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Identifying Inference Rules for Automatic Metadata Generation from Pre-existing Metadata of Related Resources

  • Merkourios Margaritopoulos
  • Isabella Kotini
  • Athanasios Manitsaris
  • Ioannis Mavridis

Manual indexing of learning resources according to metadata standards is a laborious task. The introduction of automatic metadata generation methods is a developing research field with diverse approaches, which appears as an option having the advantage of the economy of work for not having to manually create metadata. In this paper, a methodology for automatic generation of metadata which exploits relations between resources to be described is introduced and examples and empirical data on the application of the methodology to the LOM (Learning Object Metadata) standard are presented. The methodology comprises the execution of consecutive steps of actions aiming at identifying inference rules for automatic generation of a resource's metadata based on pre-existing metadata of its related resources.

Keywords

Inference Rule Related Resource Manual Indexing Metadata Standard Implicit Relation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Applied InformaticsUniversity of MacedoniaThessalonikiGreece

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