search for a Second Santa Claus strong person, > 1.90, Australian Seeking strong, tall Australian man I'm 27, blue eyes, looking for a tall strong Australian man. ... I'm 190 kg girls-seek-guys.com/london/ Cached Similar pages YAGO - A Core of Semantic Knowledge

The Search for a Second Santa Claus Hi Larry, it's me, Santa Claus. I think you misunderstood wh Seeking strong, tall Australian man I'm 27, blue eyes, looking for a tall strong Australian man. girls-seek-guys.com/london/ Cached Similar pages YAGO - A Core of Semantic Knowledge

Solution: An Ontology physical entity is a person is a is a continent is a isFrom height Australia 1.90m YAGO - A Core of Semantic Knowledge

Solution: An Ontology physical entity is a Classes person is a Relations is a continent is a isFrom Individuals Australia YAGO - A Core of Semantic Knowledge

Vision Gathering the knowledge of this world in a structured ontology. ر Semantic Search ر Question answering ر Machine Translation ر Document classification ر … The world, I‘d like to say, even though some may contradict, is not as it seems. It rather seems as if the world seems not what it seems YAGO - A Core of Semantic Knowledge

Plan of Attack Motivation  Building a Core Ontology: YAGO Extending the Core Ontology: SOFIE The world, I‘d like to say, even though some may contradict, is not as it seems. It rather seems as if the world seems not what it seems YAGO - A Core of Semantic Knowledge

YAGO: Goal Goal: Build a Large Ontology Previous Approaches: ر Assemble the ontology manually (WordNet, SUMO, Cyc, GeneOntology)‏ Problem: Usually low coverage (MPI is in none of these)‏ ر Use community work (Semantic Wikipedia, Freebase)‏ Problem: We don't know yet whether it takes off YAGO - A Core of Semantic Knowledge 11

YAGO: Goal Goal: Build a Large Ontology Our Approach: ر Extract knowledge from Wikipedia and WordNet (securing high coverage) ر Use extensive quality control techniques (securing high consistency) YAGO - A Core of Semantic Knowledge 12

YAGO: Infoboxes Claus K bornIn Sydney blah blah blub (don't read this! Better listen to the talk!) laber fasel suelz. Insbesondere, blub, texte zu, und so weiter blah blah blub Elvis laber fasel suelz. Blub, aber blah! Insbesondere, blub, texte zu, und so weiter blah blah blub Elvis laber fasel suelz. Insbesondere, blub, texte zu, und so weiter Exploit infoboxes Born in: Sydney ... YAGO - A Core of Semantic Knowledge 13

YAGO: Categories Claus K born bornIn Sydney blah blah blub (don't read this! Better listen to the talk!) laber fasel suelz. Insbesondere, blub, texte zu, und so weiter blah blah blub Elvis laber fasel suelz. Blub, aber blah! Insbesondere, blub, texte zu, und so weiter blah blah blub Elvis laber fasel suelz. Insbesondere, blub, texte zu, und so weiter 1980 Exploit infoboxes Exploit relational categories Categories: 1980_births YAGO - A Core of Semantic Knowledge 14

YAGO: Categories Australian Boxer Claus K isA born bornIn Sydney blah blah blub (don't read this! Better listen to the talk!) laber fasel suelz. Insbesondere, blub, texte zu, und so weiter blah blah blub Elvis laber fasel suelz. Blub, aber blah! Insbesondere, blub, texte zu, und so weiter blah blah blub Elvis laber fasel suelz. Insbesondere, blub, texte zu, und so weiter 1980 Exploit infoboxes Exploit relational categories Categories: Exploit conceptual categories Australian Boxers YAGO - A Core of Semantic Knowledge 15

YAGO: Categories Kick boxing Australian Boxer Claus K isA isA born bornIn Sydney blah blah blub (don't read this! Better listen to the talk!) laber fasel suelz. Insbesondere, blub, texte zu, und so weiter blah blah blub Elvis laber fasel suelz. Blub, aber blah! Insbesondere, blub, texte zu, und so weiter blah blah blub Elvis laber fasel suelz. Insbesondere, blub, texte zu, und so weiter 1980 Exploit infoboxes Exploit relational categories Categories: Exploit conceptual categories Kick boxing Avoid thematic categories YAGO - A Core of Semantic Knowledge 16

