QuerioDALI: Question Answering Over Dynamic and Linked Knowledge Graphs

  • Vanessa Lopez
  • Pierpaolo Tommasi
  • Spyros Kotoulas
  • Jiewen Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9982)


We present a domain-agnostic system for Question Answering over multiple semi-structured and possibly linked datasets without the need of a training corpus. The system is motivated by an industry use-case where Enterprise Data needs to be combined with a large body of Open Data to fulfill information needs not satisfied by prescribed application data models. Our proposed Question Answering pipeline combines existing components with novel methods to perform, in turn, linguistic analysis of a query, named entity extraction, entity/graph search, fusion and ranking of possible answers. We evaluate QuerioDALI with two open-domain benchmarks and a biomedical one over Linked Open Data sources, and show that our system produces comparable results to systems that require training data and are domain-dependent. In addition, we analyze the current challenges and shortcomings.


Query Term User Query Graph Pattern Question Answering Name Entity Recognition 
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.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Vanessa Lopez
    • 1
  • Pierpaolo Tommasi
    • 1
  • Spyros Kotoulas
    • 1
  • Jiewen Wu
    • 1
  1. 1.Smarter Cities Technology CentreIBM ResearchDublinIreland

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