Ontology-aware prediction from rules: A reconciliation-based approach

Knowledge-Based Systems

Volumn 67, Issue , 2014, Pages 117-130

  Saïs, Fatiha ^a   Thomopoulos, Rallou ^a,b,c  

^a UNIVERSITÉ PARIS SACLAY   (France)

^b UMR IATE   (France)

^c De Robotique et de Mi croélectronique de Montpellier   (France)

Author keywords

Case based reasoning; Data reconciliation; Information integration; Prediction; Reasoning from knowledge

Indexed keywords

DECISION SUPPORT SYSTEMS; DECISION TREES;

CLASSICAL APPROACH; DATA RECONCILIATION; DECISION SUPPORTS; EXPERIMENTAL CONDITIONS; HOMOGENEOUS STRUCTURE; INFORMATION INTEGRATION; REASONING FROM KNOWLEDGE; SCIENTIFIC PUBLICATIONS;

FORECASTING;

EID: 84904752597     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2014.05.023     Document Type: Article

References (49)

1. H.J. Levesque Incompleteness in knowledge bases SIGPLAN Not. 16 1 1981 150 152 http://dx.doi.org/10.1145/960124.806905
2. P. Buche, C. Dervin, O. Haemmerlé, and R. Thomopoulos Fuzzy querying of incomplete, imprecise, and heterogeneously structured data in the relational model using ontologies and rules IEEE Trans. Fuzzy Syst. 13 3 2005 373 383
3. P. Buche, J. Dibie-Barthélemy, O. Haemmerlé, M. Houhou, Towards flexible querying of xml imprecise data in a dataware house opened on the web, in: FQAS, 2004, pp. 28-40.
4. T. Gabaldon, and M. Huynen Prediction of protein function and pathways in the genome era Cell. Mol. Life Sci. 61 7-8 2004 930 944
5. F. Saïs, N. Pernelle, and M.-C. Rousset Combining a logical and a numerical method for data reconciliation J. Data Semant. (JoDS) 12 2009 66 94 (lNCS 5480)
6. F. Saïs, R. Thomopoulos, Reference fusion and flexible querying, in: ODBASE-OTM Conferences (2), 2008, pp. 1541-1549.
7. F. Saïs, N. Pernelle, M.-C. Rousset, L2r: a logical method for reference reconciliation, in: AAAI, 2007, pp. 329-334.
8. J. Zhang, A. Silvescu, and V. Honavar Ontology-driven induction of decision trees at multiple levels of abstraction Lect. Notes Comput. Sci. 2002 316 323
9. R. Thomopoulos, S. Destercke, B. Charnomordic, I. Johnson, and J. Abécassis An iterative approach to build relevant ontology-aware data-driven models Inf. Sci. 221 2013 452 472
10. J.P. Haton, M.T. Keane, M. Manago (Eds.), Advances in Case-Based Reasoning, Second European Workshop, EWCBR-94, Chantilly, France, November 7-10, 1994, Selected Papers, vol. 984 of LNCS, Springer, 1995.
11. A. Aamodt, and E. Plaza Case-based reasoning: foundational issues, methodological variations, and system approaches AI Commun. 7 1 1994 39 59
12. C.K. Riesbeck, and R.C. Schank Inside Case-Based Reasoning 1989 Lawrence Erbaum Associates, Inc. Hillsdale, New Jersey
13. A. Aamodt, Knowledge-intensive case-based reasoning and sustained learning, in: ECAI, 1990, pp. 1-6.
14. A. Aamodt Knowledge-intensive case-based reasoning in creek P. Funk, P.A. González-Calero, ECCBR LNCS vol. 3155 2004 Springer 1 15
15. J. Kolodner Case-Based Reasoning 1993 Morgan Kaufmann
16. M. d'Aquin, J. Lieber, and A. Napoli Case-based reasoning within semantic web technologies J. Euzenat, J. Domingue, AIMSA LNCS vol. 4183 2006 Springer 190 200
17. P. Koton, Reasoning about evidence in causal explanations, in: AAAI, 1988, pp. 256-263.
18. B. López, and E. Plaza Case-based planning for medical diagnosis H.J. Komorowski, Z.W. Ras, ISMIS LNCS vol. 689 1993 Springer 96 105
19. I.P. Fellegi, and A.B. Sunter A theory for record linkage J. Am. Stat. Assoc. 64 328 1969 1183 1210
20. W.E. Winkler, Overview of Record Linkage and Current Research Directions, Tech. rep., Statistical Research Division U.S. Census Bureau Washington, DC, 20233, 2006.
21. X. Dong, A. Halevy, and J. Madhavan Reference reconciliation in complex information spaces ACM SIGMOD 2005 ACM Press 85 96 http://dx.doi.org/10.1145/ 1066157.1066168
22. O. Hassanzadeh, A. Kementsietsidis, L. Lim, R.J. Miller, M. Wang, A framework for semantic link discovery over relational data, in: ACM CIKM Conference, Hong Kong, China, 2009, pp. 1027-1036.
23. G. Stumme, A. Hotho, and B. Berendt Semantic web mining: state of the art and future directions J. Web Semant. 4 2006 124 143
24. N. Maillot, and M. Thonnat Ontology based complex object recognition Image Vision Comput. 26 2008 102 113
