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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ngonga Ngomo, Axel-Cyrille Gerber, Daniel Unger, Christina Lehmann, Jens Bühmann, Lorenz |
| Abstract | Linked Data technologies are now being employed by a large number of applications. While experts can query the backend of these applications using the 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. Yet, these tools are usually unable to make the meaning of the queries they generate plain 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. We present SPARQL2NL, a generic approach that allows verbalizing SPARQL queries, i.e., converting them into natural language. In addition to generating verbalizations, our approach can also explain the output of queries by providing a natural-language description of the reasons that led to each element of the result set being selected. Our evaluation of SPARQL2NL within a large-scale user survey shows that SPARQL2NL generates 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. Within the demo, we present the results generated by our approach on arbitrary questions to the DBpedia and MusicBrainz datasets. Moreover, we present how our framework can be used to explain results of SPARQL queries in natural language. |
| Starting Page | 329 |
| Ending Page | 332 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450320382 |
| DOI | 10.1145/2487788.2487936 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-05-13 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Sparql Natural language generation Query verbalization |
| Content Type | Text |
| Resource Type | Article |
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