Name
Abstract | ||
|---|---|---|
Considering the high heterogeneity of the ontologies published 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, considered 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 process based on a distance measure to identify the best possible matching entities for a given source entity. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-08795-5_6 | Communications in Computer and Information Science |
Keywords | DocType | Volume |
Theory of belief functions,decision rule,Jousselme distance,ontology matching | Journal | 442 |
ISSN | Citations | PageRank |
1865-0929 | 2 | 0.40 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amira Essaid | 1 | 4 | 1.80 |
| Arnaud Martin | 2 | 2 | 2.09 |
| Grégory Smits | 3 | 66 | 18.17 |
| Boutheina Ben Yaghlane | 4 | 189 | 33.49 |