Actively Learning Ontology Matching via User Interaction

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Actively Learning Ontology Matching via User Interaction

Feng Shi, Juanzi Li, Jie Tang, Guotong Xie and Hanyu Li

Knowledge Engineering GroupDepartment of Computer Science and TechnologyTsinghua University

IBM China Research Laboratory,

October 27, 2009

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Outline

Motivation Problems Our Approach

Match SelectionCorrect Propagation

Experiments Conclusion

Motivation

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Matching results of the anatomy real world case in OAEI 2009

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Agenda

Motivation Problems Our Approach

Match SelectionCorrect Propagation

Experiments Conclusion

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How to select the most informative candidate match to query?

How to improve the whole matching result with the user feedback?

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Agenda

Motivation Problems Our Approach

Match SelectionCorrect Propagation

Experiments Conclusion

Match Selection

Confidence

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Similar Distance

Contention Point

[ ( , )] / , ( , )( ( , ))

[ ( , ) ] /(1 ), ( , )S D S D

S DS D S D

sim e e sim e eConfidence sim e e

sim e e sim e e

( , ) min{| ( , ) ( , ' ) |,| ( , ) ( ' , ) |}S D S D S D S D S DSD e e sim e e sim e e sim e e sim e e

{ ( , ),? | , , . ( , ) ( , )}S D i S D j S DCP e e U i j st R e e R e e

))},((min{},,,{ 21

DSiMMMsimi eesimConfidencewQ

ki

CPIf and 2 2( , )sim A B

1 1( , )sim A B

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Motivation

Example of the similarity propagation graph

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Agenda

Motivation Core Problems Our Approaches

Match SelectionCorrect Propagation

Experiment Results Conclusion

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if k=2 then n=9

Correct Propagation

If the candidate match is unmatched

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If the candidate match is confirmed by users

),( ii ba),( yx)),(),,(( ii bayxw

)),((),(1 iiii basimConfidencebaer

),( ii ba),( yx)),(),,(( ii bayxw

)),((1),( iiii basimConfidencebaer

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Agenda

Motivation Problems Our Approach

Match SelectionCorrect Propagation

Experiments Conclusion

Experiments

Data setsOAEI 2005 Benchmark DirectoryOAEI 2008 Benchmark 301-304OAEI 2009 A-R-S Instance Matching Benchmark

Baseline Matching ResultResult of RiMOM

Evaluation MetricsPrecisionRecallF1-Measure

Experiment Design

Exp 1: The effect of the 3 measuresConfidenceSimilarity DistanceContention Point

Exp 2: The effect of the weight for the number of influenced matches

Exp 3: The effect of propagation

Exp 1: OAEI 2008 benchmark 302.

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Exp 2: OAEI 2009 A-R-S Benchmark

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Exp 3: OAEI 2005 Directory.

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Agenda

Motivation Core Problems Our Approaches

Match SelectionCorrect Propagation

Experiment Results Conclusion

Conclusion

Propose an active learning framework for ontology matching.

Experiments show that our approach is effective Batch active learning for ontology matching Avoid Error feedback from users

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