Probabilistic Query Answering in the Bayesian Description Logic BEL

被引:14
|
作者
Ceylan, Ismail Ilkan [1 ]
Penaloza, Rafael [2 ]
机构
[1] Tech Univ Dresden, Theoret Comp Sci, Dresden, Germany
[2] Free Univ Bozen Bolzano, KRDB Res Ctr, Bolzano, Italy
来源
SCALABLE UNCERTAINTY MANAGEMENT (SUM 2015) | 2015年 / 9310卷
关键词
INFORMATION;
D O I
10.1007/978-3-319-23540-0_2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
BEL is a probabilistic description logic (DL) that extends the light-weight DL EL with a joint probability distribution over the axioms, expressed with the help of a Bayesian network (BN). In recent work it has been shown that the complexity of standard logical reasoning in BEL is the same as performing probabilistic inferences over the BN. In this paper we consider conjunctive query answering in BEL. We study the complexity of the three main problems associated to this setting: computing the probability of a query entailment, computing the most probable answers to a query, and computing the most probable context in which a query is entailed. In particular, we show that all these problems are tractable w.r.t. data and ontology complexity.
引用
收藏
页码:21 / 35
页数:15
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