 A collection of scientific papers is often accompanied by tags:
  keywords, topics, concepts etc., associated with each paper.
   Sometimes these tags are human-generated, sometimes they are
  machine-generated.   We propose a simple measure of the consistency
  of the tagging of scientific papers: whether these tags are
  predictive for the citation graph links.   Since the authors tend to
  cite papers about the topics close to those of their publications, a
  consistent tagging system could predict citations.   We present an
  algorithm to calculate consistency, and experiments with human- and
  machine-generated tags.   We show that augmentation, i.e. the combination
  of the manual tags with the machine-generated ones, can enhance the
  consistency of the tags.   We further introduce cross-consistency,
  the ability to predict citation links between papers tagged by
  different taggers, e.g. manually and by a machine.
   Cross-consistency can be used to evaluate the tagging quality when
  the amount of labeled data is limited.
A good tagger gives similar tags to a given paper and the papers it cites