00496nas a2200181 4500008004100000245003200041210003200073260001300105300001200118490000600130100002000136700001800156700001900174700002100193700001600214700002000230856006400250 2014 eng d00aLogics for the Semantic Web0 aLogics for the Semantic Web bElsevier a679-7100 v91 aHitzler, Pascal1 aLehmann, Jens1 aPolleres, Axel1 aGabbay, Dov., M.1 aWoods, John1 aSiekmann, Jörg uhttps://daselab.cs.ksu.edu/publications/logics-semantic-web01837nas a2200205 4500008004100000245007000041210006900111300001400180490000700194520119100201653002301392653003201415653000801447653002501455653001701480653003201497100001801529700002001547856006401567 2010 eng d00aConcept learning in description logics using refinement operators0 aConcept learning in description logics using refinement operator a203–2500 v783 a
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.
10adescription logics10aInductive logic programming10aOWL10arefinement operators10aSemantic Web10aStructured Machine Learning1 aLehmann, Jens1 aHitzler, Pascal uhttp://springerlink.metapress.com/content/c040n45u15qrnu44/01300nas a2200145 4500008004100000245007000041210006900111300001400180490000700194520084600201100001801047700002101065700002001086856004801106 2010 eng d00aExtracting Reduced Logic Programs from Artificial Neural Networks0 aExtracting Reduced Logic Programs from Artificial Neural Network a249–2660 v323 aArtificial 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 — where simple is being understood in some clearly defined and meaningful way.
1 aLehmann, Jens1 aBader, Sebastian1 aHitzler, Pascal uhttp://dx.doi.org/10.1007/s10489-008-0142-y