I. Horrocks, P. F. Patel-schneider, . Boley, . Harold, . Tabet et al., Swrl: A semantic web rule language combining owl and ruleml, vol.21, p.39, 2004.

P. Abele, J. P. Mccrae, and R. Cyganiak,

R. Agrawal, T. Imieli?ski, and A. Swami, Mining association rules between sets of items in large databases, SIGMOD Rec, vol.22, issue.2, pp.207-216, 1993.

P. J. Azevedo and A. M. Jorge, Comparing rule measures for predictive association rules, Machine Learning: ECML 2007, p.15, 2007.

R. J. Bayardo, J. , and R. Agrawal, Mining the most interesting rules, Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '99, p.15, 1999.

F. Baader, D. Calvanese, D. L. Mcguinness, D. Nardi, and P. F. Patel-schneider, The Description Logic Handbook: Theory, Implementation, and Applications, 2003.

L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and regression trees, p.37, 1984.

R. Sergey-brin, J. D. Motwani, S. Ullman, and . Tsur, Dynamic itemset counting and implication rules for market basket data, SIGMOD Rec, vol.26, issue.2, p.36, 1997.

P. Clark and R. Boswell, Rule induction with cn2: Some recent improvements, p.36, 1991.

K. Dejong, Evolutionary Computation: A unified approach, p.66, 2002.

A. Delteil, C. Faron-zucker, and R. Dieng, Learning Ontologies from RDF annotations, IJCAI 2001 Workshop on Ontology Learning, Proceedings of the Second Workshop on Ontology Learning OL, vol.38, 2001.

F. Divina, Evolutionary concept learning in first order logic: An overview, AI Commun, vol.19, issue.1, p.14, 2006.

F. Divina and E. Marchiori, Evolutionary concept learning, GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, p.14, 2002.

S. D?eroski, Advances in knowledge discovery and data mining. chapter Inductive Logic Programming and Knowledge Discovery in Databases, pp.117-152, 1996.

A. Eiben and J. Smith, Introduction to Evolutionary Computing, p.66, 2003.

N. Fanizzi, C. Amato, and F. Esposito, Learning with kernels in description logics, p.37, 2008.
DOI : 10.1007/978-3-540-85928-4_18

URL : http://www.di.uniba.it/~cdamato/ILP2008-cameraReady.pdf

N. Fanizzi, C. Amato, and F. Esposito, Dl-foil concept learning in description logics, Filip ?elezný and Nada Lavra?, p.13, 2008.
DOI : 10.1007/978-3-540-85928-4_12

URL : http://www.di.uniba.it/%7Ecdamato/ILP2008-DL-FOIL.pdf

L. M. Fu and E. H. Shortliffe, The application of certainty factors to neural computing for rule discovery, IEEE TRANS. On Neural Networks, p.36, 2000.

P. Gunnar-aastrand-grimnes, A. Edwards, and . Preece, Learning meta-descriptions of the foaf network, The Semantic WebISWC 2004, p.12, 2004.

D. E. Goldberg, Genetic Algorithms in Search, Optimization & Machine Learning, p.66, 1989.

L. Antonio-galárraga, C. Teflioudi, K. Hose, and F. Suchanek, Amie: Association rule mining under incomplete evidence in ontological knowledge bases, Proceedings of the 22Nd International Conference on World Wide Web, WWW '13, pp.413-422, 2013.

I. Horrocks, O. Kutz, and U. Sattler, The even more irresistible sroiq, Proceedings of the Tenth International Conference on Principles of Knowledge Representation and Reasoning, KR'06, pp.57-67, 2006.

S. Hellmann, J. Lehmann, and S. Auer, Learning of owl class descriptions on very large knowledge bases, Proceedings of the 2007 International Conference on Posters and Demonstrations, vol.401, pp.102-103, 2008.
DOI : 10.4018/jswis.2009040102

URL : http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-401/iswc2008pd_submission_83.pdf

J. H. Holland, Adaptation in Natural and Artificial Systems, p.66, 1975.

I. Horrocks and P. F. Patel-schneider, A proposal for an owl rules language, Proceedings of the 13th International Conference on World Wide Web, WWW '04, pp.723-731, 2004.
DOI : 10.1145/988672.988771

URL : http://www.www2004.org/proceedings/docs/1p723.pdf

I. Horrocks, F. Peter, H. Patel-schneider, S. Boley, B. Tabet et al., SWRL: A semantic web rule language combining OWL and RuleML. W3c member submission, World Wide Web Consortium, 2004.

J. Józefowska, A. Lawrynowicz, and T. ,

. Lukaszewski, The role of semantics in mining frequent patterns from knowledge bases in description logics with rules, TPLP, vol.10, issue.3, pp.251-289, 2010.

