Michael's presentation about Data Integration with SMW+ and TripleStore Professional.

1. Data Integration with SMW+
Michael Erdmann, ontoprise, Karlsruhe
SMWCon Fall 2011, Berlin
© 2010 ontoprise GmbH Seite 1

2. Data Integration Scenario
 Enterprises typically manage their data in relational databases
 Often a landscape of isolated data silos grows
 Wiki provides one platform for sharing information
 Nevertheless,
 the data bases exist
 contain valuable information
 will not be deserted
 The enterprise use cases we see, often require access to
external data from within the wiki
 Usually the external data sources are relational databases
 Wiki is used as a data access and visualization tool
 Wiki users should be able to formulate queries
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3. Architecture
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4. Architecture
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5. Workflow for Data Integration with SMW+
 OntoStudio  SMW+ / TSC
 Lift database schema  Import ontologies
 Model wiki ontology  Formulate queries (QI)
 Map database ontology to  Create articles containing
inline queries
wiki ontology
 Browse database via Non-
 Test and refine mappings Existing-Pages
 Export ontologies  Potentially, enhance model
 Transfer to Wiki server via annotations
 Adapt and extend wiki
ontology
 Import ontology  Export ontology as OBL
 Test and refine ontology
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6. Query Answering
TSC with OntoBroker
mapping results
manual mappings query
SMW+
generated
ontologies
mapping rules
integrated
results
Wiki
facts lifting automatic ontology
schema
from rules mapping
source
databases
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7. Queries and Results use the Wiki Vocabulary
TSC with OntoBroker
mapping results
manual mappings query
SMW+
generated
ontologies
mapping rules
integrated
results
Wiki
facts lifting automatic ontology
schema
from rules mapping
source
databases
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8. Screen Capture Lifting and Mapping
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9. Summary and Outlook
 SMW+ and TripleStoreConnector realize
 Access to relational data from within SMW
 Using the wiki vocabulary
 Live queries
 Enhance/Extend the data by wiki users in the wiki
 Currently working on
 Supporting the same feature for SPARQL endpoints
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10. Lifting SPARQL Endpoints
 Approach
 Endpoint is queried for its schema information
 Schema is extracted
 Lifting rules are generated
 This results is an ObjectLogic ontology
 This ontology can be integrated and mapped into the wiki
ontology
 It can even be mixed with other data sources, e.g. DBs.
 In the wiki
 Wiki ontology can be used to query and visualize remote data
from the endpoint
 Query Interface supports query building out of the box
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11. Architecture
External Data Sources
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12.  Thank you!
Michael Erdmann
ontoprise GmbH
Karlsruhe, Germany
erdmann@ontoprise.de
© 2010 ontoprise GmbH Seite 12