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-today 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.
CITATION STYLE
Ell, B., Vrandečić, D., & Simperl, E. (2015). SPARTIQULATION: Verbalizing SPARQL queries. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7540, pp. 117–131). Springer Verlag. https://doi.org/10.1007/978-3-662-46641-4_9
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