Dexa2007 Orsi V1.5

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  • Ricerca di sorgenti
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  • Dexa2007 Orsi V1.5

    1. 1. X-SOM A Flexible Ontology Mapper Carlo Curino, Giorgio Orsi , Letizia Tanca {curino,orsi,tanca}@elet.polimi.it Politecnico di Milano Dipartimento di Elettronica e Informazione September 4 th SWAE 2007 (DEXA’07)‏ Regensburg
    2. 2. Motivations <ul><li>Part of the Context-ADDICT Project (Context Aware Data Design Integration Customization and Tailoring). </li></ul><ul><li>Scenarios: </li></ul><ul><ul><li>Ontology-based integration of heterogeneous data sources </li></ul></ul><ul><ul><li>Semantic Web applications </li></ul></ul><ul><ul><li>Knowledge Management </li></ul></ul><ul><li>Tasks: </li></ul><ul><ul><li>Semantic (semi-)automatic ontology matching/mapping/aligning… </li></ul></ul><ul><ul><li>Semantic consistency checking </li></ul></ul>
    3. 3. Outline <ul><li>Problem setting. </li></ul><ul><li>X-SOM algorithm. </li></ul><ul><li>From matchings to mappings: The debugging process. </li></ul><ul><li>Experimental Results. </li></ul>
    4. 4. The Problem Alignment Ontology Alignment : The process of bringing two or more ontologies into mutual agreement , by relating their constitutive elements by means of alignment relationships , and making them coherent and consistent. .
    5. 5. The Problem Matching Ontology Alignment : The process of bringing two or more ontologies into mutual agreement , by relating their constitutive elements by means of alignment relationships , and making them coherent and consistent. .
    6. 6. The Problem Mapping Ontology Alignment : The process of bringing two or more ontologies into mutual agreement , by relating their constitutive elements by means of alignment relationships , and making them coherent and consistent.
    7. 7. X-SOM’s mapping process Matching: Similarities between ontologies computed with a customizable set of matching algorithms (strategy). The results are combined by means of a feed-forward neural network. Debugging: Matchings are tested for consistency and coherency to improve their quality. Conflicts are solved in a (semi-)automatic fashion. Mapping: An ontology containing the mappings between the constitutive components of the input ontologies.
    8. 8. X-SOM Architecture <ul><li>Three Subsystems: </li></ul><ul><ul><li>Matching </li></ul></ul><ul><ul><li>Mapping </li></ul></ul><ul><ul><li>Inconsistency Resolution </li></ul></ul>
    9. 9. Matching phase: Production <ul><li>Features: </li></ul><ul><ul><li>Syntactic (Jaro, Levenshtein, …), structural and semantic (WordNet, Google, …) similarities. </li></ul></ul><ul><ul><li>A module can use other modules results to have a starting point for its algorithm (e.g., structural ones). </li></ul></ul><ul><ul><li>X-SOM matching modules are designed to exploit intrinsic parallelism of matching algorithms where possible. </li></ul></ul><ul><li>Where are the problems? </li></ul><ul><ul><li>The optimal combination function is often non-linear: It is approximated via machine learning . </li></ul></ul><ul><ul><li>Matching strategy definition: What modules are suitable for a given mapping task? </li></ul></ul>
    10. 10. Matching phase: Combination <ul><li>X-SOM’s Neural Network: </li></ul><ul><ul><li>X-SOM combines the modules’ outputs using a three-layers feed-forward neural network. </li></ul></ul><ul><ul><li>Training set built from data (benchmarks ontologies). </li></ul></ul><ul><ul><li>The Neural network increases performance up to 15% in precision and 35% in recall if compared with simple average functions (LWM, QWM, sigmoid, etc.). </li></ul></ul><ul><li>Controversial points: </li></ul><ul><ul><li>Is the learned function domain dependent? </li></ul></ul><ul><ul><li>How to build a good training set? </li></ul></ul>
    11. 11. Controversial points <ul><li>Domain Independence: </li></ul><ul><ul><li>Learned function robust to domain changes, but </li></ul></ul><ul><ul><li>It is not robust to different design techniques. </li></ul></ul><ul><ul><li>The network learns the intrinsic reliability of the matching algorithms (and their combinations). </li></ul></ul><ul><li>Training set: </li></ul><ul><ul><li>The number of samples with positive and negative outcomes must be balanced. </li></ul></ul><ul><ul><li>The techniques influence each others: selection of almost independent techniques. </li></ul></ul>
    12. 12. Matchings debugging <ul><li>Semantic consistency checking : </li></ul><ul><li>The process of verifying whether there are mappings that modify the semantics of the elements belonging to the original ontologies. </li></ul><ul><li>Debugging process: </li></ul><ul><ul><li>Guarantees satisfiability while preserving the semantics of the original ontologies. </li></ul></ul><ul><ul><li>Makes use of heuristics and of an extended tableau algorithm for description logics to allow matching debugging and explanation. </li></ul></ul><ul><ul><li>Addresses multiple mappings . </li></ul></ul>
    13. 13. Semantic consistency: Examples <ul><li>Bowties: </li></ul><ul><li>Cycles: </li></ul>
    14. 14. Semantic consistency: Solutions <ul><li>Bowties: </li></ul><ul><li>Cycles: </li></ul>
    15. 15. Experimental Results: OAEI 2007
    16. 16. Experimental Results: OAEI 2007
    17. 17. Conclusion and Future Work <ul><li>Summary : </li></ul><ul><ul><li>We presented an extensible ontology mapper that combines several matching algorithms by means of a neural network and uses a debugging process to improve the quality of ontology mappings as well as guarantee the consistency of the mapping. </li></ul></ul><ul><ul><li>We tested its performance against the OAEI’07 benchmarks. </li></ul></ul><ul><li>Future Work: </li></ul><ul><ul><li>Increase mappings expressiveness (Heterogeneity / GLAV). </li></ul></ul><ul><ul><li>New modules: e.g., pure structural matchers, instance and instance-based matchers. </li></ul></ul><ul><ul><li>How can collaborative background knowledge improve mapping algorithms? </li></ul></ul>
    18. 18. Question time <ul><li>Q & A </li></ul><ul><li>(If I’m showing this slide, I haven’t run out of time)‏ </li></ul>
    19. 20. Overall System Architecture
    20. 21. Models view
    21. 22. Data Tailoring <ul><li>Data Tailoring, based on the Dimension Tree Instantiation : </li></ul><ul><li>Schema Tailoring </li></ul><ul><li>Instance Tailoring </li></ul>
    22. 23. Semantic Extraction <ul><li>Data Source Ontology: </li></ul><ul><li>Semantic Extraction: data abstract model + storage model </li></ul><ul><li>Supports the query processing </li></ul><ul><li>Models isolation (different models can be used separately)‏ </li></ul>

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