W3C Workshop on RDF Access to Relational Databases October, 2007 Boston, MA, USA D2RQ. Lessons Learned

Transcription

1 W3C Workshop on RDF Access to Relational Databases October, 2007 Boston, MA, USA D2RQ Lessons Learned Christian Bizer Richard Cyganiak Freie Universität Berlin The D2RQ Plattform 2002: D2R MAP dump relational databases as RDF based on and expressive declarative mapping language 2004: D2RQ RDQL/SPARQL to SQL query rewriting Jena and Sesame API 2006: D2R Server SPARQL, Linked Data access over the Web Tested with Oracle, MySQL, and PostgreSQL Should work with any SQL-92 compatible database GNU GPL license, 4600 downloads (150 per month) 1

2 Outline 1. D2RQ Mapping Language 2. D2RQ Architecture and Interfaces 3. Areas for Future Community Work 1. RDF Access to Relational Databases 2. The Web Perspective The D2RQ Mapping Language Declarative language to express mappings between a given RDF schemata and a given relational database schemata. 2

3 Class Map Author ID first last 12 Chris Bizer map:author_classmap a d2rq:classmap; d2rq:class foaf:person; d2rq:uripattern "/people/@@author.id@@". rdf:type foaf:person. Property Bridge Author ID first last 12 Chris Bizer chris@bizer.de map: _propertybridge a d2rq:propertybridge; d2rq:belongstoclassmap map:author_classmap; d2rq:property foaf:name; d2rq:pattern foaf:name Chris Bizer. 3

4 Joins Author ID name 12 Chris Papers ID title confid 312 D2R Server 132 Rel_Authors_Papers AuthorID PaperID map:author_propertybridge a d2rq:propertybridge; d2rq:belongstoclassmap :PeopleClassMap; d2rq:property dc:creator; d2rq:referstoclassmap :PapersClassMap; d2rq:join Author.ID=Rel_Authors_Papers.AuthorID"; d2rq:join "Rel_Authors_Papers.PaperID=Papers.ID. dc:creator Other Features of the Mapping Language Conditional mappings Value translation tables Extensible with arbitrary value translation functions Performance hints 4

5 D2RQ Architecture and Interfaces Usage Examples Public Data Sources using D2R Server DBLP Bibliography (Universität Hannover) DBtune (University of London) Project Gutenberg catalogue (FU Berlin) CIA World Factbook (FU Berlin) Roller - Weblog Server (Sun Microsystems) OEM distribution as part of the TopBraid Ontology Editor Plus many unknown ones :-( 5

6 Performance and Limitations Performance is fine with databases containing a few million records. Dumps, Linked Data und HTML interface usually no problem. Simple SPARQL queries usually fine. Complex SPARQL queries (OPTINAL, FILTER, LIMIT) sometimes slow. Due to limitations of the implementation. Will improve with future releases. Limitations No support for Named Graphs Read only. No support for CREATE/DELETE/UPDATE No support for inference Areas for Future Community Work 6

7 RDF Access to Relational Databases With Virtuoso, DartGrid, SPASQL, SquirrelRDF, Relational.OWL, D2RQ, and there are various suitable solutions around. Compare the Expressivity of Mapping Languages People need weird mappings and fixups for database design anti-patterns. We need an accepted mapping benchmark which reflects this. First approach: THALIA testbed. Compare the Performance of the different Implementations We need an accepted performance benchmark. Future Community Work seen from the Web Perspective Mapping relational databases to RDF is a local problem and its technical realization matters little from the Web perspective. What people really want are expressive and fast queries over an integrated view on an unbounded number of data sources (the Web) expressed via simple user interfaces. We should aim at providing answers to the well-known, but hard data integration questions arising from this scenario. 7

8 Testbed: The Linking Open Data Cloud Federation versus Replication FOAF RDF Link DBpedia RDF Link geonames RDF Link SIOC RDF Link 1. Virtual Integration via SPARQL Query Federation DARQ (HU Berlin) Complicated and slow. 2. Materialized Integration via Crawling Zitgist (Zitgist), SWSE (DERI), Swoogle (UMBC), Watson (Open University) Fast, but requires huge RDF repositories. Worked for HTML, worked for RSS, so why not for RDF? 3. Materialization On-the-Fly Crawl only data that is needed while answering the query. Semantic Web Client Library (FU Berlin), SWIC (University of London) Works, but is really slow. 8

9 Data Source Discovery and Description 1. Registry-based Discovery Registries collect links or data source descriptions. - Example: Ping the Semantic Web Work on data source descriptions - DARQ, SADDLE 2. Link-based Discovery Discovering RDF data by following RDF Links. Worked fine on the classic HTML Web, so why not for the Semantic Web? Schema Mapping Still no clear answers to: How to express mappings between different RDF vocabularies? How to publish and search for such mappings on the Web? RDF Schema and OWL are insufficient in practice to express mappings. Maybe upcoming Rules Interchange Format (RIF) could provide a solution? 9

10 Conclusion We should have a look which parts of the Semantic Web puzzle are missing to make RDF-based data integration work on WEB- scale! This talk is online at 10