25. C. Schmitz, A. Hotho, R. Jäschke, and G. Stumme Content aggregation on knowledge bases using graph clustering Y. Sure, J. Domingue, ESWC LNCS vol. 4011 2006 Springer 530 544
26. N. Fanizzi, C. d'Amato, and F. Esposito A multi-relational hierarchical clustering method for datalog knowledge bases A. An, S. Matwin, Z.W. Ras, D. Slezak, ISMIS LNCS vol. 4994 2008 Springer 137 142
27. C.-Y. Fan, P.-C. Chang, J.-J. Lin, and J. Hsieh A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification Appl. Soft Comput. 11 1 2011 632 644
28. W. Tang, K.Z. Mao, L.O. Mak, and G.W. Ng Adaptive fuzzy rule-based classification system integrating both expert knowledge and data Proceedings of the 2012 IEEE 24th International Conference on Tools with Artificial Intelligence ICTAI '12 vol. 01 2012 IEEE Computer Society Washington, DC, USA 814 821
29. L. Breiman Classification and Regression Trees 1984 Chapman & Hall/CRC Boca Raton
30. R. Ihaka, and R. Gentleman R: a language for data analysis and graphics J. Comput. Graph. Stat. 5 3 1996 299 314
31. W.W. Cohen, P. Ravikumar, S.E. Fienberg, A comparison of string distance metrics for name-matching tasks, in: Proceedings of IJCAI-03 Workshop on Information Integration, 2003, pp. 73-78.
32. Z. Wu, and M. Palmer Verbs semantics and lexical selection Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics 1994 Association for Computational Linguistics Morristown, NJ, USA 133 138 http://dx.doi.org/10.3115/981732.981751
33. N. Pernelle, F. Saïs, and D. Symeonidou An automatic key discovery approach for data linking J. Web Sem. 23 2013 16 30
34. D.Z. Wang, X.L. Dong, A.D. Sarma, M.J. Franklin, A.Y. Halevy, Functional dependency generation and applications in pay-as-you-go data integration systems, in: WebDB, 2009.
35. M.-J. Lesot, M. Rifqi, and H. Benhadda Similarity measures for binary and numerical data: a survey Int. J. Knowl. Eng. Soft Data Paradigm 1 2009 63 84
36. D. Lin An Information-Theoretic Definition of Similarity ICML 1998 Morgan Kaufmann Publishers Inc. San Francisco, CA, USA 296 304
37. M. Ehrig, Similarity for ontologies - a comprehensive framework, in: 13th European Conference on Information Systems, 2005.
38. S. Choi, S. Cha, and C. Tappert A survey of binary similarity and distance measures J. Syst. Cybern. Inf. 8 2010 43 48
39. M.-J. Lesot, Similarity, typicality and fuzzy prototypes for numerical data, J. Res-Syst. (Special issue on the 6th European Congress on Systems Science 5).
40. D. Dubois, and H. Prade On the use of aggregation operations in information fusion processes Fuzzy Sets Syst. 142 1 2004 143 161
41. S. Coste-Marquis, C. Devred, S. Konieczny, M.-C. Lagasquie-Schiex, and P. Marquis On the merging of dung's argumentation systems Artif. Intell. 171 10-15 2007 730 753
42. G. Dalbon, D. Grivon, M. Pagnani, Continuous manufacturing process, in: J. Kruger, R. Matsuo, J. Dick (Eds.), Pasta and noodle technology, AACC, St Paul (MN-USA), 1996.
43. R. Thomopoulos, J.-F. Baget, O. Haemmerlé, Conceptual graphs as cooperative formalism to build and validate a domain expertise, in: ICCS, 2007, pp. 112-125.
44. M. Peck, T. Roberts, J. Sutherland, and S. Walker Modelling the growth, survival and death of microorganisms in foods: the UK Food Micromodel approach Int. J. Food Microbiol. 23 1994 265 275
45. J. Baranyi, M. Tamplin, A new international effort for the advance of predictive microbiology, ComBase: combined database of microbial responses to food environments, in: ICPMF'03, Quimper, France, 2003, pp. 50-51.
46. O. Haemmerlé, P. Buche, and R. Thomopoulos The MIEL system: uniform interrogation of structured and weakly-structured imprecise data J. Intell. Inf. Syst. 29 3 2007 279 304
47. N. Mueangdee, F. Mabille, R. Thomopoulos, J. Abecassis, Virtual grain: a data warehouse for mesh grid representation of cereal grain properties, in: Proceedings of the 9th European Conference on Food Industry and Statistics, Agrostat'2006, Montpellier, France, 2006, pp. 291-299.
48. L. Young Application of baking knowledge in software systems Technology of Breadmaking second ed. 2007 Springer US 207 222
49. A. Ndiaye, G.D. Valle, and P. Roussel Qualitative modelling of a multi-step process: the case of French breadmaking Expert Syst. Appl. 36 2, Part 1 2009 1020 1038