A Hitchhiker’s guide to OntologyYAGO ElvisPedia 64. Match classes, entities, & relations...
Transcript of A Hitchhiker’s guide to OntologyYAGO ElvisPedia 64. Match classes, entities, & relations...
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A Hitchhiker’s guideto Ontology
Fabian M. SuchanekTelecom ParisTech UniversityParis, France
me>13intro>19
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permanent
money freedom
2003:
2005:
2008:
Fabian M. Suchanek
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money
permanent
freedom
2003: BSc in Cognitive ScienceOsnabruck University/DE
2005: MSc in Computer ScienceSaarland University/DE
2008: PhD in Computer Science
Max Planck Institute/DE
Fabian M. Suchanek
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2003: BSc in Cognitive ScienceOsnabruck University/DE
2005: MSc in Computer ScienceSaarland University/DE
2008: PhD in Computer Science
Max Planck Institute/DE
money
permanent
freedom
Fabian M. Suchanek
44
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2009: PostDoc at Microsoft ResearchSilicon Valley/US
permanent
money freedom
Fabian M. Suchanek
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2009: PostDoc at Microsoft ResearchSilicon Valley/US
permanent
money freedom
Fabian M. Suchanek
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2009: PostDoc at Microsoft ResearchSilicon Valley/US
2010: PostDocINRIA Saclay/FR
money
permanent
freedom
Fabian M. Suchanek
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2009: PostDoc at Microsoft ResearchSilicon Valley/US
2010: PostDocINRIA Saclay/FR
money
permanent
freedom
Fabian M. Suchanek
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2009: PostDoc at Microsoft ResearchSilicon Valley/US
2010: PostDocINRIA Saclay/FR
2012: Research group leader
Max Planck Institute/DE
permanent
money freedom
Fabian M. Suchanek
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2009: PostDoc at Microsoft ResearchSilicon Valley/US
2010: PostDocINRIA Saclay/FR
2012: Research group leader
Max Planck Institute/DE
permanent
money freedom
Fabian M. Suchanek
1010
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2009: PostDoc at Microsoft ResearchSilicon Valley/US
2010: PostDocINRIA Saclay/FR
2012: Research group leader
Max Planck Institute/DE2013: Associate Professor
Telecom ParisTech/FRmoney
permanent
freedom
Fabian M. Suchanek
1111
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2009: PostDoc at Microsoft ResearchSilicon Valley/US
2010: PostDocINRIA Saclay/FR
2012: Research group leader
Max Planck Institute/DE2013: Associate Professor
Telecom ParisTech/FRmoney
permanent
freedom
Fabian M. Suchanek
1212
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2009: PostDoc at Microsoft ResearchSilicon Valley/US
2010: PostDocINRIA Saclay/FR
2012: Research group leader
Max Planck Institute/DE2013: Associate Professor
Telecom ParisTech/FR2016: Full Professor
Telecom ParisTech/FR
money
permanent
freedom
Fabian M. Suchanek
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2009: PostDoc at Microsoft ResearchSilicon Valley/US
2010: PostDocINRIA Saclay/FR
2012: Research group leader
Max Planck Institute/DE2013: Associate Professor
Telecom ParisTech/FR2016: Full Professor
Telecom ParisTech/FR
money
permanent
freedom
Fabian M. Suchanek
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I am an Elvis Fan!
1961
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Old Elvis from Sachsmedia
At Elvis’ ranchin New Zealand
Ranch is Murchison Lodge in New Zealand 17
Meeting Elvis Presley
1961
2015
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Knowledge Cards
Elvis Presley
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Knowledge Cards
Elvis Presley
???
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For us, a knowledge base (“KB”, “ontology”) is a graph, wherethe nodes are entities and the edges are relations.
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Knowledge Bases
type
born 1935
Singer
Person
subclassOf
(We do not distinguish T-Box and A-Box.)
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Miningknowledge
bases->
DeterminingIncompleteness->
A ∧B ⇒ C
Protectingknowledge bases->
Creativly extendingknowledge bases ->
Constructingknowledge bases
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Singer
type
born
Knowledge Base Life Cycle
1935
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Elvis Presley was one
of the best blah blahblub blah don’t readthis, listen to thespeaker! blah blah blahblubl blah you are stillreading this! blah blah
blah blah blabbel blah
Born: 1935In: Tupelo...
Categories:Rock&Roll, American Singers,Academy Award winners...
