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414–428 (2007) 13. : Efficient top-k query evaluation on probabilistic data. In: Proc. ICDE-2007, pp. 886–895 (2007) 14. : Top-k query processing in uncertain databases. In: Proc. ICDE-2007, pp. 896–905 (2007) 15. : Answering vague queries in fuzzy DL-Lite. In: Proc. IPMU-2006, pp. 2238– 2245 (2006) 16. : Towards top-k query answering in deductive databases. In: Proc. SMC-2006, pp. 4873–4879 (2006) 17. : Towards top-k query answering in description logics: The case of DL-Lite. , Lisitsa, A. ) JELIA 2006.

3. Active Q P Q Q 4. P 5. 2 Fig. 10. Facts and computation of Example 11 5 Related Work While there are many works addressing the top-k problem for vague queries in databases (cf. [1,3,5,4,6,7,9,8,10]), little is known for the corresponding problem in knowledge representation and reasoning. For instance, [21] considers non-recursive fuzzy logic programs in which the score combination function is a function of the score of the atoms in the body only (no expensive fuzzy predicates are allowed). The work [16] considers non-recursive fuzzy logic programs as well, though the score combination function may consider expensive fuzzy predicates.

If logic operators appear, the calculation of this compatibility degree is carried out as table 3 shows. We use the minimum T-norm and the maximum T-conorm, but the user may change these values by modifying a view (FSQL_NOTANDOR). In this view the user can set the function to be used for every logic operator (NOT, AND, OR). Obviously, that function must be implemented in the FSQL Server or by the user himself. Table 3. Default CDEG computation with logic operators AND OR NOT CDEG() min(CDEG(),CDEG()) max(CDEG(),CDEG()) 1 - CDEG() – Fuzzy Constants: We can use the fuzzy constants detailed in table 4.

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A density lemma


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