Relational kernel machines for learning from graph-structured RDF data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6643 LNCS, Issue PART 1, 2011, Pages 47-62

  Bicer, Veli ^a   Tran, Thanh ^b   Gossen, Anna ^c  

^a FZI RESEARCH CENTER FOR INFORMATION TECHNOLOGY   (Germany)

^b UNIVERSITY OF KARLSRUHE   (Germany)

^c Fluid Operations AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX STRUCTURE; COMPOSITE KERNELS; DATA SPARSITY; DYNAMIC FEATURES; KERNEL MACHINE; OPEN PROBLEMS; RDF DATA; REAL-WORLD DATASETS; SEMANTIC DATA; SEMANTIC WEB RESEARCH;

ENGINEERING RESEARCH; SEMANTICS; SUPPORT VECTOR MACHINES; USER INTERFACES; WORLD WIDE WEB;

SEMANTIC WEB;

EID: 79960044943     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-21034-1_4     Document Type: Conference Paper

References (11)

1. Bloehdorn, S., Sure, Y.: Kernel methods for mining instance data in ontologies. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudŕe-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 58-71. Springer, Heidelberg(2007)
2. Cristianini, N., Kandola, J., Elisseeff, A., Shawe-Taylor, J.: On kernel target alignment. Innovations in Machine Learning, 205-256 (2006) (Pubitemid 44856919)
3. d'Amato, C., Fanizzi, N., Esposito, F.: Classification and retrieval through semantic kernels. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part III. LNCS (LNAI), vol. 5179, pp. 252-259. Springer, Heidelberg (2008)
4. Fanizzi, N., d'Amato, C., Esposito, F.: Learning with kernels in description logics. In: Železný, F., Lavrač, N. (eds.) ILP 2008. LNCS (LNAI), vol. 5194, pp. 210-225. Springer, Heidelberg (2008)
5. Frasconi, P., Passerini, A.: Learning with kernels and logical representations. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.H. (eds.) Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911, pp. 56-91. Springer, Heidelberg(2008)
6. Haussler, D.: Convolution kernels on discrete structures (Technical Report UCSCCRL-99-10). University of California, Santa Cruz (1999)
7. Kiefer, C., Bernstein, A., Locher, A.: Adding data mining support to SPARQL via statistical relational learning methods. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 478-492. Springer, Heidelberg (2008)
8. Landwehr, N., Passerini, A., De Raedt, L., Frasconi, P.: kFOIL: Learning simple relational kernels. In: Proceedings of the National Conference on Artificial Intelligence, vol. 21, p. 389. AAAI Press, Menlo Park (1999), MIT Press, Cambridge(2006)
9. Quinlan, J.: Learning logical definitions from relations. Machine Learning 5(3), 239-266 (1990)
10. Rakotomamonjy, A., Bach, F., Canu, S., Grandvalet, Y.: More effciency in multiple kernel learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 775-782. ACM, New York (2007)
11. Rettinger, A., Nickles, M., Tresp, V.: Statistical relational learning with formal ontologies. In: Buntine, W., Grobelnik, M., Mladeníc, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5782, pp. 286-301. Springer, Heidelberg (2009)