Extracting from Wikipedia
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Elvis Presley
ElvisPresley
AmericanSinger
1935 Tupelo
USAAcAward
type
bornIn
locatedInwon
born
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See it online
Intermediate extractors• clean facts• deduplicate facts and entities• check consistency
Extractor
GeoNames Extractor
Extractor
Extractor
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Creating a large knowledge base
ensuring high quality (95%)
Extractor
Extractor
ExtractorWordNet
See it locally23
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Intermediate extractors• clean facts• deduplicate facts and entities• check consistency
See it online See it locally
multilingual>29
Extractor
GeoNames
WordNet
Extractor
Extractor
Extractor
24
Creating a large knowledge base
ensuring high quality (95%)
Extractor
Extractor
Extractor
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Extractor
25
Integrating multilingual Wikis
was born
e nato
est ne
Extractor
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Extractor
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was born
e nato
est ne
map?
Extractor
Integrating multilingual Wikis
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Extractor
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was born
e nato
est ne
?Extractor
Extractor
Integrating multilingual Wikis
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<Elvis,USA><Mao,China>
<Elvis,USA><Mao,China>
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bornIn
est ne
Integrating multilingual Wikis
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<Elvis,USA><Mao,China>
<Elvis,USA><Mao,China>
est ne= bornIn
P (S1 ⊇ S2) > θ
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bornIn
est ne
[CIDR 2015]
?
Integrating multilingual Wikis
29
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<Elvis,USA><Mao,China>
<Elvis,USA><Mao,China>
est ne= bornIn
P (S1 ⊇ S2) > θ
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bornIn
est ne
[CIDR 2015]
?
Integrating multilingual Wikis
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Example: YAGO about Elvis
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Wikipedia + WordNettime and space10 languages
100 relations100m facts10m entities95% accuracyused by DBpedia
and IBM Watson
http://yago-knowledge.org
Caveat:focus onprecision!
canonic&IBEX>42 32
YAGO: a large knowledge base
[WWW2007,JWS2008,WWW2011 demo,AIJ2013,WWW2013 demo,CIDR2015]
canonic>37
soon open-source!
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ElvisPresley
Mr.Presley
Mr.Obama
ElvisHurricane
Canonicalizing New Entities
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Elvis?
??
??
?
?
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Use hierarchical agglomerative clustering
• TF-IDF token overlap• Triple overlap• String Similarity
•Word overlap in source docs• Type overlap• Overlap of co-mentions
ElvisHurricane
ElvisPresley
Mr.Presley
Mr.Obama
details>36
Canonicalizing New Entities
34
Elvis
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• TF-IDF token overlap• Triple overlap• String Similarity
•Word overlap in source docs• Type overlap• Overlap of co-mentions
MachineLearning
Use hierarchical agglomerative clustering
ElvisHurricane
ElvisPresley
Mr.Presley
Mr.Obama
Canonicalizing New Entities
35
Elvis
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• TF-IDF token overlap• Triple overlap• String Similarity
•Word overlap in source docs• Type overlap• Overlap of co-mentions
MachineLearning
MachineLearning
Use hierarchical agglomerative clustering
ElvisHurricane
ElvisPresley
Mr.Presley
Mr.Obama
Canonicalizing New Entities
36
Elvis
36
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• TF-IDF token overlap• Triple overlap• String Similarity
•Word overlap in source docs• Type overlap• Overlap of co-mentions
MachineLearning
MachineLearning
Use hierarchical agglomerative clustering
ElvisHurricane
ElvisPresley
Mr.Presley
Mr.Obama
Canonicalizing New Entities
37
Elvis
[CIKM 2014]
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ElvisHurricane
ElvisPresley
Mr.Presley
Mr.Obama
IBEX>42
Canonicalizing New Entities
38
Elvis
[CIKM 2014]
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Goal: Harvest entities from the Web
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c© Whittleseacitylac
Unique identifiers can be verified by a checksum.They exist for products, books, documents, chemicals,...
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IBEX: Collect unique ids
887128476661
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c© Whittleseacitylac 41
IBEX: Collect unique ids
id name
1234 Puma PowerTech Blaze1234 Please choose
1234 Puma Shoe1234 Puma PowerTech Blaze
5678Sony Cybershot TS1005678Please choose
... ...
URLu1u1u2u2u3u3...
