Two approaches to merging knowledge bases

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3229, Issue , 2004, Pages 426-438

  Delgrande, James P ^a   Schaub, Torsten ^a,b  

^a SIMON FRASER UNIVERSITY   (Canada)

^b UNIVERSITY OF POTSDAM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; KNOWLEDGE BASED SYSTEMS; CONSTRAINT THEORY; FORMAL LOGIC; INFORMATION ANALYSIS; MATHEMATICAL MODELS; MATHEMATICAL OPERATORS; SET THEORY;

BELIEF REVISION; BELIEF SETS; BELIEF SOURCES; GENERALISATION; INTEGRITY CONSTRAINTS; KNOWLEDGE BASE; KNOWLEDGE BASIS; MERGING OPERATORS;

MERGING; KNOWLEDGE ENGINEERING;

ARBITRATION OPERATORS; BELIEF REVISION; KNOWLEDGE BASES; MERGE OPERATIONS;

EID: 22944486718     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30227-8_36     Document Type: Conference Paper

References (19)

1. C. Baral, S. Kraus, J. Minker, and V. Subrahmanian. Combining multiple knowledge bases consisting of first order theories. Computational Intelligence, 8(1):45-71, 1992.
2. S. Benferhat, D. Dubois, S. Kaci, and H. Prade. Possibilistic merging and distance-based fusion of propositional information. Annals of Mathematics and Artificial Intelligence, 34(1-3):217-252, 2003.
3. R. Booth. Social contraction and belief negotiation. In D. Fensel, F. Giunchiglia, D. McGuiness, and M. Williams, editors, Proceedings of the Eighth International Conference on the Principlesof KnowledgeRepresentationand Reasoning, pages375-384, SanFrancisco, 2002. Morgan Kaufmann.
4. M. Dalal. Investigations into theory of knowledge base revision. In Proceedings of the AAAI National Conference on Artificial Intelligence, pages 449-479, St. Paul, Minnesota, 1988.
5. J. Delgrande and T. Schaub. A consistency-based approach for belief change. Artificial Intelligence, 151(1-2):1-41, 2003.
6. J. P. Delgrande, T. Schaub, H. Tompits, and S. Woltran. On computing solutions to belief change scenarios. In S. Benferhat and P. Besnard, editors, Proceedings of the Sixth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU-2001), volume 2143 of Lecture Notes in Artificial Intelligence, pages 510-521, Toulouse, Fr., 2001. Springer Verlag.
7. P. Gärdenfors. Knowledge in Flux: Modelling the Dynamics of Epistemic States. The MIT Press, Cambridge, MA, 1988.
8. S. Konieczny, J. Lang, and P. Marquis. Distance-based merging: a general framework and some complexity results. In D. Fensel, F. Giunchiglia, D. McGuiness, and M. Williams, editors, Proceedings of the Eighth International Conference on the Principles of Knowledge Representation and Reasoning, pages 97-108, San Francisco, 2002. Morgan Kaufmann.
9. S. Konieczny and R. Pino Pérez. Merging information under constraints: A logical framework. Journal of Logic and Computation, 12(5):773-808, 2002.
10. Sébastian Konieczny. On the difference between merging knowledge bases and combining them. InA. G. Cohn, F. Giunchiglia, and B. Selman, editors, KR2000: Principles of Knowledge Representation and Reasoning, pages 135-144, San Francisco, 2000. Morgan Kaufmann.
11. Sébastian Konieczny and Ramón Pino Pérez. On the logic of merging. In A. G. Cohn, L. Schubert, and S. C. Shapiro, editors, KR'98: Principles of Knowledge Representation and Reasoning, pages 488-498. Morgan Kaufmann, San Francisco, California, 1998.
12. P. Liberatore and M. Schaerf. Reducing belief revision to circumscription (and vice versa). Artificial Intelligence, 93(1-2):261-296, 1997.
13. P. Liberatore and M. Schaerf. Arbitration (or how to merge knowledge bases). IEEE Transactions on Knowledge and Data Engineering, 10(1):76-90, 1998.
14. J. Lin and A. Mendelzon. Knowledge base merging by majority. In Dynamic Worlds: From the Frame Problem to Knowledge Management. Kluwer, 1999.
15. T. Meyer. On the semantics of combination operations. Journal of Applied NonClassical Logics, 11(1-2):59-84, 2001.
16. R. Reiter. Towards a logical reconstruction of relational database theory. In M. L. Brodie, J. Mylopoulos, and J. W. Schmidt, editors, On Conceptual Modelling, pages 191-233. Springer-Verlag, 1984.
17. P. Revesz. On the semantics of theory change: Arbitration between old and new information. In C. Beeri, editor, Proceedings of the Twelth ACM Symposium on Principles of Database Systems, pages 71-82, Washington D. C., 1993.
18. F. Sadri and R. Kowalski. A theorem-proving approach to database integrity. In J. Minker, editor, Foundations of Deductive Databases and Logic Programming, chapter 9, pages 313-362. Morgan Kaufmann Publishers, 1987.
19. W. Spohn. Ordinal conditional functions: A dynamic theory ofepistemic states. InW. L. Harper and B. Skyrms, editors, Causation in Decision, Belief Change, and Statistics, volume II, pages 105-134. Kluwer Academic Publishers, 1988.