Document Type

Conference Proceeding

Publication Date

6-1-2007

Abstract

In order to leverage techniques from Inductive Logic Programming for the learning in description logics (DLs), which are the foundation of ontology languages in the Semantic Web, it is important to acquire a thorough understanding of the theoretical potential and limitations of using refinement operators within the description logic paradigm. In this paper, we present a comprehensive study which analyses desirable properties such operators should have. In particular, we show that ideal refinement operators in general do not exist, which is indicative of the hardness inherent in learning in DLs. We also show which combinations of desirable properties are theoretically possible, thus providing an important step towards the definition of practically applicable operators.

Comments

Presented at the 17th International Conference on Inductive Logic Programming, Corvallis, OR, June 19-21, 2007.

Attached is the unpublished, authors' version of this proceeding. The final, publisher's version can be found at http://dx.doi.org/10.1007/978-3-540-78469-2_18.

DOI

10.1007/978-3-540-78469-2_18