Ontology mapping needs context & approximation Frank van Harmelen Vrije Universiteit Amsterdam.

Ontology mapping needs

context & approximation

Frank van HarmelenVrije Universiteit Amsterdam

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Or:

andmore like a social conversation

How to make ontology-mapping less like data-base integration

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Two obvious intuitions

Ontology mapping needs background knowledge

Ontology mapping needs approximation

The Semantic Web needs ontology mapping

Three

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Which Semantic Web?Version 1:

"Semantic Web as Web of Data" (TBL)

recipe:expose databases on the web, use RDF, integrate

meta-data from: expressing DB schema semantics

in machine interpretable waysenable integration and unexpected re-

use

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“Enrichment of the current Web”

recipe:Annotate, classify, index

meta-data from: automatically producing markup:

named-entity recognition, concept extraction, tagging, etc.

enable personalisation, search, browse,..

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“Semantic Web as Web of Data”

Version 2:“Enrichment of the current Web”

Different use-cases Different techniques Different users

data-oriented

user-oriented

7between source & user


“Semantic Web as Web of Data”

between sources

Version 2:“Enrichment of the current Web”

But both need ontologiesfor semantic agreement

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Ontology research is almost done..we know what they are

“consensual, formalised models of a domain”we know how to make and maintain

them (methods, tools, experience)

we know how to deploy them(search, personalisation, data-integration, …)

Main remaining open questions Automatic construction (learning) Automatic mapping (integration)

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Three obvious intuitions




Ph.D. student

young researcher

?=

?≈

AIO

post-doc

This work withZharko Aleksovski &

Michel Klein

Does context knowledge help mapping?

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The general idea

source target

background knowledge

anchoring anchoring

mapping

inference

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a realistic example Two Amsterdam hospitals (OLVG, AMC) Two Intensive Care Units, different vocab’s Want to compare quality of care OLVG-1400:

1400 terms in a flat list used in the first 24 hour of stay some implicit hierarchy e.g.6 types of Diabetes

Mellitus) some reduncy (spelling mistakes)

AMC: similar list, but from different hospital

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Context ontology usedDICE:

2500 concepts (5000 terms), 4500 links Formalised in DL five main categories:

•tractus (e.g. nervous_system, respiratory_system)

•aetiology (e.g. virus, poising)

•abnormality (e.g. fracture, tumor)

•action (e.g. biopsy, observation, removal)

•anatomic_location (e.g. lungs, skin)

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Baseline: Linguistic methods

Combine lexical analysis with hierarchical structure

313 suggested matches, around 70 % correct 209 suggested matches, around 90 % correct

High precision, low recall (“the easy cases”)

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Now use background knowledge

OLVG (1400, flat)

AMC (1400, flat)

DICE(2500 concepts,

4500 links)

anchoring anchoring

mapping

inference

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Example found with context knowledge (beyond lexical)

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Example 2

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Anchoring strengthAnchoring = substring + trivial

morphologyanchored on N aspects OLVG AMC

N=5N=4N=3N=2N=1

004

144401

2198711285208

total nr. of anchored termstotal nr. of anchorings

549129

8

39% 1404

5816

96%

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Experimental results Source & target =

flat lists of ±1400 ICU terms each Background = DICE (2300 concepts in DL) Manual Gold Standard (n=200)

Does more context knowledge help?

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Adding more context Only lexical DICE (2500 concepts) MeSH (22000 concepts) ICD-10 (11000 concepts)

Anchoring strength:DICE MeSH ICD10

4 aspects 0 8 0

3 aspects 0 89 0

2 aspects 135 201 0

1 aspect 413 694 80

total 548 992 80

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Results with multiple ontologies

Monotonic improvement Independent of order Linear increase of cost

Separate

Lexical ICD-10 DICE MeSH

RecallPrecision

64%95%

64%95%

76%94%

88%89%

Joint

0102030405060708090

100

Lexical ICD-10 DICE MeSH

does structured context knowledge help?