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Found• 13m email addresses
with their name• 235K chemical products
• 1.4m books• 1.1m products... with an accuracy of 73%-96%
Analyzed
• Global trade flow• frequent email providers• frequent people names and more
All data available online athttp://resources.mpi-inf.mpg.de/d5/ibex/
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IBEX: analyses
[WebDB 2015]
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Miningknowledge
bases->
DeterminingIncompleteness->
A ∧B ⇒ C
Protectingknowledge bases->
Creativly extendingknowledge bases ->
Constructingknowledge bases
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Singer
type
born
Knowledge Base Life Cycle
1935
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married(x, y) ∧ hasChild(x, z)⇒ hasChild(y, z)
PCA>49
Rule Mining finds patterns
married
hasChild hasChild
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But: Rule mining needs counter examplesand RDF ontologies are positive only
married(x, y) ∧ hasChild(x, z)⇒ hasChild(y, z)
PCA>49
Rule Mining finds patterns
45
hasChild hasChild
married
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Assumption:If we know r(x,y1),..., r(x,yn),then all other r(x,z) are false.
Partial Completeness Assumption
46
hasChild
46
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Assumption:If we know r(x,y1),..., r(x,yn),then all other r(x,z) are false.
Partial Completeness Assumption
47
hasChild
47
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Assumption:If we know r(x,y1),..., r(x,yn),then all other r(x,z) are false.
conf>49
Partial Completeness Assumption
48
hasChild
48
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~B ⇒ r(x, y)
married(z, x) ∧ hasChild(z, y)⇒ hasChild(x, y)
conf( ~B ⇒ r(x, y)) = #x,y: ~B∧r(x,y)#x,y:∃y′: ~B∧r(x,y′)
# of parent-child
relations that wepredict correctly
in the KB
# of parent-child
relations that wepredict and where
a child is known
Partial Completeness Assumption
4949
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Caveat: rules cannotpredict the unknownwith high precision
AMIE is based on an efficient in-memory databaseimplementation.
r(x, y) ∧ r′(z, y)⇒ r”(x, z)
AMIE finds rules in ontologies
AMIE
(5min)
5050
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type(x, pope)⇒diedIn(x,Rome)
AMIE finds rules in ontologies
51
[WWW 2013, VLDB journal 2015]
AMIE
(5min)
>Rosa
51
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Matching heterogeneous KBs
52
USA
bornInCountryUSA
TupelobornInCity locatedIn
52
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1. Coalesce the KBs
53
USAbornInCountry
bornInCityTupelo
locatedIn
53
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bornInCity(x, y) ∧ locatedIn(y, z)⇒ bornInCountry(x, z)
Caveat:Spurious
correlations
2. Mine rules
54
USAbornInCountry
AMIE
“ROSA rule”
TupelobornInCity locatedIn
54
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⇒ bornInCountry(x, z)bornInCity(x, y) ∧ locatedIn(y, z)
ROSA rules match ontologies
55
[AKBC 2013]
“ROSA rule”
55
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Miningknowledge
bases->
DeterminingIncompleteness->
A ∧B ⇒ C
Protectingknowledge bases->
Creativly extendingknowledge bases ->
Constructingknowledge bases
56
Singer
type
born
Knowledge Base Life Cycle
1935
56
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Incompleteness
59
marriedTo
c© Girl Happy
?
marriedTo
Given a subject s anda relation r, do we knowall o with r(s, o) ?
59
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Signals for Incompleteness
60
marriedTo
?
marriedTo
Closed World AssumptionPartial Completeness AssumptionPopularity oracle
AMIE oracle: Learn rules such asmoreThan1(x, hasParent)⇒ complete(x, hasParent)
No-change oracleStar-pattern oracle
Class-oracle
>Details
60
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Signals for Incompleteness (F1)
61* = biased training sample61
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Signals for Incompleteness
62
marriedTo
marriedTo
AMIE can predict incompleteness
• bornIn: 100% F1-measure• diedIn: 96%• directed: 100%• graduatedFrom: 87%
• hasChild: 78%• isMarriedTo: 46%... and more.
[WSDM 2017]
>paris
62
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If incomplete, use another KB
63
birthDate
1935
type
RockSinger
married
1935-01-08
born
Singer
type
plays
YAGO ElvisPedia
63
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Wo is the spouseof the guitar
player?
