Fuzzy OWL: Uncertainty and the Semantic Web.

TLDR

The Semantic Web seeks to automatically retrieve, process, share, reuse, and align information, but most data and applications involve imprecise concepts that require degrees of relatedness, similarity, or ranking rather than binary truth values. The authors argue that handling such imprecise information will yield more realistic, intelligent, and effective applications. They extend the OWL ontology language with fuzzy set theory to capture, represent, and reason about these degrees of relatedness, similarity, and ranking.

Abstract

In the Semantic Web context information would be retrieved, processed, shared, reused and aligned in the maximum automatic way possible. Our experience with such applications in the Semantic Web has shown that these are rarely a matter of true or false but rather procedures that require degrees of relatedness, similarity, or ranking. Apart from the wealth of applications that are inherently imprecise, information itself is many times imprecise or vague. For example, the concepts of a “hot” place, an “expensive” item, a “fast” car, a “near” city, are examples of such concepts. Dealing with such type of information would yield more realistic, intelligent and effective applications. In the current paper we extend the OWL web ontology language, with fuzzy set theory, in order to be able to capture, represent and reason with such type of information.