A Comprehensive System for Computer-aided Metadata Generation

In this paper, we describe a system that generates suggested values for metadata elements. The system significantly increases the productivity of metadata creators as well as the quality of the metadata. The system is applicable to any metadata standard both for single metadata records and collections of related metadata records. Instead of aiming for automated metadata generation we have developed a mechanism for suggesting the most relevant values for a particular metadata field. The suggested values are generated using a combination of four methods: inheritance, aggregation, content based similarity and ontology-based similarity. The main strength of the system is that it provides a generic solution independent of the metadata schema and application domain. In addition to generating metadata from standard sources such as object content and user profiles, the system benefits from considering metadata record assemblies, metadata repositories, explicit domain ontologies and inference rules as prime sources for metadata generations. First, we describe the generic system and then provide examples of how the system can be implemented and used in tools developed for creating elearning material conformant with the SCORM reference model and the IEEE LTSC LOM standard.