Wyniki wyszukiwania - BazTech

1

Learning in Description Logics with Fuzzy Concrete Domains

Lisi F. A., Straccia U.

Fundamenta Informaticae

|

2015

|

Vol. 140, nr 3/4

373--391

EN

Description Logics (DLs) are a family of logic-based Knowledge Representation (KR) formalisms, which are particularly suitable for representing incomplete yet precise structured knowledge. Several fuzzy extensions of DLs have been proposed in the KR field in order to handle imprecise knowledge which is particularly pervading in those domains where entities could be better described in natural language. Among the many approaches to fuzzification in DLs, a simple yet interesting one involves the use of fuzzy concrete domains. In this paper, we present a method for learning within the KR framework of fuzzy DLs. The method induces fuzzy DL inclusion axioms from any crisp DL knowledge base. Notably, the induced axioms may contain fuzzy concepts automatically generated from numerical concrete domains during the learning process. We discuss the results obtained on a popular learning problem in comparison with state-of-the-art DL learning algorithms, and on a test bed in order to evaluate the classification performance.

2

A Logic-based Computational Method for the Automated Induction of Fuzzy Ontology Axioms

Lisi F. A., Straccia U.

Fundamenta Informaticae

|

2013

|

Vol. 124, nr 4

503--519

EN

Fuzzy Description Logics (DLs) are logics that allow to deal with structured vague knowledge. Although a relatively important amount of work has been carried out in the last years concerning the use of fuzzy DLs as ontology languages, the problem of automatically managing the evolution of fuzzy ontologies has received very little attention so far. We describe here a logic-based computational method for the automated induction of fuzzy ontology axioms which follows the machine learning approach of Inductive Logic Programming. The potential usefulness of the method is illustrated by means of an example taken from the tourism application domain.

3

Representing Uncertainty in RuleML

Damásio C.V., Pan J.Z., Stoilos G., Straccia U.

Fundamenta Informaticae

|

2008

|

Vol. 82, nr 3

265-288

EN

The RuleML initiative defines a normalized markup for expressing and exchange rules in the Semantic Web. However, the syntax of the language is still limited and lacks features for representing rule-based languages capable of handling uncertainty and vagueness. It is desirable to have a general extension of RuleML which accommodates major existing languages proposed in the latest two decades. The main contribution of the paper is to propose such a general extension, showing how to encode many of the existing languages in this extension. It is detailed the important case of fuzzy rule languages. We hope this work can also provide some insights on how to cover uncertainty in the RIF framework.