Dissimilarity Measure for Collections of Objects and Values

Automatic classiication may be used in object knowledge bases in order to suggest hypothesis about the structure of the available object sets. Yet its direct application meets some diiculties due to the way data is represented: attributes relating objects, multi-valued attributes, non-standard and external data types used in object descriptions. We present here an approach to the automatic classiication of objects based on a speciic dissimilarity model. The topological measure, presented in a previous paper, accounts for both object relations and the variety of available data types. In this paper, the extension of the topological measure on multi-valued object attributes, e.g. lists or sets, is presented. The resulting dissimilarity is completely integrated in the knowledge model Tropes which enables the deenition of a classiication strategy for an arbitrary knowledge base built on top of Tropes.