YAGO: Upper Model entity ? person Australian boxer is a born 1980 YAGO - A Core of Semantic Knowledge 17

YAGO: Upper Model Business Social_group ? People_by_occupation Australian boxer is a born 1980 YAGO - A Core of Semantic Knowledge 18

YAGO: Upper Model Person subclass WordNet Boxer subclass Australian boxer is a Wikipedia born 1980 [Suchanek et al.: WWW 2007] YAGO - A Core of Semantic Knowledge 19

YAGO: Quality Control 1. Canonicalization of entities Santa Klaus Santa Clause Santa Claus Santa YAGO - A Core of Semantic Knowledge 20

YAGO: Quality Control 1. Canonicalization of entities YAGO - A Core of Semantic Knowledge 21

YAGO: Quality Control 1. Canonicalization of entities of facts born 1980 born YAGO - A Core of Semantic Knowledge 22

YAGO: Quality Control 1. Canonicalization of entities of facts 2. Type Checks 1. Reductive Type Checking range(bornOnDate, timepoint)‏ bornOnDate(Claus_Kent, Sydney)‏ YAGO - A Core of Semantic Knowledge 23

YAGO: Quality Control Entity 1. Canonicalization of entities of facts 2. Type Checks 1. Reductive Type Checking 2. Type Coherence Checking Person Artifact Boxer, Swimmer, Flight instructor, Airplane YAGO - A Core of Semantic Knowledge 24

YAGO: Quality Control 1. Canonicalization of entities of facts 2. Type Checks 1. Reductive Type Checking 2. Type Coherence Checking Every fact and every entity occurs exactly once Every fact fulfills its type constraints [Suchanek et al.: JWS 2008] YAGO - A Core of Semantic Knowledge 25

YAGO: Numbers bornIn, actedIn, hasInflation,... Relations: 100
Entities: 2 million Facts: million Accuracy: 95% One of the largest public free ontologies Unprecedented quality among automatedly constructed ontologies YAGO - A Core of Semantic Knowledge 26

YAGO: Model boxer #1 (ClausKent,is_a,boxer)‏ #2 (#1, since, 1990)‏ #3 (#1, source, Wikipedia)‏ since 1990 is a source Wikipedia YAGO - A Core of Semantic Knowledge 27

YAGO: Model A YAGO ontology over a set of relations R a set of common entities C a set of fact identifiers I is a function I  (RCI)  R  (RIC)‏ #1 (ClausKent,is_a,boxer)‏ #2 (#1, since, 1990)‏ #3 (#1, source, Wikipedia)‏ We can talk about facts (#1, source, Wikipedia)‏ additional arguments (#1, since, 1990)‏ relations (since, hasRange, time_interval)‏ Still: Decideable Consistency YAGO - A Core of Semantic Knowledge 28

YAGO: Summary YAGO is an ontology that is ر large (combining Wikipedia and WordNet) ر accurate (using extensive quality control) ر computationally tractable (with a decideable consistency) YAGO - A Core of Semantic Knowledge

Plan of Attack Motivation  Building a Core Ontology: YAGO  Extending the Core Ontology: SOFIE YAGO The world, I‘d like to say, even though some may contradict, is not as it seems. It rather seems as if the world seems not what it seems YAGO - A Core of Semantic Knowledge

SOFIE: Goal Statement bornIn Saint Nicholas Patara Goal: Extending the ontology Saint Nicholas was born in Patara. YAGO - A Core of Semantic Knowledge 31

SOFIE: Goal Statement bornIn Saint Nicholas Patara Goal: Extending the ontology Saint Nicholas ce e poдuл в Patara. YAGO - A Core of Semantic Knowledge 32

SOFIE: Goal Statement Goal: Extending the ontology
bornIn Saint Nicholas Patara Goal: Extending the ontology recoverWithout(most_people, medication)‏ areUnder(0%, the_age_of_18)‏ support(these_findings, the_notion)‏ Saint Nicholas was born in Patara. Previous Approaches: ر Extract knowledge from corpora (e.g. the Web)‏ (Text2Onto, Espresso, Snowball, TextRunner)‏ Problems: Low accuracy, non-canonicity YAGO - A Core of Semantic Knowledge 33

