Advertisement

Trill: A Reusable Front-End for QA Systems

  • Dennis Diefenbach
  • Shanzay Amjad
  • Andreas Both
  • Kamal Singh
  • Pierre Maret
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10577)

Abstract

The Semantic Web contains an enormous amount of information in the form of knowledge bases. To make this information available to end-users many question answering (QA) systems over knowledge bases were created in the last years. Their goal is to enable users to access large amounts of structured data in the Semantic Web by bridging the gap between natural language and formal query languages like SPARQL.

But automatically generating a SPARQL query from a user’s question is not sufficient to bridge the gap between Semantic Web data and the end-users. The result of a SPARQL query consists of a list of URIs and/or literals, which is not a user-friendly presentation of the answer. Such a presentation includes the representation of the URI in the right language and additional information like images, maps, entity summaries and more.

We present Trill, the first reusable user-interface (UI) for QA systems over knowledge bases supporting text and audio input, able to present answers from DBpedia and Wikidata in 4 languages (English, French, German, and Italian). It is designed to be used together with Qanary, an infrastructure for composing QA pipelines. This front-end enables the QA community to show their results to end-users and enables the research community to explore new research directions like studying and designing user-interactions with QA systems.

Keywords

Question answering systems Front-end User interaction Answer presentation 

Notes

Acknowledgments

Parts of this work received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No. 642795, project: Answering Questions using Web Data (WDAqua).

References

  1. 1.
    Both, A., Diefenbach, D., Singh, K., Shekarpour, S., Cherix, D., Lange, C.: Qanary – a methodology for vocabulary-driven open question answering systems. In: Sack, H., Blomqvist, E., d’Aquin, M., Ghidini, C., Ponzetto, S.P., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9678, pp. 625–641. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-34129-3_38CrossRefGoogle Scholar
  2. 2.
    Cabrio, E., Cojan, J., Aprosio, A.P., Magnini, B., Lavelli, A., Gandon, F.: QAKiS: an open domain QA system based on relational patterns. In: Proceedings of the 2012th International Conference on Posters & Demonstrations Track, vol. 914 (2012)Google Scholar
  3. 3.
    Diefenbach, D., Hormozi, N., Amjad, S., Both, A.: Introducing feedback in Qanary: how users can interact with QA systems. In: ESWC P&D (2017)Google Scholar
  4. 4.
    Diefenbach, D., Singh, K., Both, A., Cherix, D., Lange, C., Auer, S.: The Qanary ecosystem: getting new insights by composing question answering pipelines. In: Cabot, J., Virgilio, R., Torlone, R. (eds.) ICWE 2017. LNCS, vol. 10360, pp. 171–189. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-60131-1_10CrossRefGoogle Scholar
  5. 5.
    Diefenbach, D., Singh, K., Maret, P.: WDAqua-core0: a question answering component for the research community. In: ESWC, 7th Open Challenge on Question Answering over Linked Data (QALD-7) (2017)CrossRefGoogle Scholar
  6. 6.
    Ferrández, Ó., Spurk, C., Kouylekov, M., Dornescu, I., Ferrández, S., Negri, M., Izquierdo, R., Tomás, D., Orasan, C., Neumann, G., Magnini, B., González, J.: The QALL-ME framework: a specifiable-domain multilingual Question Answering architecture. J. Web Sem. 9(2), 137–145 (2011)CrossRefGoogle Scholar
  7. 7.
    Marx, E., Usbeck, R., Ngonga Ngomo, A., Höffner, K., Lehmann, J., Auer, S.: Towards an open question answering architecture. In: SEMANTiCS (2014)Google Scholar
  8. 8.
    Shekarpour, S., Marx, E., Ngomo, A.C.N., Auer, S.: Sina: semantic interpretation of user queries for question answering on interlinked data. Web Semant. Sci. Serv. Agents World Wide Web 30, 39–51 (2015)CrossRefGoogle Scholar
  9. 9.
    Singh, K., Both, A., Diefenbach, D., Shekarpour, S.: Towards a message-driven vocabulary for promoting the interoperability of question answering systems. In: ICSC 2016 (2016)Google Scholar
  10. 10.
    Thalhammer, A., Lasierra, N., Rettinger, A.: LinkSUM: using link analysis to summarize entity data. In: Bozzon, A., Cudre-Maroux, P., Pautasso, C. (eds.) ICWE 2016. LNCS, vol. 9671, pp. 244–261. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-38791-8_14CrossRefGoogle Scholar
  11. 11.
    Thalhammer, A., Rettinger, A.: PageRank on wikipedia: towards general importance scores for entities. In: Sack, H., Rizzo, G., Steinmetz, N., Mladenić, D., Auer, S., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9989, pp. 227–240. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-47602-5_41CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dennis Diefenbach
    • 1
  • Shanzay Amjad
    • 2
  • Andreas Both
    • 3
  • Kamal Singh
    • 1
  • Pierre Maret
    • 1
  1. 1.Laboratoire Hubert CurienSaint EtienneFrance
  2. 2.University of OttawaOttawaCanada
  3. 3.DATEV eGNurembergGermany

Personalised recommendations