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Exploiting structure CRISP: 700 concepts, broader-than MeSH: 1475 concepts, broader-than FMA: 75.000 concepts, 160 relation-types

(we used: is-a & part-of)

CRISP (738)

MeSH (1475)

FMA (75.000)

anchoring anchoring

mapping

inference

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(S <a B) & (B < B’) & (B’ <a T) ! (S <i T)

a a

i

Using the structure or not ?

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(S <a B) & (B < B’) & (B’ <a T) ! (S <i T)

No use of structure Only stated is-a & part-of Transitive chains of is-a, and

transitive chains of part-of Transitive chains of is-a and part-of One chain of part-of before

one chain of is-a

Using the structure or not ?

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Examples

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Examples

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Matching results (CRISP to MeSH)

Recall = · ¸ total incr.

Exp.1:DirectExp.2:Indir. is-a + part-ofExp.3:Indir. separate closuresExp.4:Indir. mixed closuresExp.5:Indir. part-of before is-a

448

395

395

395

395

417516933151

1972

156405140

2222

8180

0

1021

1316

2730

4143

3167

-29%

167%306%210%

Precision = · ¸ total

correct

Exp.1:DirectExp.4:Indir. mixed closuresExp.5:Indir. part-of before is-a

171414

183937

35950

38112101

100%94%

100%

(Golden Standard n=30)

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young researcher

?≈ post-doc

This work withZharko Aleksovski

Risto Gligorov Warner ten Kate

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B

Approximating subsumptions(and hence mappings)

query: A v B ?

B = B1 u B2 u B3 A v B1, A v B2, A v

B3 ?

B1 B3

B2

A

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Approximating subsumptions

Use “Google distance” to decide which subproblems are reasonable to focus on

Google distance

wheref(x) is the number of Google hits for xf(x,y) is the number of Google hits for

the tuple of search items x and yM is the number of web pages indexed by

Google

)}(log),(min{loglog

),(log)}(log),(max{log),(

yfxfM

yxfyfxfyxNGD

≈ symmetric conditio

nal probability

of co-occurrence

≈ estimate of semantic distance

≈ estimate of “c

ontribution” to

B1 u B2 u B3

≈ symmetric conditio

nal probability

of co-occurrence

≈ estimate of semantic distance

≈ estimate of “c

ontribution” to

B1 u B2 u B3

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Google distance

animal

sheep cow

madcow

vegeterian

plant

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Google for sloppy matching

Algorithm for A v B (B=B1 u B2 u

B3)

determine NGD(B, Bi)=i, i=1,2,3

incrementally:

• increase sloppyness threshold • allow to ignore A v Bi with i ·

match if remaining A v Bj hold

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Properties of sloppy matchingWhen sloppyness threshold goes up,

set of matches grows monotonically=0: classical matching=1: trivial matching

Ideally: compute i such that: desirable matches

become true at low undesirable matches

become true only at high Use random selection of Bi as baseline

?

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CDNow (Amazon.com)

All Music Guide

MusicMoz

ArtistGigs

Artist Direct NetworkCD baby

YahooSize: 96 classesDepth: 2 levels

Size: 2410 classesDepth: 5 levels Size: 382 classes

Depth: 4 levels

Size: 222 classesDepth: 2 levels




Experiments in music domain

very sloppy terms good

7 16-05-2006

Experimentpr

ecis

ion

recall

classical

randomNGD

Manual Gold Standard, N=50, random pairs

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=0.5397

60 =0.5

wrapping up

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So that shared context & approximation

make ontology-mapping a bit more like a social conversation

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Future: Distributed/P2P setting

source target

background knowledge

anchoring anchoring

mapping

inference

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Vragen & discussie

[email protected]://www.cs.vu.nl/~frankh

Ontology mapping needs context & approximation Frank van Harmelen Vrije Universiteit Amsterdam.

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Transcript of Ontology mapping needs context & approximation Frank van Harmelen Vrije Universiteit Amsterdam.