pattern based knowledge base enrichment · buhmann, lehmann 2013/10/25 pattern based knowledge base...

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AKSW Research Group

Pattern Based Knowledge Base Enrichment

Lorenz Buhmann, Jens Lehmann

Agile Knowledge Engineering and Semantic Web (AKSW)University of Leipzig

25th October 2013

Buhmann, Lehmann 2013/10/25 Pattern Based Knowledge Base Enrichment

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Outline

1 Motivation

2 Approach

3 Experiments

4 Conclusion


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Table of Contents

1 Motivation

2 Approach

3 Experiments

4 Conclusion


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rise in the availability and usage of knowledge bases

still a lack of knowledge bases that consist of sophisticated schemainformation and instance data adhering to this schema

e.g. in the life sciences several knowledge bases

only consist of schema informationto a large extent, a collection of facts without a clearstructure(e.g. information extracted from databases)

combination of sophisticated schema and instance data would allowpowerful reasoning, consistency checking, and improved querying

→ create schemata based on existing data


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Example

Given knowledge base with

property birthPlace

subjects in triples of this property, e.g. Brad Pitt, Angela Merkel,Albert Einstein

Suggestions: birthPlace may be functional and has the domainPerson, ...

O b j e c t P r o p e r t y : b i r t h P l a c eC h a r a c t e r i s t i c s : F u n c t i o n a lDomain : PersonRange : P l a c eSubPropertyOf : hasBeenAt


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Advantages of more complex schemas

additional implicit information can be inferred

axioms serve as documentation for the purpose and correct usage ofschema elements

dbo:author for booksdbo:writer for film scripts

improve the application of schema debugging techniques

next slides


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Each person was only born at one place?!


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birthPlace birthPlace

6=

birthPlace is functional

SELECT ? s WHERE {? s dbo : b i r t h P l a c e ?o1 .? s dbo : b i r t h P l a c e ?o2 .FILTER (? o1 != ?o2 )}

}


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Table of Contents

1 Motivation

2 Approach

3 Experiments

4 Conclusion


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Basic Approach

We provide a

light-weight method for the

semi-automatic enrichment of

SPARQL knowledge bases to

reduce the effort of creating and maintaining such schemainformation.


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3 Steps to get a schema

SPARQLEndpoint

Input: Entity URI, Axiom Type, Knowledge Base (SPARQL Endpoint)

3-Phase EnrichmentLearning Approach:


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1. obtain schema information

SPARQLEndpoint


Background Knowledge


(onl

y ex

ecu

ted

once

per

know

ledg

e ba

se)


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Reasoner

SPARQLEndpoint



BackgroundKnowledge+ Relevant Instance Data

(opt

ion

alin

voca

tion

)

2. obtain axiom type and entity specific data


(onl

y ex

ecu

ted

once

per

know

ledg

e ba

se)

(sam

ple

dat

aif

nece

ssar

y)


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Reasoner

SPARQLEndpoint

EnrichmentOntology




List of Axiom Suggestions+ Metadata

(opt

ion

alin

voca

tion

)


3. run machine learning algorithm


(onl

y ex

ecu

ted

once

per

know

ledg

e ba

se)

(sam

ple

dat

aif

nece

ssar

y)

Learner

DL-Learner


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Reasoner

SPARQLEndpoint

EnrichmentOntology





(opt

ion

alin

voca

tion

)


3. run machine learning algorithm


(onl

y ex

ecu

ted

once

per

know

ledg

e ba

se)

iterate over all axiom typesand schema entities for fullenrichment

(sam

ple

dat

aif

nece

ssar

y)

Learner

DL-Learner


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Starting Point

SPARQL endpoint: http://dbpedia.org/sparql

Entity URI: http://dbpedia.org/ontology/author

Axiom Type: Object Property Domain


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http://dbpedia.org/sparql

http://dbpedia.org/ontology/author

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Step 1 - Obtain Schema Information

CONSTRUCT WHERE {? sub r d f s : s u b C l a s s O f ? sup .

