QAestro – Semantic-Based Composition of Question Answering Pipelines

  • Kuldeep Singh
  • Ioanna LytraEmail author
  • Maria-Esther Vidal
  • Dharmen Punjani
  • Harsh Thakkar
  • Christoph Lange
  • Sören Auer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10438)


The demand for interfaces that allow users to interact with computers in an intuitive, effective, and efficient way is increasing. Question Answering (QA) systems address this need by answering questions posed by humans using knowledge bases. In recent years, many QA systems and related components have been developed both by practitioners and the research community. Since QA involves a vast number of (partially overlapping) subtasks, existing QA components can be combined in various ways to build tailored QA systems that perform better in terms of scalability and accuracy in specific domains and use cases. However, to the best of our knowledge, no systematic way exists to formally describe and automatically compose such components. Thus, in this work, we introduce QAestro, a framework for semantically describing both QA components and developer requirements for QA component composition. QAestro relies on a controlled vocabulary and the Local-as-View (LAV) approach to model QA tasks and components, respectively. Furthermore, the problem of QA component composition is mapped to the problem of LAV query rewriting, and state-of-the-art SAT solvers are utilized to efficiently enumerate the solutions. We have formalized 51 existing QA components implemented in 20 QA systems using QAestro. Our empirical results suggest that QAestro enumerates the combinations of QA components that effectively implement QA developer requirements.


Question Answering (QA) Semantics-based Composition QA Pipeline Query Rewriting QA Components 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Parts of this work received funding from the EU Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 642795 (WDAqua).


  1. 1.
    Androutsopoulos, I., Ritchie, G.D., Thanisch, P.: Natural language interfaces to databases - an introduction. Nat. Lang. Eng. 1(1), 29–81 (1995)CrossRefGoogle Scholar
  2. 2.
    Arvelo, Y., Bonet, B., Vidal, M.: Compilation of query-rewriting problems into tractable fragments of propositional logic. In: Proceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference (2006)Google Scholar
  3. 3.
    Berardi, D., Cheikh, F., Giacomo, G.D., Patrizi, F.: Automatic service composition via simulation. Int. J. Found. Comput. Sci. 19(2), 429–451 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Bolc, L. (ed.): Natural Language Communication with Computers. LNCS, vol. 63. Springer, Heidelberg (1978). doi: 10.1007/BFb0031367 zbMATHGoogle Scholar
  5. 5.
    Both, A., Diefenbach, D., Singh, K., Shekarpour, S., Cherix, D., Lange, C.: Qanary-a methodology for vocabulary-driven open question answering systems. In: ESWC (2016)Google Scholar
  6. 6.
    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 ISWC 2012 Posters and Demonstrations Track (2012)Google Scholar
  7. 7.
    Dubey, M., Dasgupta, S., Sharma, A., Höffner, K., Lehmann, J.: AskNow: a framework for natural language query formalization in SPARQL. In: Sack, H., Blomqvist, E., d’Aquin, M., Ghidini, C., Ponzetto, S.P., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9678, pp. 300–316. Springer, Cham (2016). doi: 10.1007/978-3-319-34129-3_19 CrossRefGoogle Scholar
  8. 8.
    Ferrández, Ó., Spurk, C., Kouylekov, M., Dornescu, I., Ferrández, S., Negri, M., Izquierdo, R., Tomás, D., Orasan, C., Neumann, G., Magnini, B., González, J.L.V.: The QALL-ME framework: a specifiable-domain multilingual question answering architecture. J. Web Semant. 9(2), 137–145 (2011)CrossRefGoogle Scholar
  9. 9.
    Finkel, J.R., Grenager, T., Manning, C.D.: Incorporating non-local information into information extraction systems by Gibbs sampling. In: 43rd Annual Meeting of the Association for Computational Linguistics ACL (2005)Google Scholar
  10. 10.
    Gomes, C.P., Kautz, H.A., Sabharwal, A., Selman, B.: Satisfiability Solvers (2008)Google Scholar
  11. 11.
    Halevy, A.Y.: Answering queries using views: a survey. VLDB J. 10(4), 270–294 (2001)CrossRefzbMATHGoogle Scholar
  12. 12.
    Höffner, K., Walter, S., Marx, E., Usbeck, R., Lehmann, J., Ngonga Ngomo, A.-C.: Survey on challenges of question answering in the semantic web. Semant. Web J. (2016).
  13. 13.
    Izquierdo, D., Vidal, M.-E., Bonet, B.: An expressive and efficient solution to the service selection problem. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010. LNCS, vol. 6496, pp. 386–401. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-17746-0_25 CrossRefGoogle Scholar
  14. 14.
    Konstantinidis, G., Ambite, J.L.: Scalable query rewriting: a graph-based approach. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2011)Google Scholar
  15. 15.
    Levy, A.Y., Rajaraman, A., Ordille, J.J.: Querying heterogeneous information sources using source descriptions. In: Proceedings of 22th International Conference on Very Large Data Bases (1996)Google Scholar
  16. 16.
    López, V., Uren, V.S., Sabou, M., Motta, E.: Is question answering fit for the semantic web?: a survey. Semant. Web 2(2), 125–155 (2011)Google Scholar
  17. 17.
    Marx, E., Usbeck, R., Ngomo, A.N., Höffner, K., Lehmann, J., Auer, S.: Towards an open question answering architecture. In: SEMANTICS (2014)Google Scholar
  18. 18.
    Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems, I-SEMANTICS (2011)Google Scholar
  19. 19.
    Singh, K., Both, A., Diefenbach, D., Shekarpour, S.: Towards a message-driven vocabulary for promoting the interoperability of question answering systems. In: ICSC (2016)Google Scholar
  20. 20.
    Ullman, J.D.: Information integration using logical views. Theor. Comput. Sci. 239(2), 189–210 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Unger, C., Bühmann, L., Lehmann, J., Ngomo, A.N., Gerber, D., Cimiano, P.: Template-based question answering over RDF data. In: WWW (2012)Google Scholar
  22. 22.
    Unger, C., Freitas, A., Cimiano, P.: An introduction to question answering over linked data. In: Koubarakis, M., Stamou, G., Stoilos, G., Horrocks, I., Kolaitis, P., Lausen, G., Weikum, G. (eds.) Reasoning Web 2014. LNCS, vol. 8714, pp. 100–140. Springer, Cham (2014). doi: 10.1007/978-3-319-10587-1_2 CrossRefGoogle Scholar
  23. 23.
    Usbeck, R., Ngonga Ngomo, A.-C., Röder, M., Gerber, D., Coelho, S.A., Auer, S., Both, A.: AGDISTIS - graph-based disambiguation of named entities using linked data. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 457–471. Springer, Cham (2014). doi: 10.1007/978-3-319-11964-9_29 Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kuldeep Singh
    • 1
    • 2
  • Ioanna Lytra
    • 1
    • 2
    Email author
  • Maria-Esther Vidal
    • 1
  • Dharmen Punjani
    • 3
  • Harsh Thakkar
    • 2
  • Christoph Lange
    • 1
    • 2
  • Sören Auer
    • 1
    • 2
  1. 1.Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS)Sankt AugustinGermany
  2. 2.Institute for Applied Computer ScienceUniversity of BonnBonnGermany
  3. 3.Department of Informatics and TelecommunicationsNational and Kapodistrian University of AthensAthensGreece

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