NLP Data Cleansing Based on Linguistic Ontology Constraints

  • Dimitris Kontokostas
  • Martin Brümmer
  • Sebastian Hellmann
  • Jens Lehmann
  • Lazaros Ioannidis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8465)

Abstract

Linked Data comprises of an unprecedented volume of structured data on the Web and is adopted from an increasing number of domains. However, the varying quality of published data forms a barrier for further adoption, especially for Linked Data consumers. In this paper, we extend a previously developed methodology of Linked Data quality assessment, which is inspired by test-driven software development. Specifically, we enrich it with ontological support and different levels of result reporting and describe how the method is applied in the Natural Language Processing (NLP) area. NLP is – compared to other domains, such as biology – a late Linked Data adopter. However, it has seen a steep rise of activity in the creation of data and ontologies. NLP data quality assessment has become an important need for NLP datasets. In our study, we analysed 11 datasets using the lemon and NIF vocabularies in 277 test cases and point out common quality issues.

Keywords

#eswc2014Kontokostas Linked Data NLP data quality 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dimitris Kontokostas
    • 1
  • Martin Brümmer
    • 1
  • Sebastian Hellmann
    • 1
  • Jens Lehmann
    • 1
  • Lazaros Ioannidis
    • 2
  1. 1.Institut für Informatik, AKSWUniversität LeipzigGermany
  2. 2.Medical Physics LaboratoryAristotle University of ThessalonikiGreece

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