NDLI: Template-based question answering over RDF data

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Template-based question answering over RDF data

Content Provider	ACM Digital Library
Author	Ngonga Ngomo, Axel-Cyrille Gerber, Daniel Unger, Christina Lehmann, Jens Cimiano, Philipp Bühmann, Lorenz
Abstract	As an increasing amount of RDF data is published as Linked Data, intuitive ways of accessing this data become more and more important. Question answering approaches have been proposed as a good compromise between intuitiveness and expressivity. Most question answering systems translate questions into triples which are matched against the RDF data to retrieve an answer, typically relying on some similarity metric. However, in many cases, triples do not represent a faithful representation of the semantic structure of the natural language question, with the result that more expressive queries can not be answered. To circumvent this problem, we present a novel approach that relies on a parse of the question to produce a SPARQL template that directly mirrors the internal structure of the question. This template is then instantiated using statistical entity identification and predicate detection. We show that this approach is competitive and discuss cases of questions that can be answered with our approach but not with competing approaches.
Starting Page	639
Ending Page	648
Page Count	10
File Format	PDF
ISBN	9781450312295
DOI	10.1145/2187836.2187923
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-04-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Semantic web Sparql Natural language patterns Question answering
Content Type	Text
Resource Type	Article

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