The Qanary Ecosystem: Getting New Insights by Composing Question Answering Pipelines

  • Dennis DiefenbachEmail author
  • Kuldeep Singh
  • Andreas Both
  • Didier Cherix
  • Christoph Lange
  • Sören Auer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10360)


The field of Question Answering (QA) is very multi-disciplinary as it requires expertise from a large number of areas such as natural language processing (NLP), artificial intelligence, machine learning, information retrieval, speech recognition and semantic technologies. In the past years a large number of QA systems were proposed using approaches from different fields and focusing on particular tasks in the QA process. Unfortunately, most of these systems cannot be easily reused, extended, and results cannot be easily reproduced since the systems are mostly implemented in a monolithic fashion, lack standardized interfaces and are often not open source or available as Web services. To address these issues we developed the knowledge-based Qanary methodology for choreographing QA pipelines distributed over the Web. Qanary employs the qa vocabulary as an exchange format for typical QA components. As a result, QA systems can be built using the Qanary methodology in a simpler, more flexible and standardized way while becoming knowledge-driven instead of being process-oriented. This paper presents the components and services that are integrated using the qa vocabulary and the Qanary methodology within the Qanary ecosystem. Moreover, we show how the Qanary ecosystem can be used to analyse QA processes to detect weaknesses and research gaps. We illustrate this by focusing on the Entity Linking (EL) task w.r.t. textual natural language input, which is a fundamental step in most QA processes. Additionally, we contribute the first EL benchmark for QA, as open source. Our main goal is to show how the research community can use Qanary to gain new insights into QA processes.


Semantic web Software reusability Question answering Service composition Semantic search Ontologies Annotation model 



Parts of this work received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 642795, project: Answering Questions using Web Data (WDAqua). We would like to thank Elena Demidova for proof-reading.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dennis Diefenbach
    • 1
    Email author
  • Kuldeep Singh
    • 2
    • 3
  • Andreas Both
    • 4
  • Didier Cherix
    • 5
  • Christoph Lange
    • 2
    • 3
  • Sören Auer
    • 2
    • 3
  1. 1.Laboratoire Hubert CurienSaint EtienneFrance
  2. 2.Fraunhofer IAISSankt AugustinGermany
  3. 3.University of BonnBonnGermany
  4. 4.DATEV eGNurembergGermany
  5. 5.FLAVIA IT-Management GmbHKasselGermany

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