No Links => No Use
64
birthDate
1935
type
RockSinger
married
1935-01-08
born
Singer
type
plays
YAGO ElvisPedia
64
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Match classes, entities, & relations
subClassOf
sameAs
subPropertyOf
65
birthDate
1935
type
RockSinger
married
1935-01-08
born
Singer
type
plays
YAGO ElvisPedia >details
65
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Match classes, entities, & relations
”Elvis” ”Elvis”
name label
6666
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1. Match literals
Match classes, entities, & relations
”Elvis”
name label
”Elvis”
6767
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1. Match literals
Match classes, entities, & relations
”Elvis”
name label
”Elvis”
6868
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2. Assume small equivalence of all relations
Match classes, entities, & relations
”Elvis”
name label
”Elvis”
6969
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2. Assume small equivalence of all relations
Match classes, entities, & relations
”Elvis”
name label
”Elvis”
7070
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What about matching the entities?
What does it mean that both Elvises share the same name?
Match classes, entities, & relations
”Elvis”
name label
”Elvis”
71>fun
71
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ifun(r, y) = 1#x:r(x,y)
ifun(r) = HMyifun(r, y)
Inverse Functionality
”Elvis”
name
1935
born
ifun(name, Elvis)= 1 / 2ifun(born, 1935)= 1/10
ifun(name)= 0.9ifun(born)= 0.1
72>fun
72
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ifun(r) = #x:∃y:r(x,y)#x,y:r(x,y)
# of subjectsdivided by
# of factsifun(r) = HMyifun(r, y)
Inverse Functionality
”Elvis”
name
1935
born
7373
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3. If subjects share a relation that is highlyinverse functional, and the object is matched,then match the subjects.
Match classes, entities, & relations
”Elvis”
name label
”Elvis”
7474
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4. If relations share many pairs,
increase their match
Match classes, entities, & relations
”Elvis”
name label
”Elvis”
7575
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4. If relations share many pairs,
increase their match
Match classes, entities, & relations
”Elvis”
name label
”Elvis”
7676
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5. Iterate
Caveat: Convergence proof only in subcase
P (r1 ⊆ r2) = 42φγ∑...P (e1 = e2)...
P (e1 ≡ e2) =∏1
42αβ...P (r1 ⊆ r2)...
Match classes, entities, & relations
”Elvis”
name label
”Elvis”
7777
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6. Compute class subsumption
P (c1 ⊆ c2) = arcsin(4.1125)× P (e1 ≡ e2)× ...
Match classes, entities, & relations
singer AmericanSinger
type type
7878
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Caveat: Class alignmentworks with “only” 85%precision due tospurious correlation.
6. Compute class subsumption
Match classes, entities, & relations
AmericanSingersinger
type type
7979
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PARIS matches DBpedia & YAGO
• in 2 hours• with 90% accuracy
PARIS:match entities,classes,relations
80
[VLDB 2012, APWeb 2014 invited paper]
80
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Miningknowledge
bases->
DeterminingIncompleteness->
A ∧B ⇒ C
Protectingknowledge bases->
Creativly extendingknowledge bases ->
Constructingknowledge bases
81
Singer
type
born
Knowledge Base Life Cycle
1935
81
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People may “steal” from other ontologies without giving due credit.Most ontologies have licenses that require attribution.
stolen
Plagiarism
YAGO
cc
82
EvilPedia
82
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By adding a few fake facts to the source ontology,one can prove theft in the target ontology.
Additive Watermarking
[WWW 2012 demo]83
stolen
YAGOEvilPedia
83
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One can also prove theft by selectivelyremoving facts from the source ontology.
