Concept Learning in Description Logics
The problem of learning logic programs has been researched extensively, but other knowledge representation formalisms like Description Logics are also an interesting target language. The importance of inductive reasoning in Description Logics has increased with the rise of the Semantic Web, because the learning algorithms can be used as a means for the computer aided building of ontologies. Ontology construction is a burdensome task and powerful tools are needed to support knowledge engineers.
One of the keys for designing induction algorithms in Description Logics are refinement operators. They allow for an efficient traversal of the subsumption hierarchy of concepts. One way to assess the suitability of a refinement operator for learning algorithms is to look at its properties. We analysed the properties completeness, weak completeness, properness, redundancy, finiteness, minimality, and their combinations, in particular we show theoretical limitations of refinement operators in Descriptions Logics.
Learning algorithms can be designed by combining a refinement operator with a a search heuristic. We propose an operator and show that it is close to the best we can hope for. We then create a sound learning algorithm by adding an intelligent search heuristic.
As a second approach we investigate the use of Genetic Programming to solve the learning problem in Description Logics. We discuss the characteristics of Genetic Programming in this context and show a way to incorporate refinement operators in the Genetic Programming framework. Again, we define a suitable operator and analyse it. Some further extensions are also proposed.
Related Publications
Journal Articles
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DL-Learner - A framework for inductive learning on the Semantic Web Journal Article
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Class expression learning for ontology engineering Journal Article
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DL-Learner: Learning Concepts in Description Logics Journal Article
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Books
Perspectives on Ontology Learning Book
IOS Press, 2014, ISBN: 978-1-61499-378-0.
Learning OWL Class Expressions Book
IOS Press, 2010, ISBN: 978-1-60750-528-0.
Incollections
Learning of OWL Class Expressions on Very Large Knowledge Bases and its Applications Incollection
In: Semantic Services, Interoperability and Web Applications - Emerging Concepts, pp. 104–130, CRC Press, 2011.
Inproceedings
A Simulated Annealing Meta-heuristic for Concept Learning in Description Logics Inproceedings
In: Inductive Logic Programming - 30th International Conference, ILP 2021, Virtual Event, October 25-27, 2021, Proceedings, pp. 266–281, Springer, 2021.
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Ideal Downward Refinement in the emphEL Description Logic Inproceedings
In: Inductive Logic Programming, 19th International Conference, ILP 2009, Leuven, Belgium, July 02-04, 2009. Revised Papers, pp. 73–87, Springer, 2009.
Learning of OWL Class Descriptions on Very Large Knowledge Bases Inproceedings
In: Proceedings of the Poster and Demonstration Session at the 7th International Semantic Web Conference (ISWC2008), Karlsruhe, Germany, October 28, 2008, CEUR-WS.org, 2008.
A Refinement Operator Based Learning Algorithm for the emphALC Description Logic Inproceedings
In: Inductive Logic Programming, 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers, pp. 147–160, Springer, 2007.
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