actively learning ontology matching via user interaction
DESCRIPTION
Actively Learning Ontology Matching via User Interaction. Feng Shi , Juanzi Li, Jie Tang, Guotong Xie and Hanyu Li Knowledge Engineering Group Department of Computer Science and Technology Tsinghua University IBM China Research Laboratory, October 27, 2009. Outline. Motivation Problems - PowerPoint PPT PresentationTRANSCRIPT
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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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