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Finding Concept Coverings in Aligning Ontologies of Linked Data Read chapter Read first chapter

2015 | OriginalPaper | Chapter

SPARTIQULATION: Verbalizing SPARQL Queries

Authors : Basil Ell, Denny Vrandečić, Elena Simperl

Published in: The Semantic Web: ESWC 2012 Satellite Events

Publisher: Springer Berlin Heidelberg

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Abstract

Much research has been done to combine the fields of Databases and Natural Language Processing. While many works focus on the problem of deriving a structured query for a given natural language question, the problem of query verbalization – translating a structured query into natural language – is less explored. In this work we describe our approach to verbalizing SPARQL queries in order to create natural language expressions that are readable and understandable by the human day-to-day user. These expressions are helpful when having search engines that generate SPARQL queries for user-provided natural language questions or keywords. Displaying verbalizations of generated queries to a user enables the user to check whether the right question has been understood. While our approach enables verbalization of only a subset of SPARQL 1.1, this subset applies to 90 % of the \(209\) queries in our training set. These observations are based on a corpus of SPARQL queries consisting of datasets from the QALD-1 challenge and the ILD2012 challenge.

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previous chapter Linguistic Modeling of Linked Open Data for Question Answering
next chapter Multilingual Ontology Matching Evaluation – A First Report on Using MultiFarm
Footnotes
1
The name is derived from joining SPARQL and articulation. A demo is available at http://​km.​aifb.​kit.​edu/​projects/​spartiqulator.
 
2
The main entity is rendered as the subject of the verbalization.
 
3
African countries is the rdfs:label of yago:AfricanCountries.
 
6
For nine questions no SPARQL query is given since they are out of scope regarding the datasets provided for the challenge. \(28\) queries are ASK queries.
 
7
\(37\) queries from 2011-dbpedia-train, \(36\) queries from 2011-musicbrainz-train, \(68\) queries from 2012-dbpedia-train, and \(68\) queries from 2012-musicbrainz-train.
 
8
Note that this not a complete visual representation of SPARQL SELECT queries and only solves for the purpose of visually representing the query graph examples.
 
9
DBpedia provides no rdfs:domain and rdfs:range information, such as foaf:Person for this property. Therefore here we give a generic verbalization to demonstrate the effect of missing domain and range information.
 
10
In case of a SELECT * query, all variables within the WHERE clause are candidates.
 
11
Given that all three variables can either be selected or not selected and at least one variable needs to be selected, this results in 7 combinations.
 
12
For example the query SELECT ?m WHERE { ex:PersonA ex:wife ?m . } can then be verbalized as The wife of PersonA instead of The wives of PersonA.
 
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Metadata
Title
SPARTIQULATION: Verbalizing SPARQL Queries
Authors
Basil Ell
Denny Vrandečić
Elena Simperl
Copyright Year
2015
Publisher
Springer Berlin Heidelberg
Book
The Semantic Web: ESWC 2012 Satellite Events

Print ISBN: 978-3-662-46640-7

Electronic ISBN: 978-3-662-46641-4

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-662-46641-4_9