Inductive learning for the Semantic Web: What does it buy?

Semantic Web

Volumn 1, Issue 1-2, 2010, Pages 53-59

  D'Amato, Claudia ^a   Fanizzi, Nicola ^a   Esposito, Floriana ^a  

^a UNIVERSITY OF BARI   (Italy)

Author keywords

Inductive learning; Ontology mining; Uncertainty

Indexed keywords

EID: 79960032696     PISSN: 15700844     EISSN: 22104968     Source Type: Journal    
DOI: 10.3233/SW-2010-0007     Document Type: Article

References (48)

1. F. Baader, D. Calvanese, D. L. McGuinness, D. Nardi, and P. F. Patel-Schneider, editors. Description Logic Handbook. Cambridge University Press, 2003.
2. F. Baader, B. Ganter, B. Sertkaya, and U. Sattler. Completing description logic knowledge bases using formal concept analysis. In M. Veloso, editor, IJCAI 2007, Proc. of the Int. Joint Conference on Artificial Intelligence, pages 230-235, 2007.
3. T. Berners-Lee, J. Hendler, and O. Lassila. The semantic web. Scientific American Magazine, 2001.
4. J. C. Bezdek and N. R. Nikhil. Cluster validation with generalized dunn's indices. In Proc. of the Int. Conference on Artificial Neural Networks and Expert Systems, ANNES '95, page 190. IEEE Computer Society, 1995.
5. S. Bloehdorn and Y. Sure. Kernel methods for mining instance data in ontologies. In K. Aberer et al., editors, Proc. of the International Semantic Web Conference, volume 4825 of LNCS, pages 58-71. Springer, 2007.
6. P. Buitelaar and P. Cimiano, editors. Ontology Learning and Population: Bridging the Gap between Text and Knowledge, volume 167. Frontiers in Artificial Intelligence and Applications, 2008.
7. F. De Comité, F. Denis, R. Gilleron, and F. Letouzey. Positive and unlabeled examples help learning. In Algorithmic Learning Theory: 10th Int. Conf., ALT'99, volume 1720 of LNCS. Springer, 1999.
8. C. d'Amato, N. Fanizzi, and F. Esposito. Query answering and ontology population: An inductive approach. In S. Bechhofer et al., editors, Proc. of the 5th European Semantic Web Conference, volume 5021 of LNCS, pages 288-302. Springer, 2008.
9. C. d'Amato, N. Fanizzi, B. Fazzinga, G. Gottlob, and T. Lukasiewicz. Combining semantic web search with the power of inductive reasoning. In F. Bobillo et al., editors, Proc. of the International Workshop on Uncertainty Reasoning for the Semantic Web at ISWC 2009, volume 527 of CEUR Workshop Proceedings, pages 15-26. CEUR-WS.org, 2009.
10. C. d'Amato, N. Fanizzi, and T. Lukasiewicz. Tractable reasoning with bayesian description logics. In S. Greco and T. Lukasiewicz, editors, Scalable Uncertainty Management, Second International Conference, SUM 2008, volume 5291 of LNCS, pages 146-159. Springer, 2008.
11. C. d'Amato, F. Nicola, and F. Esposito. Analogical reasoning in description logics. In Uncertainty Reasoning for the Semantic Web I: Revised Selected and Invited Papers, pages 330-347. Springer-Verlag, 2008.
12. N. Fanizzi and C. d'Amato. A declarative kernel for ALC concept descriptions. In F. Esposito et al., editors, Foundations of Intelligent Systems, 16th International Symposium, IS-MIS 2006, Bari, Italy, September 27-29, 2006, Proceedings, volume 4203 of LNCS, pages 322-331. Springer, 2006.
13. N. Fanizzi, C. d'Amato, and F. Esposito. Conceptual clustering and its application to concept drift and novelty detection. In Proc. of the Europ. Semantic Web Conference, volume 5021 of LNCS, pages 318-332. Springer, 2008.
14. N. Fanizzi, C. d'Amato, and F. Esposito. Statistical learning for inductive query answering on owl ontologies. In A. P. Sheth et al., editors, International Semantic Web Conference, volume 5318 of LNCS, pages 195-212. Springer, 2008.
15. N. Fanizzi, C. d'Amato, and F. Esposito. Towards the induction of terminological decision trees. In Proc. of the Symposium on Applied Computing, volume 2, pages 1424-1428. ACM, 2010.
16. Nicola Fanizzi, C. d'Amato, and F. Esposito. Instance-based query answering with semantic knowledge bases. In R. Basili and M. T. Pazienza, editors, AI∗IA 2007: Artificial Intelligence and Human-Oriented Computing, 10th Congress of the Italian Association for Artificial Intelligence, Rome, Italy, September 10-13, 2007, Proceedings, volume 4733 of LNCS, pages 254-265. Springer, 2007.
17. D. H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2:139-172, 1987.
18. B. Ganter and R. Wille, editors. Formal Concept Analysis: Mathematical Foundations. Springer-Verlag, 1998.
19. M. Grobelnik and D. Mladenić. Knowledge discovery for ontology construction. In Semantic web technologies: trends and research in ontology-based systems, pages 9-27. John Wiley and Sons, 2006.
