TY - GEN
T1 - Learning probabilistic description logics: A framework and algorithms
AU - Ochoa-Luna, José Eduardo
AU - Revoredo, Kate
AU - Cozman, Fábio Gagliardi
PY - 2011/1/1
Y1 - 2011/1/1
N2 - Description logics have become a prominent paradigm in knowledge representation (particularly for the Semantic Web), but they typically do not include explicit representation of uncertainty. In this paper, we propose a framework for automatically learning a Probabilistic Description Logic from data. We argue that one must learn both concept definitions and probabilistic assignments. We also propose algorithms that do so and evaluate these algorithms on real data. © 2011 Springer-Verlag.
AB - Description logics have become a prominent paradigm in knowledge representation (particularly for the Semantic Web), but they typically do not include explicit representation of uncertainty. In this paper, we propose a framework for automatically learning a Probabilistic Description Logic from data. We argue that one must learn both concept definitions and probabilistic assignments. We also propose algorithms that do so and evaluate these algorithms on real data. © 2011 Springer-Verlag.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=82555184018&origin=inward
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=82555184018&origin=inward
U2 - 10.1007/978-3-642-25324-9_3
DO - 10.1007/978-3-642-25324-9_3
M3 - Conference contribution
SN - 9783642253232
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 28
EP - 39
BT - Learning probabilistic description logics: A framework and algorithms
T2 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Y2 - 1 January 2018
ER -