Collaboratively Patching Linked Data

  • Knuth M
  • Hercher J
  • Sack H
  • 16


    Mendeley users who have this article in their library.
  • N/A


    Citations of this article.


Today's Web of Data is noisy. Linked Data often needs extensive preprocessing to enable efficient use of heterogeneous resources. While consistent and valid data provides the key to efficient data processing and aggregation we are facing two main challenges: (1st) Identification of erroneous facts and tracking their origins in dynamically connected datasets is a difficult task, and (2nd) efforts in the curation of deficient facts in Linked Data are exchanged rather rarely. Since erroneous data often is duplicated and (re-)distributed by mashup applications it is not only the responsibility of a few original publishers to keep their data tidy, but progresses to be a mission for all distributers and consumers of Linked Data too. We present a new approach to expose and to reuse patches on erroneous data to enhance and to add quality information to the Web of Data. The feasibility of our approach is demonstrated by example of a collaborative game that patches statements in DBpedia data and provides notifications for relevant changes.

Author-supplied keywords

  • a purpose
  • crowdsourcing
  • data cleansing
  • dbpedia
  • games with
  • linked data
  • user feedback management

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Magnus Knuth

  • Johannes Hercher

  • Harald Sack

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free