Considering the high heterogeneity of the ontologies pub-lished on the web, ontology matching is a crucial issue whose aim is to establish links between an entity of a source ontology and one or several entities from a target ontology. Perfectible similarity measures, consid-ered as sources of information, are combined to establish these links. The theory of belief functions is a powerful mathematical tool for combining such uncertain information. In this paper, we introduce a decision pro-cess based on a distance measure to identify the best possible matching entities for a given source entity.
@article{arxiv.1501.05724,
title = {Uncertainty in Ontology Matching: A Decision Rule-Based Approach},
author = {Amira Essaid and Arnaud Martin and Grégory Smits and Boutheina Ben Yaghlane},
journal= {arXiv preprint arXiv:1501.05724},
year = {2015}
}