Post on 22-Nov-2014
description
X-SOMX-SOMA Flexible Ontology A Flexible Ontology
MapperMapperCarlo Curino, Giorgio Orsi, Letizia Tanca
{curino,orsi,tanca}@elet.polimi.it
Politecnico di MilanoDipartimento di Elettronica e Informazione
September 4th
SWAE 2007 (DEXA’07)Regensburg
SWAE 2007
MotivationsMotivationsPart of the Context-ADDICT Project (Context Aware Data Design Integration Customization and Tailoring).
Scenarios:Scenarios:• Ontology-based integration of heterogeneous data sources• Semantic Web applications• Knowledge Management
Tasks:Tasks: • Semantic (semi-)automatic ontology matching/mapping/aligning…• Semantic consistency checking
SWAE 2007
OutlineOutline
• Problem setting.
• X-SOM algorithm.
• From matchings to mappings: The debugging process.
• Experimental Results.
SWAE 2007
The ProblemThe Problem
AlignmentAlignment
Ontology AlignmentOntology 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..
SWAE 2007
The ProblemThe Problem
MatchinMatchingg
Ontology AlignmentOntology 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..
SWAE 2007
The ProblemThe Problem
MappingMapping
Ontology AlignmentOntology 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.
SWAE 2007
X-SOM’s mapping processX-SOM’s mapping process
Matching: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:Debugging: Matchings are tested for consistency and coherency to improve their quality. Conflicts are solved in a (semi-)automatic fashion.
Mapping:Mapping: An ontology containing the mappings between the constitutive components of the input ontologies.
SWAE 2007
X-SOM ArchitectureX-SOM ArchitectureThree Subsystems:Three Subsystems:
• Matching• Mapping• Inconsistency Resolution
SWAE 2007
Matching phase: ProductionMatching phase: Production• Features:
• Syntactic (Jaro, Levenshtein, …), structural and semantic (WordNet, Google, …) similarities.
• A module can use other modules results to have a starting point for its algorithm (e.g., structural ones).
• X-SOM matching modules are designed to exploit intrinsic parallelism of matching algorithms where possible.
• Where are the problems?• The optimal combination function is often non-linear: It
is approximated via machine learning.• Matching strategy definition: What modules are
suitable for a given mapping task?
SWAE 2007
Matching phase: CombinationMatching phase: Combination• X-SOM’s Neural Network:
• X-SOM combines the modules’ outputs using a three-layers feed-forward neural network.
• Training set built from data (benchmarks ontologies).• The Neural network increases performance up to 15%
in precision and 35% in recall if compared with simple average functions (LWM, QWM, sigmoid, etc.).
• Controversial points: • Is the learned function domain dependent?• How to build a good training set?
SWAE 2007
Controversial pointsControversial points• Domain Independence:
• Learned function robust to domain changes, but• It is not robust to different design techniques. The network learns the intrinsic reliability of the
matching algorithms (and their combinations).
• Training set: • The number of samples with positive and negative
outcomes must be balanced.• The techniques influence each others: selection of
almost independent techniques.
SWAE 2007
Matchings debuggingMatchings debugging• Semantic consistency checking: The process of verifying whether there are mappings that modify the semantics of the elements belonging to the original ontologies.
• Debugging process:
• Guarantees satisfiability while preserving the semantics of the original ontologies.• Makes use of heuristics and of an extended tableau algorithm for description logics to allow matching debugging and explanation.• Addresses multiple mappings.
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Semantic consistency: Semantic consistency: ExamplesExamples
• Bowties:
• Cycles:
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Semantic consistency: Semantic consistency: SolutionsSolutions
• Bowties:
• Cycles:
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Experimental Results: OAEI Experimental Results: OAEI 20072007
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1.20RecallMean (Recall)PrecisionMean (Precision)
OAEI-2007 Benchmarks Results
SWAE 2007
Experimental Results: OAEI Experimental Results: OAEI 20072007
Linear Sigmoidal Neural0
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Combination Functions Per-formance
recallPrecision
SWAE 2007
Conclusion and Future WorkConclusion and Future Work
• Summary:• 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.
• We tested its performance against the OAEI’07 benchmarks.
• Future Work:• Increase mappings expressiveness (Heterogeneity / GLAV).• New modules: e.g., pure structural matchers, instance and
instance-based matchers.• How can collaborative background knowledge improve mapping
algorithms?
SWAE 2007
Question timeQuestion time
Q & A(If I’m showing this slide, I haven’t run out of time)
SWAE 2007
SWAE 2007
Overall System ArchitectureOverall System Architecture
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Models viewModels view
SWAE 2007
Data TailoringData TailoringData Tailoring, based on the Data Tailoring, based on the Dimension Tree Dimension Tree InstantiationInstantiation::• Schema Tailoring• Instance Tailoring
SWAE 2007
Semantic ExtractionSemantic Extraction
Data Source Ontology:• Semantic Extraction: data abstract model + storage model• Supports the query processing• Models isolation (different models can be used separately)