details>76
details&DIVINA>99
Subtractive Watermarking
[ISWC 2011]84
YAGO
stolen
EvilPedia
84
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Subtractive Watermarking
85
YAGO
85
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ratio of overlap
ratio of covered holes
≈
An Innocent KB
86
YAGOElvisPedia
86
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high overlap
no holes covered
but
Probability that thishappens by chance
overlap ratio
number of holes
α :
n :
∼ (1− α)n
DIVINA>99
A Stolen KB
87
YAGO
Dieb-i-pedia
(Argument by Mauro Sozio; original paper uses chi square test)87
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VectorPortalc©Ethan Hill 88
Protecting the user
Mat Honan
CC by-sa88
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VectorPortalc©Ethan Hill 89
Protecting the user
Mat Honan
CC by-sa89
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VectorPortalc©Ethan Hill
resetpasswordby credit card
90
Protecting the user
Mat Honan
CC by-sa90
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VectorPortalc©Ethan Hill
resetpasswordby credit card
resetpasswordby backup email
91
Protecting the user
Mat Honan
CC by-sa91
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VectorPortalc©Ethan Hill
resetpasswordby credit card
resetpasswordby backup email
resetpasswordby backup email
92
Protecting the user
Mat Honan
CC by-sa Mat’s story92
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VectorPortalc©Ethan Hill
resetpasswordby credit card
resetpasswordby backup email
resetpasswordby backup email
Owen’s story
Two-factorauthentication
93
Protecting the user
Mat Honan
CC by-sa Mat’s story93
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CC by-sa VectorPortalc©Ethan Hill
resetpasswordby credit card
resetpasswordby backup email
resetpasswordby backup email
Two-factorauthentication
Security: How many keys does a hacker need?Safety: How many keys can I afford to lose?Protection: How many keys to destroy my data?
94
Protecting the user
Mat Honan
Owen’s storyMat’s story94
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gmail-destructiongmail-access
gmail-2ndfactor
gmail-
trusteddevice
phone-
authen-tication
phone-app
phone-
SMS
mac-access
dell-access
phone-possession
phone-password
phone-
simPin
gmail-password
gmail-1stfactor
gmail-recovery
code
yahoo-
data
95
Account Dependenciesgmail-data
95
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https://suchanek.name/programs/divina96
DIVINADiscovering Vulnerabilities of Internet Accounts
[WWW 2015 demo]
96
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Miningknowledge
bases->
DeterminingIncompleteness->
A ∧B ⇒ C
Protectingknowledge bases->
Creativly extendingknowledge bases ->
Constructingknowledge bases
97
Singer
type
born
Knowledge Base Life Cycle
1935
97
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98
Combinatorial Creativity
c© BetterThanPants.com c© Vileda
+ ( - ) +
c© Avsar Arasc© Stone Mens Wear98
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99
Description Logics do not work
c© Vileda
+ ( - ) +
c© Avsar Arasc© Stone Mens Wear
BabyMop ≡Romper u ∃has.(Mop u ¬∃has.Stick) u ∃has.Baby≡ ⊥
Mop ≡ Tool u ∃has.Stick u ∃has.Strings
99
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100
Language for Combinatorial Creativity
c© Vileda
+ ( - ) +
c© Avsar Arasc© Stone Mens Wear
Romper + ∃has.(Mop − ∃has.Stick) + ∃has.Baby≡ BabyMop
Mop ≡ Tool u ∃has.Stick u ∃has.Strings
Mop − ∃has.> ≡ Tool u ∃StringsMop + ∃has.> ≡ Mop
Mop → ∃r.> ≡ Stick
Mop ↑ ∃has.> ≡ ∃has.Stick
Subtraction:Addition:Succession:Selection*:
100
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Language for Combinatorial CreativityMop ≡ Tool u ∃has.Stick u ∃has.Strings
[ISWC 2016paper & demo]
1) Descriptive experiments 2) Generative experiments1/3 nonsense, 1/3 exists,1/3 “imaginable”
Mop − ∃has.> ≡ Tool u ∃StringsMop + ∃has.> ≡ Mop
Mop → ∃r.> ≡ Stick
Mop ↑ ∃has.> ≡ ∃has.Stick
Subtraction:Addition:Succession:Selection*:
>LeMonde
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Another creative idea...
1944 2013
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Mining Le Monde
time place entity
1967 USA
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red: many foreign companies mentionedblue: few foreign companies mentioned
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Presence of foreign companies
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[AKBC 2013]
[VLDB 2014 vision]
Average age of people mentioned
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Miningknowledge
bases->
DeterminingIncompleteness->
A ∧B ⇒ C
Protectingknowledge bases->
Creativly extendingknowledge bases ->
Constructingknowledge bases
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Singer
type
born
Knowledge Base Life Cycle
1935
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Is Elvis dead?
1977died
Elvis
???
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100m statements95% precision-> 5m wrong statements
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Is Elvis dead?
1977died
Elvis
???
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Miningknowledge
bases->
DeterminingIncompleteness->
A ∧B ⇒ C
Protectingknowledge bases->
Creativly extendingknowledge bases ->
Constructingknowledge bases
109
Singer
type
born
Knowledge Base Life Cycle
1935
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