Abstract
There has been a significant amount of interest in recent years on how to reason about inconsistent knowledge bases. However, with the exception of three papers by Lozinskii, Hunter and Konieczny and by Grant and Hunter, there has been almost no work on characterizing the degree of dirtiness of a database. One can conceive of many reasonable ways of characterizing how dirty a database is. Rather than choose one of many possible measures, we present a set of axioms that any dirtiness measure must satisfy. We then present several plausible candidate dirtiness measures from the literature (including those of Hunter-Konieczny and Grant-Hunter) and identify which of these satisfy our axioms and which do not. Moreover, we define a new dirtiness measure which satisfies all of our axioms.
Funded in part by grant N6133906C0149, ARO grant DAAD190310202, AFOSR grants FA95500610405 and FA95500510298, and NSF grant 0540216.
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Martinez, M.V., Pugliese, A., Simari, G.I., Subrahmanian, V.S., Prade, H. (2007). How Dirty Is Your Relational Database? An Axiomatic Approach. In: Mellouli, K. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2007. Lecture Notes in Computer Science(), vol 4724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75256-1_12
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DOI: https://doi.org/10.1007/978-3-540-75256-1_12
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