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Coupling Knowledge Bases and Web Services for Active Knowledge

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45219

Preda,  Nicoleta
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45572

Suchanek,  Fabian
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44738

Kasneci,  Gjergji
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons127842

Neumann,  Thomas
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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2009-5-004
(Any fulltext), 11KB

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Citation

Preda, N., Suchanek, F., Kasneci, G., Neumann, T., & Weikum, G.(2009). Coupling Knowledge Bases and Web Services for Active Knowledge (MPI-I-2009-5-004).


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-1901-1
Abstract
We present ANGIE, a system that can answer user queries by combining knowledge from a local database with knowledge retrieved from Web services. If a user poses a query that cannot be answered by the local database alone, ANGIE calls the appropriate Web services to retrieve the missing information. In ANGIE,Web services act as dynamic components of the knowledge base that deliver knowledge on demand. To the user, this is fully transparent; the dynamically acquired knowledge is presented as if it were stored in the local knowledge base. We have developed a RDF based model for declarative definition of functions embedded in the local knowledge base. The results of available Web services are cast into RDF subgraphs. Parameter bindings are automatically constructed by ANGIE, services are invoked, and the semi-structured information returned by the services are dynamically integrated into the knowledge base We have developed a query rewriting algorithm that determines one or more function composition that need to be executed in order to evaluate a SPARQL style user query. The key idea is that the local knowledge base can be used to guide the selection of values used as input parameters of function calls. This is in contrast to the conventional approaches in the literature which would exhaustively materialize all values that can be used as binding values for the input parameters.