20. H. Guo and V. L. Herna. Learning from imbalanced data sets with boosting and data generation: the databoost-im approach. SIGKDD Explor. Newsl., 6(1):30-39, 2004.
21. M. Hadzikadic and D. Y. Y. Yun. Concept formation by incremental conceptual clustering. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI'89), pages 831-836, 1989.
22. J. Hipp, U. Güntzer, and G. Nakhaeizadeh. Algorithms for association rule mining -a general survey and comparison. SIGKDD Explorations, 2(2):1-58, 2000.
23. L. Iannone, I. Palmisano, and N. Fanizzi. An algorithm based on counterfactuals for concept learning in the semantic web. Appl. Intell., 26(2):139-159, 2007.
24. A. K. Jain, M. N. Murty, and P. J. Flynn. Data clustering: A review. ACM Computing Surveys, 31(3):264-323, 1999.
25. L. Kaufman and P. J. Rousseeuw, editors. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley and Sons, 1990.
26. J. Kietz and K. Morik. A polynomial approach to the constructive induction of structural knowledge. Machine Learning, 14(1):193-217, 1994.
27. M. C. A. Klein, P. Mika, and S. Schlobach. Rough description logics for modeling uncertainty in instance unification. In F. Bobillo et al., editors, Proc. of the 3rd ISWC Workshop on Uncertainty Reasoning for the Semantic Web, volume 327 of CEUR Workshop Proceedings. CEUR-WS.org, 2008.
28. J. Lehmann and P. Hitzler. Concept learning in description logics using refinement operators. Machine Learning, 78:203-250, 2010.
29. X. Liu, J. Wu, and Z. Zhou. Exploratory under-sampling for class-imbalance learning. In Proc. of the Int. Conf. on Data Mining, pages 965-969. IEEE Computer Society, 2006.
30. T. Lukasiewicz. Expressive probabilistic description logics. Artif. Intell., 172(6-7):852-883, 2008.
31. T. Lukasiewicz. Uncertainty reasoning for the semantic web. In Web Reasoning and Rule Systems, Int. Conf., volume 5837 of LNCS, pages 26-39. Springer, 2009.
32. T. Lukasiewicz and U. Straccia. Managing uncertainty and vagueness in description logics for the semantic web. J. Web Sem., 6(4):291-308, 2008.
33. A. Maedche and S. Staab. Ontology learning for the semantic web. IEEE Intelligent Systems, 16(2):72-79, 2001.
34. C. D. Manning and H. Schütze, editors. Foundations of Statistical Natural Language Processing. MIT Press, 1999.
35. R. S. Michalski, I. Mozetic, J. Hong, and N. Lavrac. The multipurpose incremental learning system aq15 and its testing appli-cation to three medical domains. In AAAI, pages 1041-1047, 1986.
36. R. S. Michalski and R. E. Stepp. Automated construction of classifications: Conceptual clustering versus numerical taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5:396-410, 1983.
37. T. Mitchell, editor. Machine Learning. McGraw Hill, 1997.
38. P. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math., 20(1):53-65, 1987.
39. N. Shadbolt, T. Berners-Lee, and H. Hall. The semantic web revisited. IEEE Intelligent Systems, 21(3):96-101, 2006.
40. J. Shawe-Taylor and N. Cristianini, editors. Support Vector Machines and other kernel-based learning methods. Cambridge University Press, 2000.
41. M. Spiliopoulou, I. Ntoutsi, Y. Theodoridis, and R. Schult. Monic: modeling and monitoring cluster transitions. In Proc. of the Int. Conf. on Knowledge Discovery and Data Mining, pages 706-711. ACM, 2006.
42. U. Straccia. Reasoning within fuzzy description logics. J. Artif. Intell. Res. (JAIR), 14:137-166, 2001.
43. N. A. Syed, H. Liu, S. Huan, L. K., and K. Sung. Handling concept drifts in incremental learning with support vector machines. In Proc. of the Int. Conf. on Knowledge Discovery and Data Mining, pages 317-321. ACM, 1999.
44. V. Tresp, M. Bundschus, A. Rettinger, and Y. Huang. Towards machine learning on the semantic web. In P. C. G. da Costa et al., editors, Uncertainty Reasoning for the Semantic Web I: Revised Selected and Invited Papers, volume 5327 of LNCS, pages 282-314. Springer, 2008.
45. J. Völker, P. Hitzler, and P. Cimiano. Acquisition of owl dl axioms from lexical resources. In Proc. of the European Semantic Web Conference (ESWC 2007), LNCS, pages 670-685. Springer, 2007.
46. J. Völker, D. Vrandecic, Y. Sure, and A. Hotho. Learning dis-jointness. In Proc. of the European Semantic Web Conference (ESWC 2007), LNCS, pages 175-189. Springer, 2007.
47. P. Yao. Comparative study on class imbalance learning for credit scoring. Hybrid Intelligent Systems, International Conference on, 2:105-107, 2009.
48. B. Zhang and W. Zuo. Learning from positive and unlabeled examples: A survey. In International Symposium on Information Processing, pages 650-654, 2008.