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Searching RDF Graphs with SPARQL and Keywords

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44375

Elbassuoni,  Shady
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45248

Ramanath,  Maya
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45380

Schenkel,  Ralf
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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Zitation

Elbassuoni, S., Ramanath, M., Schenkel, R., & Weikum, G. (2010). Searching RDF Graphs with SPARQL and Keywords. Bulletin of the Technical Committee on Data Engineering, 33(1), 16-24. Retrieved from http://sites.computer.org/debull/A10mar/weikum-paper.pdf.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-1530-6
Zusammenfassung
The proliferation of knowledge-sharing communities likeWikipedia and the advances in automated information extraction from Web pages enable the construction of large knowledge bases with facts about entities and their relationships. The facts can be represented in the RDF data model, as so-called subject-property-object triples, and can thus be queried by structured query languages like SPARQL. In principle, this allows precise querying in the database spirit. However, RDF data may be highly diverse and queries may return way too many results, so that ranking by informativeness measures is crucial to avoid overwhelming users. Moreover, as facts are extracted from textual contexts or have community-provided annotations, it can be beneficial to consider also keywords for formulating search requests. This paper gives an overview of recent and ongoing work on ranked retrieval of RDF data with keyword-augmented structured queries. The ranking method is based on statistical language models, the state-of-the-art paradigm in information retrieval. The paper develops a novel form of language models for the structured, but schema-less setting of RDF triples and extended SPARQL queries.