}ORDER BY DESC(? sub ) LIMIT 1000 OFFSET 1000

dbo : D i s e a s e r d f s : s u b C l a s s O f owl : Thing .dbo : Book r d f s : s u b C l a s s O f dbo : WrittenWork .dbo : WrittenWork r d f s : s u b C l a s s O f dbo : Work .dbo : Work r d f s : s u b C l a s s O f owl : Thing .dbo : P h i l o s o p h e r r d f s : s u b C l a s s O f dbo : Person .dbo : Person r d f s : s u b C l a s s O f dbo : Agent .dbo : Agent r d f s : s u b C l a s s O f owl : Thing .dbo : S p o r t r d f s : s u b C l a s s O f dbo : A c t i v i t y .dbo : A c t i v i t y r d f s : s u b C l a s s O f owl : Thing .dbo : F i s h r d f s : s u b C l a s s O f dbo : Animal .


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Step 2 - Obtain axiom type and entity specific data

CONSTRUCT WHERE {? i n d dbo : a u t h o r ?o .? i n d a ? t y p e .

}ORDER BY DESC(? i n d ) LIMIT 1000 OFFSET 2000

...d b p e d i a : The Adventures o f Tom Sawyer

dbo : a u t h o r d b p e d i a : Mark Twain ;r d f : t y p e dbo : Book .

d b p e d i a : T h e Z o m b i e S u r v i v a l G u i d edbo : a u t h o r d b p e d i a : Max Brooks ;r d f : t y p e dbo : WrittenWork .

d b p e d i a : Web Therapydbo : a u t h o r d b p e d i a : L i sa Kudrow ;r d f : t y p e dbo : Book .

...


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Step 2 - Obtain axiom type and entity specific data

CONSTRUCT WHERE {? i n d dbo : a u t h o r ?o .? i n d a ? t y p e .

}ORDER BY DESC(? i n d ) LIMIT 1000 OFFSET 2000

...d b p e d i a : The Adventures o f Tom Sawyer

dbo : a u t h o r d b p e d i a : Mark Twain ;r d f : t y p e dbo : Book .



...Buhmann, Lehmann 2013/10/25 Pattern Based Knowledge Base Enrichment

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Step 3 - Machine Learning

d b p e d i a : The Adventures o f Tom Sawyerdbo : a u t h o r d b p e d i a : Mark Twain ;r d f : t y p e dbo : Book .



Score(Domain(dbo:author, dbo:Book))= 23 ≈ 66.7%

Score(Domain(dbo:author, dbo:WrittenWork))= 13 ≈ 33.3%

dbo : Book r d f s : s u b C l a s s O f dbo : WrittenWork .

Score(Domain(dbo:author, dbo:WrittenWork))= 33 = 100%


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Step 3 - Machine Learning(2)

Problem:

support for axiom in KB not taken into account→ no difference between 3 out of 3 and 100 out of 100

Solution:

Average of 95% confidence interval (Wald method)

p′ = s+2m+4

s −#successm −#total

min(1, p′ + 1.96 ·√

p′·(1−p′)m+4

) max(0, p′ − 1.96 ·√

p′·(1−p′)m+4

)

“In 95% of the intervals the true value is between ... and...”

Score(Domain(dbo:author, dbo:Book))≈ 57.3%Score(Domain(dbo:author, dbo:WrittenWork))≈ 69.1%


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http://www.genomic-cds.org/ont/genomic-cds.owl

C l a s s : human with CYP2C19 star 26SubClassOf : human w i th gene t i c po l ymorph i smAnnota t i on s : r d f s : l a b e l ”human wi th CYP2C19 ∗26”SubClassOf :

has some rs11188072 C and has some rs11568732 T andhas some rs118203756 G and has some rs118203757 G andhas some rs118203759 C and has some rs12248560 C andhas some rs12571421 A and has some rs12769205 A andhas some rs17878459 G and has some rs17878649 G andhas some rs17879685 C and has some rs17879992 T andhas some rs17882687 A and has some rs17884712 G andhas some rs17884832 T and has some rs17885098 T andhas some rs17886522 A and has some rs28399504 A andhas some rs28399513 T and has some rs3758580 C andhas some rs3758581 G and has some rs41291556 T andhas some rs4244285 G and has some rs4417205 C andhas some rs4917623 T and has some rs4986893 G andhas some rs4986894 T and has some rs55640102 A andhas some rs55752064 T and has some rs56337013 C andhas some rs58973490 G and has some rs6413438 C andhas some rs7088784 A and has some rs72552267 G andhas some rs72558186 T and has some rs7902257 G andhas some rs7916649 G


