Consistent query answering is a generally accepted approach for querying inconsistent knowledge bases. A consistent answer to a query is a tuple entailed by every repair, where a repair is a consistent database that "minimally"differs from the original (possibly inconsistent) one. This is a somewhat coarse-grained classification of tuples into consistent and non-consistent does not provide much information about the non-consistent tuples (e.g., a tuple entailed by 99 out of 100 repairs might be considered "almost consistent"). To overcome this limitation, we propose a probabilistic approach to querying inconsistent knowledge bases, which provides more informative query answers by associating a degree of consistency with each query answer by associating a probability to each repair, depending on the changes needed to obtain it.

Probabilistic Answers over Inconsistent Knowledge Bases / Calautti, M.; Fiorentino, N.; Greco, S.; Molinaro, C.; Trubitsyna, I.. - 2646:(2020), pp. 48-55. (Intervento presentato al convegno 28th Italian Symposium on Advanced Database Systems, SEBD 2020 tenutosi a ita nel 2020).

Probabilistic Answers over Inconsistent Knowledge Bases

Calautti M.;
2020-01-01

Abstract

Consistent query answering is a generally accepted approach for querying inconsistent knowledge bases. A consistent answer to a query is a tuple entailed by every repair, where a repair is a consistent database that "minimally"differs from the original (possibly inconsistent) one. This is a somewhat coarse-grained classification of tuples into consistent and non-consistent does not provide much information about the non-consistent tuples (e.g., a tuple entailed by 99 out of 100 repairs might be considered "almost consistent"). To overcome this limitation, we propose a probabilistic approach to querying inconsistent knowledge bases, which provides more informative query answers by associating a degree of consistency with each query answer by associating a probability to each repair, depending on the changes needed to obtain it.
2020
28th Italian Symposium on Advanced Database Systems, SEBD 2020
Aachen
CEUR-WS
Calautti, M.; Fiorentino, N.; Greco, S.; Molinaro, C.; Trubitsyna, I.
Probabilistic Answers over Inconsistent Knowledge Bases / Calautti, M.; Fiorentino, N.; Greco, S.; Molinaro, C.; Trubitsyna, I.. - 2646:(2020), pp. 48-55. (Intervento presentato al convegno 28th Italian Symposium on Advanced Database Systems, SEBD 2020 tenutosi a ita nel 2020).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/275780
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