1 / 13

An Adaptive Machine Learning Framework with User Interaction for Ontology Matching

This paper presents an adaptive machine learning framework for ontology matching (OM) that incorporates user interaction, aimed at reducing manual annotation costs. By leveraging semi-supervised learning strategies, the framework propagates user-provided labels through unlabeled data, enhancing prediction confidence. Experimental results demonstrate significant performance improvements compared to traditional matching systems, highlighting the potential of combining machine learning with user feedback. The study uses varied similarity measures and employs datasets from the OAEI 2008 campaign to validate its effectiveness.

yul
Télécharger la présentation

An Adaptive Machine Learning Framework with User Interaction for Ontology Matching

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. An Adaptive Machine Learning Framework with User Interaction for Ontology Matching Hoai-Viet To1, Ryutaro Ichise2, and Hoai-Bac Le1 1 Ho Chi Minh University of Science, Vietnam 2 National Institute of Informatics, Japan

  2. Ontology Matching (OM) Problem • Ontology is a hierarchical structure used to organize concepts. • Ontology plays an important role in semantic web development. • Ontology matching finds correspondences between concepts from two ontologies. • Ontology matching is an important process when we want to integrate heterogeneous information source in new semantic web environment.

  3. Machine Learning Framework for OM • We introduced a machine learning framework for ontology matching problem in [Ichise, 2008] • Our hypothesis: the use of semi-supervised learning method will reduce the manual annotation cost. Cb1 Ca1 Ca2 Ca3 Cb2 Cb3 • Pre-alignment • Correct mapping: • Ca1 Cb1 • Ca2 Cb1 … • Incorrect mapping • Ca1 Cb2 • Ca1 Cb3…

  4. Semi-supervised Learning with User Interaction • Basic idea: propagate label through unlabeled data • Problem: few samples of labeled data  low confidence prediction. ? Red ? Blue User Interaction

  5. Adaptive Machine Learning Framework • Use multiple learning strategies + user interaction Ontology Storage Ontology Parser Similarity Calculator Initialize User Interaction Pre-alignment labeling training training learner labeling learner labeling

  6. Adaptive Machine Learning Framework • Similarity measures are based on those used in machine learning framework proposed in [Ichise, 2008], which: • include 24 string-based similarity measures • calculate similarity between: concept feature, concept structure feature, and concept hierarchical feature. • Our system: Machine Learning Framework for Ontology Matching with User Interaction (MalfomUI)

  7. Experiments • Purpose: • Compare the performance of our learning framework with other matching systems. • General setting: • Dataset from directory track of OAEI 2008’s campaign. [Caracciolo et. al., 2008] • The dataset is constructed from three internet directories: Yahoo, Google, Looksmart. • Simple equivalent relation. • The dataset includes 4487 labeled matching tasks, in which there are 2160 positive samples and 2327 negative samples. • Base learner: Naïve Bayes

  8. Experiments • Pre-Experiment – Supervised Learning method: • Used as baseline to compare with semi-supervised learning method. • Study the effect of training-set size on the performance of the supervised learning method.

  9. Experimental Results • MalUI-5 to MalUI-4000: Training set size

  10. Experiments • Experiment – Semi-supervised learning with user interaction • Study the performance of semi-supervised learning method with user interaction. • User annotate 20 samples at initialize phase and then label 4 samples more in 2 feedback round.

  11. Experimental Results • MalUI-RF: Comparison with other matching systems [Caracciolo et. al., 2008]

  12. Experimental Results • Semi-supervised learning with user feedback can reduce the cost of manual annotation. * In MalfomUI-RF experiment, users need to label 28 samples in total.

  13. Conclusion • Conclusions: • Our adaptive machine learning framework is effective: it requires less annotation cost but gains approximately good performance. • Machine learning approaches with user interaction are promising for ontology matching systems. • Future works: • Integrate more similarity measures to cover real datasets. • Consider more complicate semi-supervised models.

More Related