Integrating linked data through RDFS and OWL: some lessons learnt

  • Authors:

  • Aidan Hogan

  • Affiliations:

  • Digital Enterprise Research Institute, National University of Ireland Galway, Ireland

  • Venue:
  • RR'11 Proceedings of the 5th international conference on Web reasoning and rule systems
  • Year:
  • 2011

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Abstract

In this paper, we summarise the lessons learnt from the PhD Thesis Exploiting RDFS and OWL for Integrating Heterogeneous, Large-Scale, Linked Data Corpora where we looked at three use-cases for reasoning over Linked Data: (i) translating data between different vocabulary terms; (ii) identifying and repairing noise in the form of inconsistency; and (iii) detecting and processing coreferent identifiers (identifiers which refer to the same thing). We summarise how we overcome the challenges of scalability and robustness faced when reasoning over Linked Data. We validate our methods against an open-domain corpus of 1.1 billion quadruples crawled from 4 million Linked Data documents, discussing the applicability and utility of our reasoning methods in such scenarios.