SOFIE: Goal Statement bornIn Saint Nicholas Patara Goal: Extending the ontology Saint Nicholas was born in Patara. Our Approach (1): ر LEILA - Combining Linguistic and Statistical Analysis [Suchanek et al.: KDD 2006] Has high accuracy, but does not deliver canonicity YAGO - A Core of Semantic Knowledge 34

SOFIE: Goal Statement bornIn Saint Nicholas Patara Goal: Extending the ontology Saint Nicholas was born in Patara. Our Approach (2): ر SOFIE: Use logical reasoning to guarantee canonicity YAGO - A Core of Semantic Knowledge 35

SOFIE: Example YAGO ~ Worshipped People ~ bornInYear 1935 Saint Nicholas was born in the year 1417. Elvis Presley was born in the year 1935. "was born in the year" expresses bornInYear Pattern occurrence ~~> pattern meaning YAGO - A Core of Semantic Knowledge

SOFIE: Example YAGO ~ Worshipped People ~ bornInYear 1935 Saint Nicholas was born in the year 1417. Elvis Presley was born in the year 1935. "was born in the year" expresses bornInYear Pattern occurrence ~~> pattern meaning Pattern occurrence ~~> sentence meaning bornInYear 1417 YAGO - A Core of Semantic Knowledge

SOFIE: Example YAGO ~ Worshipped People ~ bornInYear 1935 Saint Nicholas was born in the year 1417. diedInYear Elvis Presley was born in the year 1935. 347 "was born in the year" expresses bornInYear Pattern occurrence ~~> pattern meaning Pattern occurrence ~~> sentence meaning bornInYear 1417 People should be born before they die. YAGO - A Core of Semantic Knowledge

SOFIE: Example YAGO Task 1: Find Patterns bornInYear 1935 Saint Nicholas was born in the year 1417. diedInYear Elvis Presley was born in the year 1935. 347 Task 2: Use semantic reasoning Task 3: Disambiguate entities Pattern occurrence ~~> pattern meaning Pattern occurrence ~~> sentence meaning bornInYear 1417 People should be born before they die. YAGO - A Core of Semantic Knowledge

SOFIE: It‘s all logical formulae! YAGO Task 1: Find Patterns bornInYear(ElvisPresley,1935) diedInYear(NicholasOfMyra,347) occurs("was born in the year", SaintNicholas,1417) occurs("was born in the year", ElvisPresley,1935) Task 2: Use semantic reasoning Task 3: Disambiguate entities occurs(P,X,Y) /\ expresses(P,R) => R(X,Y) means(SaintNicholas,NicholasOfMyra) 0.8 means(SaintNicholas,NicholasOfFüe) 0.2 refersTo(SaintNicholas,NicholasOfFüe) ? bornOnDate(NicholasOfFüe, 1417) ? bornInYear(X,B) /\ diedInYear(X,D) => B<D YAGO - A Core of Semantic Knowledge

SOFIE: Information Extraction as MAX SAT We have a Weighted MAX SAT Problem r(x,y) /\ s(x,z) => t(x,z) [w] ... Problem: ر The Weighted MAX SAT Problem is NP-hard ر Our instance contains YAGO (19 million facts) and textual facts (e.g. 10,000 facts) ر The best-known approximation algorithm cannot deal well with our specific instance YAGO - A Core of Semantic Knowledge

SOFIE: A Unifying Framework r(a,b) => s(x,y)‏ Task 1: Find Patterns Polynomial time Algorithm Functional MAX SAT FOR i=1 TO 42 ... NEXT i Task 2: Use semantic reasoning Approximation Guarantee Task 3: Disambiguate entities NicholasOfFlüe 1417 [Suchanek et al: TR 2009] YAGO - A Core of Semantic Knowledge

