Large Scale Integration of Sensesfor the Semantic Web

Jorge Gracia, Mathieu d’Aquin, Eduardo Mena

Computer Science and Systems Engineering Department (DIIS)University of Zaragoza, Spain

Knowledge Media Institute (KMi)Open University, United Kingdom

Jorge Gracia, Mathieu d’Aquin, Eduardo Mena

Computer Science and Systems Engineering Department (DIIS)University of Zaragoza, Spain

Knowledge Media Institute (KMi)Open University, United Kingdom

18th International World Wide Web Conference

Madrid, Spain, 20th-24th April 2009

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Outline

IntroductionMethodOptimization studyExperimentsConclusions

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Introduction

Current Semantic WebFavoured by the increasing amount of online ontologies already available on the WebHampered by the high heterogeneity that this growing semantic content introduces

The redundancy problemExcess of different semantic descriptions, coming from different sources, to describe the same intended meaning

Our proposalA method to cluster the ontology terms that one can find on the Semantic Web, according to the meaning that they intend to represent

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Introduction

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Introduction

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Redundancy problem: many representations of the same meanings

?Watson

apple

Introduction

The Semantic Web

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Proposed solution: pool of cross-ontology integrated senses

“clustered” Watson

apple

Introduction

The Semantic Web

The FruitThe Tree

The Company

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Introduction

Watson

The Semantic Web

Multiontology Semantic Disambiguator

Ontology Evolution

Semantic Browsing

Scarlet Ontology Matching Folksonomy Enrichment

QueryGen Semantic Query Generation

Question Answering

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Ontology terms

Synonym expansion

integration

Sense clustering

Keyword maps

Synonym maps

Senses

(each synonym map)

Watson

Similarity > threshold?

more ont. terms?

yes yesno

no

Extraction

Similarity Computation

risethreshold?

Integration

Senses Clustering

Disintegration

yesnoModify

integration degree

CIDER

Modifyintegration?

yes

Method

OFF-LINE

RUN-TIME

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Keyword maps: ontology terms with identical label

Watson

Method

apple

apple

apple

apple

apple

apple

apple

apple

apple

apple

apple

apple

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Synonym maps: ontology terms with synonym labels

apple

apple

apple

apple

apple

apple

apple

apple

apple

apple

apple

apple

apple tree

Apple Inc.

Apple Inc.

apple tree

manzana

Watson

Method

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Method

Agglomerative clustering

CIDER

a

b

c

d

ad

a’

b

c

ad

a’’

b

c

. . .

ee

e

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Sense maps: semantically equivalent terms grouped

apple

apple

Apple Inc.

apple tree

manzana

apple

apple

apple

Apple Inc.

apple

apple

apple

The Fruit The Tree

The Company

apple tree

apple

apple

apple

apple

CIDER

Method

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Falling threshold(Integration)

Rising threshold(Disintegration)

Optimalthreshold

Method

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Integration level varies with similarity threshold

Optimization study

Integration Level = 1 - # finalSenses / # initialOntologyTerms

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Which similarity threshold is the best one?Three exploration ways:

Experimenting with ontology matching benchmarks Obtained 0.13 lower bound for optimal threshold

Contrasting with human opinion Range of good values between 0.2 and 0.3

Optimizing time response. Because: It will reduce the response time of the overall system Compatible with the other two ways It is not always feasible to have a large enough number of humans to ask or reference alignments

Optimization study

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Response time varies with thresholdOptimal value around 0.22

Optimization study

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Scalability study9156 keywords, 73169 different ontology terms to be clustered, Processing time is linear with number of ontology terms

Experiments

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Scalability studyProcessing time is independent of ontology size

Experiments

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Illustrative exampleKeyword = turkeySynonym map = turkey, Türkei, TürkiyeNº ontology terms = 58Nº Integrated senses = 9 (threshold = 0.27)

Experiments

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Experiments

More examples (threshold = 0.19)

Keyword #initial terms

#final senses

appalachian 7 1

apple 39 7

free 51 2

mace 7 3

plant 52 18

poll 5 4

stein 5 1

turkey 58 8

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Experiments

Positive factsTerms from different versions of the same ontology are easily detectedVery different meanings are not wrongly integrated (e.g., “plant” as “living organism” with “plant” as “industrial buildings”)

Negative factsHard to obtain a total integration of the same meanings (caused by very different semantic descriptions)

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ConclusionsRedundancy of semantic descriptions on the Web can be significantly reducedOur integration technique scales when used on a large body of knowledgeThe proposed method is flexible enough to configure and adapt our integration level to the necessities of client applications

Future workMore advanced prototypeMore extensive human-based evaluationStudy and evaluation of impact on other systems

Conclusions

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END of presentation

Thank you!