Querying factorized probabilistic triple databases

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

Volumn 8797, Issue , 2014, Pages 114-129

  Krompaß, Denis ^a   Nickel, Maximilian ^b,c   Tresp, Volker ^a,d  

^a UNIVERSITY OF MUNICH   (Germany)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c ISTITUTO ITALIANO DI TECNOLOGIA   (Italy)

^d SIEMENS AG   (Germany)

Author keywords

Extensional query evaluation; Probabilistic databases; Querying; RESCAL; Tensor factorization

Indexed keywords

DATABASE SYSTEMS; OPEN DATA; PROBABILITY DISTRIBUTIONS; QUERY PROCESSING;

PROBABILISTIC DATABASE; QUERY EVALUATION; QUERYING; RESCAL; TENSOR FACTORIZATION;

SEMANTIC WEB;

EID: 84910093816     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-11915-1_8     Document Type: Conference Paper

References (31)

1. Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: AAAI (2011)
2. Boulos, J., Dalvi, N.N., Mandhani, B., Mathur, S., Ré, C., Suciu, D.: Mystiq: a system for finding more answers by using probabilities. In: SIGMOD Conference, pp. 891–893 (2005)
3. Calì, A., Lukasiewicz, T., Predoiu, L., Stuckenschmidt, H.: Tightly integrated probabilistic description logic programs for representing ontology mappings. In: Hartmann, S., Kern-Isberner, G. (eds.) FoIKS 2008. LNCS, vol. 4932, pp. 178–198. Springer, Heidelberg (2008)
4. da Costa, P.C.G., Laskey, K.B., Laskey, K.J.: Pr-owl: A bayesian ontology language for the semantic web. In: da Costa, P.C.G., d’Amato, C., Fanizzi, N., Laskey, K.B., Laskey, K.J., Lukasiewicz, T., Nickles, M., Pool, M. (eds.) URSW 2005 - 2007. LNCS (LNAI), vol. 5327, pp. 88–107. Springer, Heidelberg (2008)
5. Dalvi, N.N., Re, C., Suciu, D.: Queries and materialized views on probabilistic databases. J. Comput. Syst. Sci. 77(3), 473–490 (2011)
6. Ding, Z., Peng, Y., Pan, R.: A bayesian approach to uncertainty modelling in owl ontology. In: Proceedings of the International Conference on Advances in Intelligent Systems - Theory and Applications (2004)
7. Dylla, M., Miliaraki, I., Theobald, M.: Top-k query processing in probabilistic databases with non-materialized views. Research Report MPI-I-2012-5-002, Max- Planck-Institut f¨ur Informatik, Stuhlsatzenhausweg 85, 66123 Saarbr¨ucken, Germany (June 2012)
8. Franz, T., Schultz, A., Sizov, S., Staab, S.: Triplerank: Ranking semantic web data by tensor decomposition. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 213–228. Springer, Heidelberg (2009)
9. Giugno, R., Lukasiewicz, T.: P-SHOQ(D): A probabilistic extension of SHOQ(D) for probabilistic ontologies in the semantic web. In: Flesca, S., Greco, S., Leone, N., Ianni, G. (eds.) JELIA 2002. LNCS (LNAI), vol. 2424, pp. 86–97. Springer, Heidelberg (2002)
10. Huang, J., Antova, L., Koch, C., Olteanu, D.: Maybms: a probabilistic database management system. In: SIGMOD Conference (2009)
11. Jenatton, R., Roux, N.L., Bordes, A., Obozinski, G.: A latent factor model for highly multi-relational data. In: NIPS (2012)
12. Kolda, T.G., Bader, B.W., Kenny, J.P.: Higher-order web link analysis using multilinear algebra. In: ICDM, pp. 242–249 (2005)
13. Laub, A.J.: Matrix analysis - for scientists and engineers. SIAM (2005)
14. Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia - a largescale, multilingual knowledge base extracted from wikipedia. Semantic Web Journal (2014)
15. Lukasiewicz, T.: Expressive probabilistic description logics. Artif. Intell. 172(6-7), 852–883 (2008)
16. Mutsuzaki, M., Theobald, M., de Keijzer, A., Widom, J., Agrawal, P., Benjelloun, O., Sarma, A.D., Murthy, R., Sugihara, T.: Trio-one: Layering uncertainty and lineage on a conventional dbms (demo). In: CIDR, pp. 269–274 (2007)
17. Nickel, M.: Tensor factorization for relational learning. PhDThesis, p. 48, 49, 74, Ludwig-Maximilian-University of Munich (August 2013)
18. Nickel, M., Tresp, V.: Logistic tensor factorization for multi-relational data. In: Structured Learning: Inferring Graphs from Structured and Unstructured Inputs, ICML WS (2013)
19. Nickel, M., Tresp, V., Kriegel, H.-P.: A three-way model for collective learning on multi-relational data. In: ICML, pp. 809–816 (2011)
20. Nickel, M., Tresp, V., Kriegel, H.-P.: Factorizing yago: scalable machine learning for linked data. In: WWW, pp. 271–280 (2012)
21. Olteanu, D., Wen, H.: Ranking query answers in probabilistic databases: Complexity and efficient algorithms. In: ICDE, pp. 282–293 (2012)
22. Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers, pp. 61–74. MIT Press (1999)
23. Rendle, S., Marinho, L.B., Nanopoulos, A., Schmidt-Thieme, L.: Learning optimal ranking with tensor factorization for tag recommendation. In: KDD, pp. 727–736 (2009)
24. Riedel, S., Yao, L., McCallum, A., Marlin, B.M.: Relation extraction with matrix factorization and universal schemas. In: HLT-NAACL, pp. 74–84 (2013)
25. Christopher, R., Dalvi, N., Suciu, D.: Efficient top-k query evaluation on probabilistic data. In: ICDE, pp. 886–895 (2007)
26. Singh, S., Mayfield, C., Mittal, S., Prabhakar, S., Hambrusch, S.E., Shah, R.: Orion 2.0: native support for uncertain data. In: SIGMOD Conference, pp. 1239–1242 (2008)
27. Suciu, D., Olteanu, D., Ré, C., Koch, C.: Probabilistic Databases. Synthesis Lectures on Data Management. Morgan & Claypool Publishers (2011)
28. Theobald, M., De Raedt, L., Dylla, M., Kimmig, A., Miliaraki, I.: 10 years of probabilistic querying - what next? In: Catania, B., Guerrini, G., Pokorn´y, J. (eds.) ADBIS 2013. LNCS, vol. 8133, pp. 1–13. Springer, Heidelberg (2013)
29. Tresp, V., Huang, Y., Bundschus, M., Rettinger, A.: Materializing and querying learned knowledge. In: First ESWCWorkshop on Inductive Reasoning andMachine Learning on the Semantic Web (IRMLeS 2009) (2009)
30. Wermser, H., Rettinger, A., Tresp, V.: Modeling and learning context-aware recommendation scenarios using tensor decomposition. In: ASONAM, pp. 137–144 (2011)
31. Yang, Y., Calmet, J.: Ontobayes: An ontology-driven uncertainty model. In: CIMCA/IAWTIC, pp. 457–463 (2005)