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Erschienen in:
2012 | OriginalPaper | Buchkapitel
deqa: Deep Web Extraction for Question Answering
verfasst von : Jens Lehmann, Tim Furche, Giovanni Grasso, Axel-Cyrille Ngonga Ngomo, Christian Schallhart, Andrew Sellers, Christina Unger, Lorenz Bühmann, Daniel Gerber, Konrad Höffner, David Liu, Sören Auer
Erschienen in: The Semantic Web – ISWC 2012
Verlag: Springer Berlin Heidelberg
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Despite decades of effort, intelligent object search remains elusive. Neither search engine nor semantic web technologies alone have managed to provide usable systems for simple questions such as “find me a flat with a garden and more than two bedrooms near a supermarket.”
We introduce
deqa
, a conceptual framework that achieves this elusive goal through combining state-of-the-art semantic technologies with effective data extraction. To that end, we apply
deqa
, to the UK real estate domain and show that it can answer a significant percentage of such questions correctly.
deqa
achieves this by mapping natural language questions to
Sparql
patterns. These patterns are then evaluated on an RDF database of current real estate offers. The offers are obtained using
OXPath
, a state-of-the-art data extraction system, on the major agencies in the Oxford area and linked through
Limes
to background knowledge such as the location of supermarkets.