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http://www.genomic-cds.org/ont/genomic-cds.owl

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GALEN Ontology

C l a s s : Ab s t r a c tCa v i t yEqu i va l en tTo :

BodyCav i ty and i sSpaceDe f i n edBy some( BodySt ruc tu re and hasTopology some

( Topology and ha sAb so l u t eS t a t e some su r f a c eHo l l ow ))

A ≡ B u ∃r .(C u ∃s.(D u ∃t.E ))


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Pattern Based Knowledgebase Enrichment


Reasoner

SPARQLEndpoint

EnrichmentOntology

Input: Entity URI, Pattern, Knowledge Base (SPARQL Endpoint)




(opt

ion

alin

voca

tion

)


3. compute confidence scores

(onl

y ex

ecu

ted

once

per

know

ledg

e ba

se)

batch mode:iterate overpatterns andentities

Learner

DL-Learner

Repositories

TONESOxford Library

BioPortal

Execution Phase

Preparation Phase

quer

y m

odes

: dire

ct,

sam

ple

d or

loca

l


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Reasoner

SPARQLEndpoint

EnrichmentOntology





(opt

ion

alin

voca

tion

)



(onl

y ex

ecu

ted

once

per

know

ledg

e ba

se)


Learner

DL-Learner

Repositories

TONESOxford Library

BioPortal

1. extract and normalise patterns

Normalised AxiomFrequency Database

Execution Phase

Preparation Phase

quer

y m

odes

: dire

ct,

sam

ple

d or

loca

l


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Reasoner

SPARQLEndpoint

EnrichmentOntology





(opt

ion

alin

voca

tion

)



(onl

y ex

ecu

ted

once

per

know

ledg

e ba

se)


Learner

DL-Learner

Repositories

TONESOxford Library

BioPortal

1. extract and normalise patterns

Normalised AxiomFrequency Database

2. pattern to query rewriting

SPARQL QueryPattern Library

Execution Phase

Preparation Phase

quer

y m

odes

: dire

ct,

sam

ple

d or

loca

l


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Axiom Normalisation

based on structural equivalence defined in OWL 2 specification

For subclass axioms

reordering of class expressions in sub- and superclass

replacement of entities from left to right

Ensures that

Father v Male u ∃hasChild .PersonCarnivore v ∃eat.Meat u Animal

result inA v B u ∃r .C


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Pattern Transformation

Class Expression Ci Graph Pattern p = τ(Ci ,?var)

A {?var a A.}¬C {?var ?p ?o . FILTER NOT EXISTS {τ(C , ?var)}}{a1, . . . , an} {?var ?p ?o . FILTER (?var IN (a1, . . . , an))}C1 u . . . u Cn {τ(C1, ?var) ∪ . . .∪ τ(Cn, ?var)}C1 t . . . t Cn {τ(C1, ?var)} UNION . . . UNION {τ(Cn, ?var)}∃ r .C {?var r ?s.} ∪ τ(C , ?s)∃ r .{a} {?var r a.}∃ r .SELF {?var r ?var.}

......


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Pattern Transformation - Example

A v B u ∃r .C

? x a <A>.? x a <B>.? x <r> ? s0 .? x a <C>.

A = Book

SELECT ?p ? c l s 0 ? c l s 1 (COUNT( DISTINCT ? x ) AS ? cn t ) {? x a <Book>.? x a ? c l s 0 .? x ?p ? s0 .? s0 a ? c l s 1 .

