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Konferenzbeitrag

ROXXI: Reviving witness dOcuments to eXplore eXtracted Information

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

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Hose,  Katja
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Metzger,  Steffen
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Schenkel,  Ralf
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Zitation

Elbassuoni, S., Hose, K., Metzger, S., & Schenkel, R. (2010). ROXXI: Reviving witness dOcuments to eXplore eXtracted Information. In Proceedings of the 36th International Conference on Very Large Data Bases (pp. 1589-1592). New York, NY: ACM. Retrieved from http://www.comp.nus.edu.sg/~vldb2010/proceedings/files/papers/D19.pdf.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-000F-152C-1
Zusammenfassung
In recent years, there has been considerable research on information extraction and constructing RDF knowledge bases. In general, the goal is to extract all relevant information from a corpus of documents, store it into an ontology, and answer future queries based only on the created knowledge base. Thus, the original documents become dispensable. On the one hand, an ontology is a convenient and non-redundant structured source of information, based on which specific queries can be answered efficiently. On the other hand, many users doubt the correctness of facts and ontology subgraphs presented to them as query results without proof. Instead, users often wish to verify the obtained facts or subgraphs by reading about them in context, i.e., in a document relating the facts and providing background information. In this demo, we present ROXXI, a system operating on top of an existing knowledge base and reviving the abandoned witness documents. In doing so, it goes the opposite way of information extraction approaches – starting with ontological facts and tracing their way back to the documents they were extracted from. ROXXI offers interfaces for expert users (SPARQL) as well as for non-experts (ontology browser) and provides a ranked list of documents each associated with a content snippet highlighting the queried facts in context. At the demonstration site, we will show the advantages of this novel approach towards document retrieval and illustrate the benefits of reviving the documents that information extraction approaches neglect.