Treo: best-effort natural language queries over linked data

  • Authors:

  • André Freitas;João Gabriel Oliveira;Seán O'Riain;Edward Curry;João Carlos Pereira Da Silva

  • Affiliations:

  • Digital Enterprise Research Institute, National University of Ireland, Galway;Digital Enterprise Research Institute, National University of Ireland, Galway and Computer Science Department, Universidade Federal do Rio de Janeiro;Digital Enterprise Research Institute, National University of Ireland, Galway;Digital Enterprise Research Institute, National University of Ireland, Galway;Computer Science Department, Universidade Federal do Rio de Janeiro

  • Venue:
  • NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
  • Year:
  • 2011

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Abstract

Linked Data promises an unprecedented availability of data on the Web. However, this vision comes together with the associated challenges of querying highly heterogeneous and distributed data. In order to query Linked Data on the Web today, end-users need to be aware of which datasets potentially contain the data and the data model behind these datasets. This query paradigm, deeply attached to the traditional perspective of structured queries over databases, does not suit the heterogeneity and scale of the Web, where it is impractical for data consumers to have an a priori understanding of the structure and location of available datasets. This work describes Treo, a best-effort natural language query mechanism for Linked Data, which focuses on the problem of bridging the semantic gap between end-user natural language queries and Linked Datasets.