Building natural language generation systems
Building natural language generation systems
Towards a theory of natural language interfaces to databases
Proceedings of the 8th international conference on Intelligent user interfaces
Aggregation in Natural Language Generation
EWNLG '93 Selected papers from the Fourth European Workshop on Trends in Natural Language Generation, An Artificial Intelligence Perspective
Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
HLT '02 Proceedings of the second international conference on Human Language Technology Research
From Databases to Natural Language: The Unusual Direction
NLDB '08 Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
An experiment on "free generation" from single RDF triples
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Guideline based evaluation and verbalization of OWL class and property labels
Data & Knowledge Engineering
Verbalizing Ontologies in Controlled Baltic Languages
Proceedings of the 2010 conference on Human Language Technologies -- The Baltic Perspective: Proceedings of the Fourth International Conference Baltic HLT 2010
AutoSPARQL: let users query your knowledge base
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
Keyword-Driven SPARQL Query Generation Leveraging Background Knowledge
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Template-based question answering over RDF data
Proceedings of the 21st international conference on World Wide Web
First-Order reasoning for attempto controlled english
CNL'10 Proceedings of the Second international conference on Controlled Natural Language
Extracting multilingual natural-language patterns for RDF predicates
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
DeFacto - deep fact validation
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
SPARQL2NL: verbalizing sparql queries
Proceedings of the 22nd international conference on World Wide Web companion
Introduction to linked data and its lifecycle on the web
RW'13 Proceedings of the 9th international conference on Reasoning Web: semantic technologies for intelligent data access
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Over the past years, Semantic Web and Linked Data technologies have reached the backend of a considerable number of applications. Consequently, large amounts of RDF data are constantly being made available across the planet. While experts can easily gather information from this wealth of data by using the W3C standard query language SPARQL, most lay users lack the expertise necessary to proficiently interact with these applications. Consequently, non-expert users usually have to rely on forms, query builders, question answering or keyword search tools to access RDF data. However, these tools have so far been unable to explicate the queries they generate to lay users, making it difficult for these users to i) assess the correctness of the query generated out of their input, and ii) to adapt their queries or iii) to choose in an informed manner between possible interpretations of their input. This paper addresses this drawback by presenting SPARQL2NL, a generic approach that allows verbalizing SPARQL queries, i.e., converting them into natural language. Our framework can be integrated into applications where lay users are required to understand SPARQL or to generate SPARQL queries in a direct (forms, query builders) or an indirect (keyword search, question answering) manner. We evaluate our approach on the DBpedia question set provided by QALD-2 within a survey setting with both SPARQL experts and lay users. The results of the 115 filled surveys show that SPARQL2NL can generate complete and easily understandable natural language descriptions. In addition, our results suggest that even SPARQL experts can process the natural language representation of SPARQL queries computed by our approach more efficiently than the corresponding SPARQL queries. Moreover, non-experts are enabled to reliably understand the content of SPARQL queries.