As nowadays, often different systems are used and interlinked, very often the underlying taxonomies are heterogeneous. Taxonomic heterogeneity is the cognitive and methodical disparity between two taxonomies describing the same domain of interest and occurs in four categories: terminological heterogeneity (different languages), conceptual heterogeneity (contradictory structures), syntactical heterogeneity (varying data models), and semiotic heterogeneity (disparate cognitive interpretations). Taxonomy matching systems and algorithms find correspondences between concepts laborious or facile based on the type(s) of heterogeneity between taxonomies. Because of this, it is not sufficient to reduce the observation of inhomogeneity on merely the categories of heterogeneity only, as for every type of heterogeneity different grades of unevenness exist. Based on indicated various degrees of heterogeneity a new methodology is established using this chapter. The degree of heterogeneity depends on the used semantic, syntax, and fine granularity, to express an identical domain of interest. After reviewing the existing classification, the novel methodology is discussed, before it is directly applied on recent taxonomy matching systems and compared with results provided through OAEI. The experimental evaluation highlights the efficiency of the proposed methodology.
|Title of host publication||Taxonomy Matching Using Background Knowledge|
|Subtitle of host publication||Linked Data, Semantic Web and Heterogeneous Repositories|
|Number of pages||10|
|Publication status||Published - 8 Jan 2018|