During the last decades, the amount of data has increased dramatically. This is because enterprises are using various information management systems, and because of the nowadays goal for interlinking between such systems to gain new information. To effectively store the extensive amount of data in a structured way, two metadata paradigms are used predominantly: taxonomies (formal metadata) and folksonomies (informal metadata). Taxonomies are classifying objects based on hierarchically ordered formal concepts. Because of this, taxonomies have its benefits for controlling how instances can be classified. However, when exchanging data across multiple information systems inside a single firm, or with external systems (e.g., digital marketplaces), the underlying taxonomies are very often not the same. This is because the domain is different or because the underlying methodologies are varying. Logically, the underlying taxonomies have to be mapped before exchanging data in a proper way, named taxonomy matching. Providing the chapter at hand, a detailed overview of this research area is given, including an explanation of its principles, the aim of matching taxonomies, the problem of heterogeneity, a categorization for matching attempts, as well as an overview of the mainly used evaluation metrics.
|Title of host publication||Taxonomy Matching using Background Knowledge|
|Subtitle of host publication||Linked Data, Semantic Web, and Heterogeneous Repositories|
|Number of pages||11|
|Publication status||Published - 8 Jan 2018|