Abstract
In this paper we describe an ontology and rule based system that significantly increases the productivity of those who create metadata, and the quality of the metadata they produce. The system suggests values for metadata elements using a combination of four methods: inheritance, aggregation, content based similarity and ontology-based similarity. Instead of aiming for automated metadata generation we have developed a mechanism for suggesting the most relevant values for a particular metadata field. 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. In this paper we first introduce the basic features of metadata systems and provide a typology of metadata records and metadata elements. Next we analyze the source of suggested values for metadata elements and discuss four methods of metadata generation. We discuss how the operations on objects the metadata are describing affect suggested metadata values and we present decision tables for the metadata generation scheduling algorithm. Finally, we discuss the use of our system in tools developed for creating e-learning material conformant with the SCORM reference model and the IEEE LTSC LOM standard.
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Hatala, M., Richards, G. (2003). Value-Added Metatagging: Ontology and Rule Based Methods for Smarter Metadata. In: Schröder, M., Wagner, G. (eds) Rules and Rule Markup Languages for the Semantic Web. RuleML 2003. Lecture Notes in Computer Science, vol 2876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39715-1_5
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DOI: https://doi.org/10.1007/978-3-540-39715-1_5
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