SOFIE: Experiments Corpus Type # Docs Relations Time Precision Wikipedia toy corpus structured 100 3 8min 100% Wikipedia subcorpus semi- structured 2000 15 15h 94% News article toy corpus unstructured 150 1 24min 91% Biographies from Web 3440 5 90% YAGO - A Core of Semantic Knowledge

SOFIE: Summary SOFIE unifies 3 tasks in a single framework: SOFIE delivers ر canonicalized facts ر of high precision Task 1: Find Patterns Task 2: Use semantic reasoning Task 3: Disambiguate entities YAGO - A Core of Semantic Knowledge

But back to the original question... Is there any Australian guy taller than 1.90m who could help me out? YAGO - A Core of Semantic Knowledge

Conclusion: Good News ر We made a great step towards gathering the knowledge of this world in a structured ontology YAGO The world, I‘d like to say, even though some may contradict, is not as it seems. It rather seems as if the world seems not what it seems SOFIE ر Christmas is safe! YAGO - A Core of Semantic Knowledge

References [Suchanek et al.: KDD 2006] Fabian M. Suchanek, Georgiana Ifrim and Gerhard Weikum "Combining Linguistic and Statistical Analysis to Extract Relations from Web Documents" Conference on Knowledge Discovery and Data Mining (KDD 2006)‏ [Suchanek et al.: WWW 2007] Fabian M. Suchanek, Gjergji Kasneci and Gerhard Weikum "YAGO - A Core of Semantic Knowledge" International World Wide Web conference (WWW 2007)‏ [Suchanek et al.: JWS 2008] Fabian M. Suchanek, Gjergji Kasneci and Gerhard Weikum "YAGO - A Large Ontology from Wikipedia and WordNet" Suchanek et al.: JWS Journal of Web Semantics 2008 [Suchanek et al.: TR 2009] Fabian M. Suchanek, Mauro Sozio, Gerhard Weikum „SOFIE – A Self-Organizing Framework for Information Extraction“ Submitted to the International World Wide Web conference (WWW 2009)‏ See Technical Report or my PhD Thesis on YAGO - A Core of Semantic Knowledge

Acronyms LEILA: Learning to Extract Information by Linguistic Analysis YAGO: Yet Another Great Ontology SOFIE: Self-Organizing Framework for Information Extraction NAGA: Not another Google Answer YAGO - A Core of Semantic Knowledge

YAGO: Thematic vs Conceptual Categories  conceptual: Australian boxers of German origin  thematic: Kick boxing in Australia Shallow linguistic noun phrase parsing: Premodifier Head Postmodifier Heuristics: If the head is a plural word, the category is conceptual YAGO - A Core of Semantic Knowledge 50

YAGO: Upper Model Person subclass WordNet Boxer 1 Boxer 42 .... Australian boxer is a Wikipedia born 1980 YAGO - A Core of Semantic Knowledge 51

A Hitchhiker's Guide to Ontology DBpedia (HU Berlin)‏ YAGO forms taxonomic backbone SUMO (research project)‏ YAGO and SUMO have been merged YAGO YAGO is part of the project by its Web service YAGO will be included Linking Open Data (HU Berlin, U Leipzig, OLS Inc.)‏ Freebase (community)‏ Planned YAGO is used for bootstrapping YAGO contributes the entities Cyc (commercial)‏ KOG (U Washington)‏ UMBEL (commercial)‏ [Suchanek et al.: JWS 2008] YAGO - A Core of Semantic Knowledge

YAGO: Applications NAGA (Semantic Search & Ranking)‏ TagBooster
[Kasneci et al: ICDE 2008] TagBooster (User Study on Social Tagging)‏ [Suchanek et al.: CIKM 2008] YAGO ESTER (Semantic Search + Full Text Search)‏ [Bast et al.: SIGIR 2007] Projects by other people YAGO - A Core of Semantic Knowledge

YAGO: Relations is a familyName givenName bornOnDate diedOnDate bornIn diedIn locatedIn establishedOnDate isMarriedTo hasPopulation hasHeight hasWeight hasInflation actedIn ... 100 relations YAGO - A Core of Semantic Knowledge

