Abstract

Works on taxonomy matching have the aim to help overcoming taxonomic heterogeneity existing between two taxonomies. The heterogeneity arises, because different persons usually have a varying cognitive and methodological interpretation of a domain, even if the domain of interest is same. However, when using taxonomies to structure data, the heterogeneity inside taxonomies provides significant benefits for other research areas. The heterogeneity in the form of concepts existing can be used to help creating taxonomies that only filter for relevant concepts according to a provided keywords. The heterogeneity can also be utilized to create subtaxonomies based on the preferences shown by a user or customer. And, the heterogeneity can be used to adapt the taxonomy by providing different modification rules, also by utilizing preferences provided through a recommender system. Using the chapter at hand, all the mentioned research paradigms are discussed in detail, including the areas of: dynamic taxonomies, catalog segmentation, personalized directories as well as recommender systems. For each area, the most important techniques as well as the most recent applications are discussed, after detailing the aim of the research area.

Original languageEnglish
Title of host publicationTaxonomy Matching Using Background Knowledge
Subtitle of host publicationLinked Data, Semantic Web and Heterogeneous Repositories
PublisherSpringer
Chapter5
Pages71-83
Number of pages13
ISBN (Electronic)978-3-319-72209-2
ISBN (Print)978-3-319-72208-5
DOIs
Publication statusPublished - 8 Jan 2018

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