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

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OutlineOutline

• Problem setting.

• X-SOM algorithm.

• From matchings to mappings: The debugging process.

• Experimental Results.

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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..

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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..

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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.

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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.

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X-SOM ArchitectureX-SOM ArchitectureThree Subsystems:Three Subsystems:

• Matching• Mapping• Inconsistency Resolution

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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?

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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?

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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.

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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

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Experimental Results: OAEI Experimental Results: OAEI 20072007

Linear Sigmoidal Neural0

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Combination Functions Per-formance

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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?

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Question timeQuestion time

Q & A(If I’m showing this slide, I haven’t run out of time)

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Overall System ArchitectureOverall System Architecture

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Models viewModels view

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Data TailoringData TailoringData Tailoring, based on the Data Tailoring, based on the Dimension Tree Dimension Tree InstantiationInstantiation::• Schema Tailoring• Instance Tailoring

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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)