demonstration

SPARQL2NL: verbalizing sparql queries

Authors:

Axel-Cyrille Ngonga Ngomo,

Lorenz Bühmann,

Christina Unger,

Jens Lehmann,

Daniel GerberAuthors Info & Claims

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

Pages 329 - 332

https://doi.org/10.1145/2487788.2487936

Published: 13 May 2013 Publication History

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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

References

[1]

George Doddington. Automatic evaluation of machine translation quality using n-gram co-occurrence statistics. In Proceedings of HLT, pages 138--145, 2002.

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[2]

Jens Lehmann and Lorenz Bühmann. Autosparql: Let users query your knowledge base. In Proceedings of ESWC 2011, 2011.

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[3]

Axel-Cyrille Ngonga Ngomo, Lorenz Bühmann, Christina Unger, Jens Lehmann, and Daniel Gerber. Sorry, i don't speak sparql - translating sparql queries into natural language. In Proceedings of WWW, 2013.

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[4]

Saeedeh Shekarpour, Sören Auer, Axel-Cyrille Ngonga Ngomo, Daniel Gerber, Sebastian Hellmann, and Claus Stadler. Keyword-driven sparql query generation leveraging background knowledge. In ACM/IEEE WI, 2011.

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[5]

Christina Unger, Lorenz Bühmann, Jens Lehmann, Axel-Cyrille Ngonga Ngomo, Daniel Gerber, and Philipp Cimiano. Template-based question answering over RDF data. In Proceedings of WWW, 2012.

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Cited By

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Houssein EIbrahem NZaki ASayed A(2022)Semantic Protocol and Resource Description Framework Query Language: A Comprehensive ReviewMathematics10.3390/math1017320310:17(3203)Online publication date: 5-Sep-2022
https://doi.org/10.3390/math10173203
Kacupaj E(2020)Explicable Question AnsweringThe Semantic Web: ESWC 2020 Satellite Events10.1007/978-3-030-62327-2_41(261-269)Online publication date: 11-Nov-2020
https://doi.org/10.1007/978-3-030-62327-2_41
Höffner KWalter SMarx EUsbeck RLehmann JNgonga Ngomo A(2017)Survey on challenges of Question Answering in the Semantic WebSemantic Web10.3233/SW-1602478:6(895-920)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.3233/SW-160247
Show More Cited By

Index Terms

SPARQL2NL: verbalizing sparql queries
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI theory, concepts and models
    2. Interaction paradigms
      1. Natural language interfaces
  2. Interaction design
    1. Interaction design theory, concepts and paradigms

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Information & Contributors

Information

Published In

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

May 2013

1636 pages

ISBN:9781450320382

DOI:10.1145/2487788

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

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Demonstration

Conference

WWW '13

Sponsor:

NICBR
CGIBR

WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

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Overall Acceptance Rate 1,068 of 6,946 submissions, 15%

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Cited By

View all

Houssein EIbrahem NZaki ASayed A(2022)Semantic Protocol and Resource Description Framework Query Language: A Comprehensive ReviewMathematics10.3390/math1017320310:17(3203)Online publication date: 5-Sep-2022
https://doi.org/10.3390/math10173203
Kacupaj E(2020)Explicable Question AnsweringThe Semantic Web: ESWC 2020 Satellite Events10.1007/978-3-030-62327-2_41(261-269)Online publication date: 11-Nov-2020
https://doi.org/10.1007/978-3-030-62327-2_41
Höffner KWalter SMarx EUsbeck RLehmann JNgonga Ngomo A(2017)Survey on challenges of Question Answering in the Semantic WebSemantic Web10.3233/SW-1602478:6(895-920)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.3233/SW-160247
Usbeck RRöder MHaase PKozlov ASaleem MNgomo A(2016)Requirements to Modern Semantic Search EngineKnowledge Engineering and Semantic Web10.1007/978-3-319-45880-9_25(328-343)Online publication date: 8-Sep-2016
https://doi.org/10.1007/978-3-319-45880-9_25
Bühmann LUsbeck RNgonga Ngomo A(2015)ASSESS — Automatic Self-Assessment Using Linked DataThe Semantic Web - ISWC 201510.1007/978-3-319-25010-6_5(76-89)Online publication date: 24-Oct-2015
https://doi.org/10.1007/978-3-319-25010-6_5
Marx EUsbeck RNgomo AHöffner KLehmann JAuer S(2014)Towards an open question answering architectureProceedings of the 10th International Conference on Semantic Systems10.1145/2660517.2660519(57-60)Online publication date: 4-Sep-2014
https://dl.acm.org/doi/10.1145/2660517.2660519
Ell BHarth ASimperl E(2014)SPARQL Query Verbalization for Explaining Semantic Search Engine QueriesThe Semantic Web: Trends and Challenges10.1007/978-3-319-07443-6_29(426-441)Online publication date: 2014
https://doi.org/10.1007/978-3-319-07443-6_29

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What are real SPARQL queries like?

Sorry, i don't speak SPARQL: translating SPARQL queries into natural language

Towards Reconciling SPARQL and Certain Answers