Uncertainty in Ontology Matching: A Decision Rule-Based Approach

TL;DR

This paper presents a decision rule-based method leveraging belief functions to effectively handle uncertainty in ontology matching, improving the process of linking entities across heterogeneous web ontologies.

Contribution

It introduces a novel decision process using a distance measure within the belief functions framework to enhance ontology matching accuracy under uncertainty.

Findings

Effective handling of uncertainty in ontology matching

Improved accuracy in linking entities across heterogeneous ontologies

A new decision process based on belief functions

Abstract

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.