Transcript of X Som Graduation Presentation
- 1. An Ontology-Based Data Integration System: Solving Semantic
Inconsistencies Giorgio Orsi October 23, 2006 X-SOM (eXtensible
Smart Ontology Mapper) Politecnico di Milano
- 2. Introduction
- This work is part of the Context-ADDICT project.
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- Context-dependent information systems.
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- Heterogeneous and mobile data sources.
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- Need for integration of different schemata.
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- Shared and formal specification of a
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- conceptualization of a given domain.
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- OWL-DL (Web Ontology Language [DL])
- 3. Why Ontologies?
- Well formalized, XML-based format for the description and
integration of data source schemata.
- Exploit ontology semantic richness for schema integration:
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- an ontology as a unified representation to mediate access to
data sources.
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- an ontology as a guide for terminological/structural conflict
resolution in traditional schema integration
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- (Semi-)automatic ontology mapper.
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- Semantic consistency check.
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- Need for high precision of the results.
- 4. Ontology Matching Vehicle SportCar Car Truck Motorized
Motorless SW SportCar Wheel Vehicle Bicycle 1.0 0.5 0.7 1.0 0.3
1.0
- 5. Approaches
- Ontology Mapping : Binding ontology's concepts with
owl:equivalentClass and rdfs:subClassOf properties.
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- Ontology Merging : Creating new concepts by merging data
sources' elements in new ones.
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- Ontology Articulation : Creating a set of rules , describing
relationships among data sources' concepts.
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- Ontology Integration : Creating a unique integrated ontology by
reuse of the elements of data sources' ontologies as-is.
- 6. Additional Requirements Issues What we need
- Tools requiring massive user's interaction.
- Learning from user's corrections.
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- Ontologies created by designers with different technical
backgrounds.
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- Management of the mismatches arising from different views of
the application domain.
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- Many techniques for ontology mapping (syntactic, structural,
probabilistic,...).
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- Combining different techniques considering their
reliability.
- 7. X-SOM's Components XML/OWL OWL Loader OWL dialect verifier
Mapper Mapping Strategy WordNet String Metrics Walk ISLab HMatch
OWL Input Ontologies Output Ontology Writer I/O Mapper Modules
Module Interface Module Interface Module Interface Module Interface
Modules Registry Average Functions Registry XML XML Config. Files
Consistency Checker Manual mapper Module Interface Google API
Module Interface ... Module Interface Neural Network Trainer
Training Set Builder GUI
- 8. Aggregation: The Neural Network y=ax y=ax y=ax y=ax Jaro
Module WordNet Module ISLab HMatch Another Module y=sigm(x)
y=sigm(x) y=sigm(x) y=sigm(x)
- Determines the weight of each matching technique.
- Supports the learning phase to achieve automatic behavior.
- 9. Consistency Checking
- Standard Consistency Check: Ensure the T-BOX
satisfiability.
- Semantic Consistency Check: Performs standard consistency check
with the addition of semantic local checks:
- Hypothesis : Consistent source ontologies.
- 10. Inconsistencies Sympthoms O2:Y O2:Z O1:X
- Loss of concepts satisfiability:
Master O1:Student Bachelor Ph.D. Master Bachelor O2:Student unionOf
unionOf DisjointWith DisjointWith DisjointWith equivalentClass
equivalentClass equivalentClass equivalentClass
- 11. Semantic inconsistencies Reader Author Organization Person
Author Person subclassOf subclassOf subclassOf subclassOf
equivalentClass
- 12. Semantic inconsistencies Reader Author Organization Person
Author Person subclassOf subclassOf subclassOf subclassOf
equivalentClass Everything that is an author is also a person An
organization is a person Wrong!
- 13. Semantic inconsistencies Reader Author Organization Person
Author Person subclassOf subclassOf subclassOf subclassOf
equivalentClass
- 14. Testing X-SOM
- 15. Testing X-SOM: Comparison
- 16. Conclusions and Future Works
- We have realized an ontology mapping tool with high precision
and recall. However this task is still not feasible in a
fully-automatic fashion.
- It seems that the reliability of a matching algorithm is not
domain-dependent . The neural network approach works!
- Mapping errors often triggered by an questionable ontology
design.
- Enhancing the recall through the implementation of additional
matching techniques .
- Replacing the neural network with other machine learning
techniques.
- Improving the semantic consistency check with more local and
global checks.
- 17. Questions? Further details: [email_address]
http://context-addict.elet.polimi.it/
- 18. Performance Measures:
CM RM CRM
- 19. How X-SOM works