- author = "Jens Lehmann",
- title = "Hybrid Learning of Ontology Classes",
- booktitle = "Proceedings of the 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2007",
- year = "2007",
- editor = "Petra Perner",
- volume = "4571",
- series = "Lecture Notes in Computer Science",
- pages = "883--898",
- month = jul # " 18-20",
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming",
- language = "English",
- isbn13 = "978-3-540-73498-7",
-
DOI = "
doi:10.1007/978-3-540-73499-4_66",
- size = "16 pages",
-
abstract = "Description logics have emerged as one of the most
successful formalisms for knowledge representation and
reasoning. They are now widely used as a basis for
ontologies in the Semantic Web. To extend and analyse
ontologies, automated methods for knowledge acquisition
and mining are being sought for. Despite its importance
for knowledge engineers, the learning problem in
description logics has not been investigated as deeply
as its counterpart for logic programs.
We propose the novel idea of applying evolutionary inspired methods to solve this task. In particular, we show how Genetic Programming can be applied to the learning problem in description logics and combine it with techniques from Inductive Logic Programming. We base our algorithm on thorough theoretical foundations and present a preliminary evaluation.",