} GROUP BY ?p ? c l s 0 ? c l s 1 ORDER BY DESC(? cn t )


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Table of Contents

1 Motivation

2 Approach

3 Experiments

4 Conclusion


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Experimental Setup - Pattern Detection

3 Ontology Repositories:

#OntologiesTotal Error

TONES 219 12BioPortal 385 101Oxford 793 0

#AxiomsTotal Tbox RBox Abox

Avg Max Avg Max Avg Max Avg MaxTONES 14,299 1,235,392 8297 658,449 20 932 5981 1,156,468BioPortal 25,541 847,755 23,353 847,755 35 1339 2152 220,948Oxford 49,997 2,492,761 15,384 2,259,770 25 1365 34,587 2,452,737

processed ≈ 1400 ontologies


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Pattern Frequency Detection

Pattern Frequency #Ontologies TO

NE

S

Bio

Por

tal

Oxf

ord

1. A v B 10,174,991 1050 2 1 12. A v ∃ p.B 8,199,457 604 1 2 23. A v ∃ p.(∃ q.B) 509,963 24 34. A ≡ B u ∃ p.C 361,777 319 8 4 45. B v ¬ A 237,897 417 3 3 96. A ≡ B 104,508 151 13 34 77. A ≡ ∃ p.B 70,040 139 36 32 88. ∃ p.Thing v A 41,876 595 6 7 119. A v ∀ p.B 27,556 266 4 11 19

10. A ≡ B u ∃ p.C u ∃ q.D 24,277 196 11 13 1311. A ≡ B u C 16,597 78 5 20 2212. A v ∃ p.(B u ∃ q.C) 12,453 84 23 18 1513. A v ∃ p.{a} 11,816 65 12 22 2014. A ≡ B u ∃ p.(C u ∃ q.D) 10,430 60 39 21 1715. p ≡ q− 9943 433 17 19 23


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Fixpoint Analysis

How does the ranking of the most frequent axiom patterns change?


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Experimental Setup - Pattern Application

DBpedia 3.8 (http://dbpedia.org/sparql)

100 random classes with at least 5 instances

60s data retrieval

at most 100 pattern instantiations per pattern

3 non-author evaluators


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http://dbpedia.org/sparql

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Manual Evaluation Results

manual evaluation in %pattern sample size correct minor

issuesincorrect κFleiss’

A v ∃ p.B 50 88.0 0.7 11.3 24.8A v B 47 63.8 2.1 34.0 53.8A ≡ B 25 10.7 0.0 89.3 44.0A ≡ ∃ p.B 68 29.9 2.0 68.1 60.4A ≡ B u ∃ p.C 100 25.0 3.0 72.0 72.9A ≡ B u ∃ p.(C u ∃ q.D) 100 23.0 5.3 71.7 43.5A v ∃ p.(∃ q.B) 71 85.0 3.3 11.7 34.0A v ∃ p.(B u ∃ q.C) 100 87.0 0.3 12.7 -2.8A v ∃ p.{a} 15 71.1 0.0 28.9 45.9A ≡ B u C 42 14.3 7.1 78.6 46.7A ≡ B u ∃ p.C u ∃ q.D 100 37.0 2.7 59.7 75.0

718 48.2 2.7 49.0 66.1


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Threshold Analysis

How many of the pattern instantiations with an accuracy value in aparticular interval are correct using majority voting?


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Table of Contents

1 Motivation

2 Approach

3 Experiments

4 Conclusion


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Conclusion

We proposed an approach, which allows for

detecting frequent axiom usage patterns

converting them into SPARQL query patterns

learning complex schema axioms

The evaluation has shown that

it is feasible

but still should be used in a semi-automatic manner

Overall it results in

a freely available tool that can be used

suggest both complex TBox and RBox axioms on large knowledgebases accessible via SPARQL endpoints


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Future Work

more ontology repositories, e.g. LOD cloud

check for patterns containing more than one axiom

improve score computation

learn appropriate thresholds for each axiom type


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GeoKnow

Thank You!Questions?


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pattern based knowledge base enrichment · buhmann, lehmann 2013/10/25 pattern based knowledge base...

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