1. 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
Good afternoon, I am Feng Shi, a master student from tsinghua University China. The title of my presentation is actively learning ontology matching via user interaction, It’s about a paper of my Co-operative work with
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2. Outline Motivation Problems Our Approach Experiments Conclusion
Match Selection
Correct Propagation
Experiments
Conclusion
This is the outline of my presentation, first I will introduce our motivation, and then describe the problems, and then introduce our approach, then experiments and conclusion
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3. Matching results of the anatomy real world case in OAEI 2009
Motivation
Matching results of the anatomy real world case in OAEI 2009
The results of ontology matching with complete automation is infeasible or undesirable in many real cases.
One promising solution is to involve user interactions into the matching process to improve the quality of matching results
As we know, Ontology matching plays a key role for s emantic interoperability. Many methods have been proposed for automatically finding the alignment between heterogeneous ontologies. However, in many real-world cases, finding the alignment in a completely automatic way is highly infeasible or undesirable. Lets see an example, this is a matching result of the anatomy real world case in OAEI this task has been started from 2005, but from the table we can see that the result is still not desirable. The best F1-measure of all the 3 tasks are around 80%. In this case, we think about whether using user interaction is helpful for ontology matching. However, regarding the large size of ontologies, random user interactions are really limited for helping ontology matching. So is there any way to minimize the amount of user interactions, while maximize the effect (accuracy improvement) of interactive efforts? We also hope the improvement through user interaction is increasing gradually.
The users can only give very limited feedback, so is there any way to minimize the amount of user interactions, while maximize the effect of interactive efforts?
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4. Agenda Motivation Problems Our Approach Experiments Conclusion
Match Selection
Correct Propagation
Experiments
Conclusion
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5. How to select the most informative candidate match to query?
How to improve the whole matching result with the user feedback?
Active learning is a very useful method in the area of machine learning. The basic idea of active learning is to try to select the most representative data as the training data from the dataset. In our work, active learning is employed to solve the problem. In similar, we hope to use active learning method to select the most informative candidate matches to query users so that we can get the greatest improvement. This is the active learning framework for ontology matching. We have 2 main inputs, one is two ontologies to be matched and the other can be any automatical ontology matching method, or the combination of them. First it selects the most informative candidate mach automatically, and then query users for confirmation. Last to improve the whole matching result with the user feedback. Then it repeat this process. In the framework, the key process is an iteration process. In the process the two questions we should answer are:
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清华大学知识工程研究室

6. Agenda Motivation Problems Our Approach Experiments Conclusion
Match Selection
Correct Propagation
Experiments
Conclusion
And next we will introduce
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7. Match Selection Confidence Similar Distance Contention Point If and
We propose 3 measures to find the matches which are possible to be wrongly matched. If we can correct the wrong matches, then the matching result will be improved. And Let me give brief description to them.
If and
Contention Point
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8. Motivation
This 3 measures can help to find the wrong matches, But the other objective is to select the matches which have influence to other related matches. For example
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9. Example of the similarity propagation graph
To solve the problem, we employ the basic idea of similarity propagation graph
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10. Agenda Motivation Core Problems Our Approaches Experiment Results
Match Selection
Correct Propagation
Experiment Results
Conclusion
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11. if k=2 then n=9
The red node, we use of the number of the influenced related match nodes as a measurement to select candidate match
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12. Correct Propagation If the candidate match is confirmed by users
If the candidate match is unmatched
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13. 5/6/2019
清华大学知识工程研究室

14. Agenda Motivation Problems Our Approach Experiments Conclusion
Match Selection
Correct Propagation
Experiments
Conclusion
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15. Experiments Data sets Baseline Matching Result Evaluation Metrics
OAEI 2005 Benchmark Directory
OAEI 2008 Benchmark
OAEI 2009 A-R-S Instance Matching Benchmark
Baseline Matching Result
Result of RiMOM
Evaluation Metrics
Precision
Recall
F1-Measure

16. Experiment Design Exp 1: The effect of the 3 measures
Confidence
Similarity Distance
Contention Point
Exp 2: The effect of the weight for the number of influenced matches
Exp 3: The effect of propagation
We conduct 3 exps to illustrate the effectiveness of our approach, In exp 1 we want to show

17. Exp 1: OAEI 2008 benchmark 302.
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18. Exp 2: OAEI 2009 A-R-S Benchmark
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19. Exp 3: OAEI 2005 Directory.
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20. Agenda Motivation Core Problems Our Approaches Experiment Results
Match Selection
Correct Propagation
Experiment Results
Conclusion
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21. 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

22. Thanks!
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