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Consensus building workshop

Conference track

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Outline

• Introduction (ideas behind the track)

• Evaluation

• Discussion – interesting mappings

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Conference track - Features

• Broadly understandable domain Conference Organisation

• Free exploration by participants within 10 ontologies

• No a priori reference alignment

• Participants: 6 research groups

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Conference track - Dataset

OWL, tool Protege

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Conference track - Participants

• 6 participants– Automs– Coma++– OWL-CtxMatch– Falcon– HMatch– RiMOM

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Conference track - Goals

• Focus on interesting mappings and unclear mappings– Why should they be mapped?

• Arguments: against and for

– Which systems did discover them?– Differences in similarity measures

• Underlying techniques?

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Outline…

• Introduction (ideas behind the track)

• Evaluation

• Discussion – interesting mappings

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Evaluation

• Processing all mappings by hand

• Assessment based on personal judgement of organisers (consistency problem)

• Tags: TP, FP, interesting, ?, heterogenous mapping

• Types of errors and phenomena: – subsumption, inverse property, siblings,

lexical confusion

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Evaluation…

• Subsumption mistaken for equivalence– Author,Paper_Author– Conference_Trip, Conference_part

• Inverse property– has_author,authorOf

• Siblings mistaken for equivalence– ProgramCommittee,Technical_commitee

• Lexical confusion error– program,Program_chair

• Relation – Class mapping– has_abstract,Abstract– Topic,coversTopic; read_paper,Paper

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Evaluation…

• Some statistics as a side-effect of processing

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Evaluation…

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Evaluation…

• Distribution of similarity measures – for True Positive Mappings and – for False Positive Mappings

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Evaluation…Coma++

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0-20% 20-40% 40-60% 60-80% 80-100%

TP

FP

Coma++

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50

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500

0-20% 20-40% 40-60% 60-80% 80-100%

TP

FP

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Evaluation…

Falcon

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400

0-20% 20-40% 40-60% 60-80% 80-100%

TP

FP

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Evaluation…

HMatch

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100

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400

0-20% 20-40% 40-60% 60-80% 80-100%

TP

FP

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Evaluation…RiMOM

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100

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0-20% 20-40% 40-60% 60-80% 80-100%

TP

FP

RiMOM

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100

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0-20% 20-40% 40-60% 60-80% 80-100%

TP

FP

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Record it!

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Outline…

• Introduction (ideas behind the track)

• Evaluation

• Discussion – interesting mappings

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Discussion

• Focus on interesting mappings and unclear mappings– Why should they be mapped?

• Arguments: against and for

– Which systems discover them?– Differences in similarity measures

• Underlying techniques?

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Mapping 1

Element1 Element2 Relation

Person Confious:human =

Notes semantically same

System

Automs Coma++ OWL-CtxMatch

Falcon HMatch RiMOM

1.0 Iasted

1.0 Ekaw

No No No 0.7 confOf

0.63 Ekaw

0.81 Sigkdd

0.77 sofsem

1.0 PCS

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Mapping 1confiousIasted

confOf

ekaw

sigkdd

sofsem

PCS

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Mapping 2

Element1 Element2 Relation

OpenConf:Surname confious:last_name =

Notes Both are datatype properties, the former with People as domain, the latter with human as domain

System

Automs Coma++ OWL-CtxMatch

Falcon HMatch RiMOM

1.0 No No No 1.0 No

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Mapping 3

Element1 Element2 Relation

sofsem:has_the_last_name confious:last_name =

Notes Both are datatype properties, the former with Person as domain, the latter with human as domain

System

Automs Coma++ OWL-CtxMatch

Falcon HMatch RiMOM

No 0.63 No No 0.8 No

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Mapping 4

Element1 Element2 Relation

ekaw: PC_Member confOf:Member_PC =

Notes Change order of incompound names

System

Automs Coma++ OWL-CtxMatch

Falcon HMatch RiMOM

1.0 No No No No 0.53

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Mapping 4

confOf

ekaw

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Mapping 5

Element1 Element2 Relation

ekaw:Document confious:article =

Notes Semantically same?

System

Automs Coma++ OWL-CtxMatch

Falcon HMatch RiMOM

1.0 No No No No 1.0

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Mapping 5

confious

ekaw

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Mapping 8

Element1 Element2 Relation

cmt:Rejection OpenConf:Reject =

Notes Both relates to process of assessment.

But the former is a recommendation, the latter is a decision. So…

System

Automs Coma++ OWL-CtxMatch

Falcon HMatch RiMOM

No 0.29 No 0.94 No No

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Mapping 8

cmt

OpenConf

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Mapping 11

Element1 Element2 Relation

ekaw:Location Place =

Notes Semantically same? Both are at the highest level of hierarchy. But Location maybe more general than Place… what about City?

System

Automs Coma++ OWL-CtxMatch

Falcon HMatch RiMOM

No No No No iasted 0.8

sigkdd 0.8No

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Mapping 11ekaw

iasted

Asserted conditions for iasted:Place

Location is domain of properties: locationOfLocation is range of properties: heldIn

iasted:Place is domain of properties: is_equipped_bysigkdd:Place is range of properties: can_stay_in

sigkdd

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Mapping 12

Element1 Element2 Relation

sofsem:reviews ekaw:hasReview =

Notes DomainOf(hasReview)=Paper,rangeOf(hasReview)=Review

DomainOf(reviews)=Review,rangeOf(reviews)=Reviewed_contribution

Inverse property phenomena, useful?

System

Automs Coma++ OWL-CtxMatch

Falcon HMatch RiMOM

No No 1.0 No No No

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• Call for contribution to our dataset

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Thank you for your participation!