J. Józefowska, A. Lawrynowicz, and T. ,

. Lukaszewski, The role of semantics in mining frequent patterns from knowledge bases in description logics with rules. CoRR, abs/1003.2700, vol.54, p.30, 2010.

G. Klyne and J. J. Carroll, Resource description framework (rdf): Concepts and abstract syntax. W3C Recommendation, 2004.

J. Lehmann, Diploma Thesis in Computer Science, supervisors: Dr. habil, 2006.

J. Lehmann, Dl-learner: Learning concepts in description logics, J

, Mach. Learn. Res, vol.10, p.13, 2009.

J. Lehmann and P. Hitzler, Foundations of refinement operators for description logics, Inductive Logic Programming, 17th International Conference, pp.161-174, 2007.

S. Muggleton, D. Luc-de-raedt, I. Poole, P. Bratko, K. Flach et al., Ilp turns 20. Machine Learning, vol.86, pp.3-23, 2012.
DOI : 10.1007/s10994-011-5259-2

URL : https://link.springer.com/content/pdf/10.1007%2Fs10994-011-5259-2.pdf

S. Muggleton and L. De-raedt, Inductive logic programming: Theory and methods, J. Log. Program, vol.19, pp.629-679, 1994.
DOI : 10.1016/0743-1066(94)90035-3

URL : https://doi.org/10.1016/0743-1066(94)90035-3

A. Maedche and S. Staab, Ontology learning, HANDBOOK ON ONTOLOGIES, vol.12, pp.173-189

B. Motik, U. Sattler, and R. Studer, Query answering for owl-dl with rules, Web Semant, vol.3, issue.1, pp.41-60, 2005.
DOI : 10.1016/j.websem.2005.05.001

S. Muggleton, Inductive logic programming, New Gen. Comput, vol.8, issue.4, p.19, 1991.

D. Mun, Knowledge-based part similarity measurement utilizing ontology and multi-criteria decision making technique. Advanced Engineering Informatics, 2010.
DOI : 10.1016/j.aei.2010.07.003

A. Maedche and V. Zacharias, Clustering ontologybased metadata in the semantic web
DOI : 10.1007/3-540-45681-3_29

URL : https://link.springer.com/content/pdf/10.1007%2F3-540-45681-3_29.pdf

H. Mannila and . Toivonen, Principles of Data Mining and Knowledge Discovery, pp.348-360, 2002.

H. Springer-berlin, , vol.12

V. Nebot and R. Berlanga, Finding association rules in semantic web data. Know.-Based Syst, vol.25, pp.51-62, 2012.
DOI : 10.1016/j.knosys.2011.05.009

-. Shan-hwei-nienhuys, R. Cheng, and . Wolf, Foundations of Inductive Logic Programming, 1997.

M. Obitko,

K. Ohamad-saraee-qudamah and . Quboa, A state-of-the-art survey on semantic web mining, Intelligent Information Management, vol.5, issue.1, 2013.

P. Smyth and R. M. Goodman, Rule induction using information theory, p.37, 1991.

G. Stumme, A. Hotho, and B. Berendt, Semantic web mining, Web Semant, vol.4, issue.2, p.11, 2006.

S. Sahar and Y. Mansour, An empirical evaluation of objective interestingness criteria, SPIE Conference on Data mining and Knowledge Discovery, p.36, 1999.

E. Sirin and B. Parsia, Sparql-dl: Sparql query for owl-dl, 3rd OWL Experiences and Directions Workshop, vol.101, p.100, 2007.

E. Sirin, B. Parsia, B. C. Grau, A. Kalyanpur, and Y. Katz, Pellet: A practical owl-dl reasoner, Web Semant, vol.5, issue.2, pp.51-53, 2007.
DOI : 10.2139/ssrn.3199351

G. B. Andrea and . Tettamanzi, Catherine Faron-Zucker, and Fabien Gandon. Testing owl axioms against rdf facts: A possibilistic approach

, Knowledge Engineering and Knowledge Management, pp.519-530, 2014.

V. Pang-ning-tan, J. Kumar, and . Srivastava, Selecting the right objective measure for association analysis, Inf. Syst, vol.29, issue.4, p.14, 2004.

B. L. Shivakumar and T. Raji, A survey on semantic web mining technologies and web mining tools, International Journal of Advanced Computational Engineering and Networking, vol.4, 2016.

J. Völker and M. Niepert, Statistical schema induction, Proceedings of the 8th Extended Semantic Web Conference on The Semantic Web: Research and Applications-Volume Part I, ESWC'11, pp.124-138, 2011.