As with the amount of a rapidly growing digitised content, the need for advanced search mechanisms, that guide end users through the information flood, is also growing. Ontologies and semantic search mechanisms will play a key role in solving that issue, presumed that stored metadata records are ontology aware and machine processable also on a semantic level. Typically, this is not the case with existing legacy metadata records. A lot of enterprises are in possession of metadata records, which are useless to new discovery services, because of the missing ontology-awareness. An Ontology is a mechanism to formally represent knowledge by the use of classification. An ontology-aware metadata record contains, a relation which links the record to a concept of the ontology that represents the records content best. Such a record is also known as enriched.<br />To enrich such metadata records, a convenient way is needed. One can imagine that for the end-user this process would be too time consuming.<br />The idea therefore, is to develop a semi-automatic way, which makes a given metadata record ontology-aware. Semi-automatic enrichment can be done by using the power of machine-learning techniques. Machine-learning algorithms try to classify data on the basis of features. In the case of a metadata record, a feature would be its textual representation.<br />Different textual representations, lead to different classifications. A classifier can be trained by an user, so that the classifier knows which terms a data record must have, to relate it to a specific part of the ontology.<br />
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English
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en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Ontologien
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Thesauri
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Klassifikation
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maschinelles lernen
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ontologies
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thesauri
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metadata
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machine learning
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semantic enrichment
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classification
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dc.title
Semantic metadata enrichment : a semi-automatic approach for linking legacy metadata with knowledge organization systems