@inbook {89,
	title = {Logics for the Semantic Web},
	booktitle = {Handbook of the History of Logic},
	volume = {9},
	year = {2014},
	pages = {679-710},
	publisher = {Elsevier},
	organization = {Elsevier},
	author = {Pascal Hitzler and Jens Lehmann and Axel Polleres},
	editor = {Dov. M. Gabbay and John Woods and J{\"o}rg Siekmann}
}
@article {80,
	title = {Concept learning in description logics using refinement operators},
	journal = {Machine Learning},
	volume = {78},
	year = {2010},
	pages = {203{\textendash}250},
	abstract = {<p class="rtejustify">With the advent of the Semantic Web, description logics have become one of the most prominent paradigms for knowledge representation and reasoning. Progress in research and applications, however, is constrained by the lack of well-structured knowledge bases consisting of a sophisticated schema and instance data adhering to this schema. It is paramount that suitable automated methods for their acquisition, maintenance, and evolution will be developed. In this paper, we provide a learning algorithm based on refinement operators for the description logic ALCQ including support for concrete roles. We develop the algorithm from thorough theoretical foundations by identifying possible abstract property combinations which refinement operators for description logics can have. Using these investigations as a basis, we derive a practically useful complete and proper refinement operator. The operator is then cast into a learning algorithm and evaluated using our implementation DL-Learner. The results of the evaluation show that our approach is superior to other learning approaches on description logics, and is competitive with established ILP systems.</p>
},
	keywords = {description logics, Inductive logic programming, OWL, refinement operators, Semantic Web, Structured Machine Learning},
	url = {http://springerlink.metapress.com/content/c040n45u15qrnu44/},
	author = {Jens Lehmann and Pascal Hitzler}
}
@article {81,
	title = {Extracting Reduced Logic Programs from Artificial Neural Networks},
	journal = {Applied Intelligence},
	volume = {32},
	year = {2010},
	pages = {249{\textendash}266},
	abstract = {<p class="rtejustify">Artificial neural networks can be trained to perform excellently in many application areas. Whilst they can learn from raw data to solve sophisticated recognition and analysis problems, the acquired knowledge remains hidden within the network architecture and is not readily accessible for analysis or further use: Trained networks are black boxes. Recent research efforts therefore investigate the possibility to extract symbolic knowledge from trained networks, in order to analyze, validate, and reuse the structural insights gained implicitly during the training process. In this paper, we will study how knowledge in form of propositional logic programs can be obtained in such a way that the programs are as simple as possible {\textemdash} where simple is being understood in some clearly defined and meaningful way.</p>
},
	doi = {10.1007/s10489-008-0142-y},
	url = {http://dx.doi.org/10.1007/s10489-008-0142-y},
	author = {Jens Lehmann and Sebastian Bader and Pascal Hitzler}
}
