|Title:||Semantic metadata enrichment : a semi-automatic approach for linking legacy metadata with knowledge organization systems||Language:||English||Authors:||Moser, Bernd||Qualification level:||Diploma||Advisor:||Klas, Wolfgang||Issue Date:||2007||Number of Pages:||73||Qualification level:||Diploma||Abstract:||
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.
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.
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.
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.
|Keywords:||Ontologien; Thesauri; Klassifikation; maschinelles lernen
ontologies; thesauri; metadata; machine learning; semantic enrichment; classification
|Library ID:||AC05035672||Organisation:||KEIN -||Publication Type:||Thesis
|Appears in Collections:||Thesis|
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checked on Jun 21, 2021
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