19,000,000 YAGO: Size* 3,000,000 30, , , ,000 KnowItAll SUMO WordNet OpenCyc Cyc Yago * Publicly available ontologies with a quality guarantee. Size is not correlated with usefulness. YAGO - A Core of Semantic Knowledge

YAGO: Model Axioms: (x, is_a, y)‏ (y, subclass, z)‏ => (x, is_a, z)‏ ... person subclass saint is a is a YAGO - A Core of Semantic Knowledge 56

YAGO: Model finite, unique f1, f2, f3, f4, f5, f6, f7, f8, f9, f10 Axioms: (x, is_a, y)‏ (y, subclass, z)‏ => (x, is_a, z)‏ ... derive facts f1, f2, f3, f4, f5 Eliminate facts f1, f2, f3 finite, unique [Suchanek et al.: WWW 2007] YAGO - A Core of Semantic Knowledge 57

YAGO: Knowledge Representation OWL Full RDFS YAGO ADTs Acyclicity Datatypes Reification subClassOf Transitivity Property Restrictions OWL DL YAGO - A Core of Semantic Knowledge

SOFIE rules! R(X,Y) /\ R(X,Z) /\ type(R,functionalRelation) => Y = Z occurs(P,WX,WY) /\ refersTo(WX.X) /\ refersTo(WY,Y) /\ R(X,Y) => expresses(P,R) occurs(P,WX,WY) /\ expressed(P,R) /\ refersTo(WX.X) /\ refersTo(WY,Y) /\ range(R,D1) /\ domain(R,D2) /\ type(X,D1) /\ type(Y,D2) => R(X,Y) disambiguationPrior(W,X) => refersTo(W,X)  R(X,Y) + relation-dependent rules: bornInYear(X,B) /\ diedInYear(X,D) => B<D YAGO - A Core of Semantic Knowledge

SOFIE: Clause transformation Rules r(X,Y) /\ s(X,Y) => t(X,X) u(a) Entities {a,b} Grounded Rules Clauses r(a,a) /\ s(a,a) => t(a,a) r(a,b) /\ s(a,b) => t(a,a) r(b,a) /\ s(b,a) => t(b,b) r(b,b) /\ s(b,b) => t(b,b) u(a)  r(a,a) \/  s(a,a) \/ t(a,a)  r(a,b) \/  s(a,b) \/ t(a,a)  r(b,a) \/  s(b,a) \/ t(b,b)  r(b,b) \/  s(b,b) \/ t(b,b) u(a) YAGO - A Core of Semantic Knowledge

SOFIE: Clause transformation Clauses 1 Textual Facts  r(a,a) \/  s(a,a) \/ t(a,a)  r(a,b) \/  s(a,b) \/ t(a,a)  r(b,a) \/  s(b,a) \/ t(b,b)  r(b,b) \/  s(b,b) \/ t(b,b) u(a) r(a,a) [w1] r(a,b) [w2] r(b,a) [w3] r(b,b) [w4] YAGO s(a,a) YAGO - A Core of Semantic Knowledge

SOFIE: Clause weighting Clauses Textual Facts  \/  \/ t(a,a) [w1]  \/  s(a,b) \/ t(a,a) [w2]  \/  s(b,a) \/ t(b,b) [w3]  \/  s(b,b) \/ t(b,b) [w4] u(a) [W] r(a,a) [w1] r(a,b) [w2] r(b,a) [w3] r(b,b) [w4] YAGO s(a,a) YAGO - A Core of Semantic Knowledge

SOFIE: Hypothesis generation Textual Facts Rules r(a,b) [w1] r(X,Y) /\ s(X,Y) => t(X,X) Hypotheses t(a,a) t(b,b) YAGO - A Core of Semantic Knowledge

SOFIE: Hypothesis generation Grounded Rules Rules r(a,a) /\ s(a,a) => t(a,a) r(a,b) /\ s(a,b) => t(a,a) r(X,Y) /\ s(X,Y) => t(X,X) Hypotheses t(a,a) YAGO - A Core of Semantic Knowledge

SOFIE: Functional MAX SAT Algorithm The functional MAX SAT Algorithm considers only unit clauses. Variables Clauses X Y Z =0 X \/ Z [w1] X \/ Y [w1] Y \/ Z [w1] Z [w1] =0 =1 YAGO - A Core of Semantic Knowledge

