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Schema Matching a nd Query Rewriting in Ontology-based Data Integration. Zdeňka Linková linkova@cs.cas.cz ICS AS CR Advisor: Július Štuller. Acknowledgement. - PowerPoint PPT Presentation
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Schema Matching and Query Rewriting in Ontology-based Data Integration
Zdeňka Linkoválinkova@cs.cas.czICS AS CR
Advisor: Július Štuller
Acknowledgement
This work was supported by the project 1ET100300419 of the Program Information Society (of the Thematic Program II of the National Research Program of the Czech Republic) “Intelligent Models, Algorithms, Methods and Tools for the Semantic Web Realization”.
Outline of presentation
Introduction Virtual data integration Ontology based system Matching in the system Mapping in the system Query rewriting Conclusion
Introduction
Today’s world is a world of information Web data use expansion Need of efficient information processing => Semantic web idea (XML, RDF, ontologies)
Many data providers, working with distributed data Need of data integration
=> Semantic web data integration
Virtual data integration
Data stays physically stored in original sources
Data integration provides an integrated view over distributed data
Virtual data integration: Schema matching Schema mapping Query processing
Ontology-based system
Sources: Semantic web data (local and global)... RDF/XML Available ontologies for the sources ... OWL Task input: sources Si and ontologies Oj
Use of ontologies: Source ontologies and global ontology for provided
integrated data To do matching To describe mapping To query rewriting
Relationships in the system
Schema matching – process of searching schema correspondences
Schema mapping – description of found schema correspondences, i.e. definition of relation, rule, formula etc. (1-1 rules, use of views, LAV and GAV approaches ...)
Consider correspondences kinds: Is-a hierarchical relationship , Equivalence Disjointness
Matching and mapping in the system
For description of found correspondences in mapping, OWL ontologies and its features are used: rdfs:subClassOf for and
owl:equivalentClass for owl:disjointWith for
=> Ontology OI ... ontology of the integration system
... contains mapping in the system
How is OI obtained?
Matching and mapping in the system Shared ontology case:
All data are described in only one (shared) ontology – in that data relationships are described => no need to search somewhere else
General case – shared ontology not available: Local ontologies describing data in the local
sources Need to obtain shared ontology => Integration local sources’ ontologies
The task is transformed to the ontology merging task
Available tools developed when solving this task kind can be employed: Chimaera, PROMPT (Protégé), FCA-MERGE, HCONE (WordNet)
Related work on matching Various approaches searching schema correspondences
at different levels: Instance – data processing, e.g. domain Terms – string processing, vocabularies use, ... Structure – graphs methods applying, ...
Classical approaches in schema matching and mapping: Estimation from available information (data, structure,
external informational sources, …) Candidates selection (meassures, uncertainty, ...)
Here, the task is solved by merging ontologies: However, in ontology merging, similar principles as
mentioned above are used => similar principles are used at different level
Querying the integrated data
Sources Sj contains RDF/XML data Querying using SPARQL language
Given guery in global environment ... QG
However, data available only in local sources with local environments
Task: to rewrite the query to the local environment of the local sources with use of mapping ... QL
Si
Use of mapping for rewriting
Using mapping described in ontology
Passing the OWL ontology graph through equivalent or hierarchical relation
Using the known OOP rule: a child can substitute its parent
For term t:
generating set of all possible term rewritings ... R(t)
End condition: difference in between two passing steps is zero
Using mapping described in ontology
Simple query processing
Simple query – only simple condition on RDF triple For each term t in the query generate set of all
possible term rewritings … R(t) Using all R(t) for each term in the query obtain all
possible query rewritings … QL
Using local queries QL on local sources obtain local answers
Using reverse rewriting return answer placed in global environment … global answer
Simple query rewriting
Optimalization: Querying all possible query rewritings in each local
source is not effective => Using set of supported terms for each source
Obtained from ontology, source schema, source preprocessing…
Generating set of all relevant term/query rewritings for each source
Complex query processing
Complex query – also complex condition on searched RDF triple
Complex query is divided into simple queries by dividing complex condition into simple ones
Obtained answers corresponding to simple queries must be composed to the answer corresponding to the original (complex) query
Conclusion
Use of ontologies in virtual data integration: Transformes data integration task to ontology
merging task Can bring use of formalism, methods and tools from
the other task area Can help in task automatization effort Standardized structure instead of particular project
oriented mapping rules bring possibility of reuse of mapping
Possibility of expression various terms relations Future plans: experiments with real data
Thank for your attention
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