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Detecting Similar Linked Datasets Using Topic Modelling Read chapter Read first chapter

2016 | OriginalPaper | Chapter

AskNow: A Framework for Natural Language Query Formalization in SPARQL

Authors : Mohnish Dubey, Sourish Dasgupta, Ankit Sharma, Konrad Höffner, Jens Lehmann

Published in: The Semantic Web. Latest Advances and New Domains

Publisher: Springer International Publishing

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Abstract

Natural Language Query Formalization involves semantically parsing queries in natural language and translating them into their corresponding formal representations. It is a key component for developing question-answering (QA) systems on RDF data. The chosen formal representation language in this case is often SPARQL. In this paper, we propose a framework, called AskNow, where users can pose queries in English to a target RDF knowledge base (e.g. DBpedia), which are first normalized into an intermediary canonical syntactic form, called Normalized Query Structure (NQS), and then translated into SPARQL queries. NQS facilitates the identification of the desire (or expected output information) and the user-provided input information, and establishing their mutual semantic relationship. At the same time, it is sufficiently adaptive to query paraphrasing. We have empirically evaluated the framework with respect to the syntactic robustness of NQS and semantic accuracy of the SPARQL translator on standard benchmark datasets.

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Footnotes
1
All POS-tag notations follow Penn Treebank.
 
2
In certain cases minor splitting has to be handled in the dependency relation, where the query starts with a preposition, e.g.: “In which country is New York located ?”.
 
3
Paraphrasing may include lexical substitution of synonymous query tokens and morphological changes of the tokens.
 
5
A standard dependency parser could also be used to understand the subject of the relation contain.
 
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Metadata
Title
AskNow: A Framework for Natural Language Query Formalization in SPARQL
Authors
Mohnish Dubey
Sourish Dasgupta
Ankit Sharma
Konrad Höffner
Jens Lehmann
Copyright Year
2016
Book
The Semantic Web. Latest Advances and New Domains

Print ISBN: 978-3-319-34128-6

Electronic ISBN: 978-3-319-34129-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-34129-3_19