SOFIE: Experiments Corpus Type # Docs # Rel Time # Facts Precision Recall Wikipedia toy corpus structured 100 3 8min 165 100% 98% semi-structured 50% infoboxes removed 57% Wikipedia subcorpus semi-structured 2000 15 15h 505 94% ? News article toy corpus unstructured 150 1 24min 35, 46 91% 24%, 31% Snowball 65 56% 31% Biographies from Web 3440 5 744 90% YAGO - A Core of Semantic Knowledge

SOFIE: Large-Scale Experiment Corpus: 3700 biography documents downloaded from the Web Goal: Extract bornIn, bornOnDate, diedIn, diedOnDate, politicianOf Results: (precision in %) Runtime: (summed over 5 batches) Parsing 7:05h Hypothesis Generation 6:15h Solving 2:30h Total :50h  90 bornIn bornOnD diedIn diedOnD polOf YAGO - A Core of Semantic Knowledge

SOFIE: Relation to Markov Logic
Number of satisfied instances of the ith formula Weight of the ith formula r(x,y) /\ s(x,z) => t(x,z) [w] ... P(X) ~  e sat(i,X) wi max X  e sat(i,X) wi P max X log(  e sat(i,X) wi ) max X  sat(i,X) wi false true bornIn(Nicholas, Patras) ~~~~> Weighted MAX SAT problem YAGO - A Core of Semantic Knowledge

LEILA: Workflow Fix one relation, e.g. foundedInYear
The UDS was founded in 1948. The UDS has 1974 employees. The MPII has 1'000 employees. The MPI-SWS was founded in 2004. The MPI-SWS has 2'003 employees. Examples: UDS 1948 UDS 1949 UDS 1950 ... MPII 1988 MPII 1989 MPII 1990 X was founded in Y 10 2 X has Y employees 3 20 foundedIn(MPI-SWS, 2004)‏

LEILA: Theoretical considerations
THEOREM [Goodnaturedness]: As the number of parsed sentences increases, the probability of false extractions decreases. Intuition: One of two cases applies 1. A pattern occurs very frequently. Then it is unlikely to be mistaken for a good pattern 2. A pattern occurs very infrequently. Then it does not matter if it is mistaken for a good pattern. [Suchanek et al.: KDD 2006]

LEILA: The Linguistic Part
X was founded in Y The MPI-SWS was founded in 2004. foundedIn(MPI-SWS, 2004)

LEILA: The Linguistic Part
X was founded in Y The MPI-SWS, the great institution, was founded in 2004. foundedIn(MPI-SWS, 2004)

Future Work: With YAGO ر personalize (Shady, Maya)
ر use social networks to extend YAGO (Maya, Sharat, Ashwin) ر make YAGO multilingual (Gerard) ر add Web services (Nicoleta) ر make querying efficient (Gjergji) ر store YAGO efficiently (Thomas) ر make reasoning efficient (Mauro,Martin) ر provide good visualization (Shady) ر add a temporal component to SOFIE ر add biomedical knowledge (Alessandro Fiori) ر add multimodal support (Martin Schreiber) ر add natural language support (help from workshop on Monday) (slide by Prof. Gerhard Weikum)

Future Work: Beyond YAGO
ر join forces with other ontology projects ر learn not just facts, but also relations ر apply the SOFIE approach in related settings (information extraction with music or pictures?)

Acknowledgements Thank you for making these projects possible!
The following people have worked with me LEILA: Georgiana Ifrim and Gerhard Weikum YAGO: Gjergji Kasneci and Gerhard Weikum SOFIE: Mauro Sozio and Gerhard Weikum TagBooster: Milan Vojnovic and Dinan Gunawardena NAGA: Gjergji Kasneci, Georgiana Ifrim, Shady Elbassuoni and Gerhard Weikum ESTER: Holger Bast, Ingmar Weber and Alex Chitea YAGO+SUMO: Gerard de Melo and Adam Pease STAR: Gjergji, Mauro, Maya Ramanath and Gerhard Thank you for making these projects possible!

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