In previous work, we have introduced probabilistic description logic programs (or pdl-programs), which are a combination of description logic programs (or dl-programs) under the answer set and well-founded semantics with Poole's independent choice logic. Such programs are directed towards sophisticated representation and reasoning techniques that allow for probabilistic uncertainty in the Rules, Logic, and Proof layers of the Semantic Web. In this paper, we continue this line of research. We concentrate on the special case of stratified probabilistic description logic programs (or spdl-programs). In particular, we present an algorithm for query processing in such pdl-programs, which is based on a reduction to computing the canonical model o...
Representing uncertain information is crucial for modeling real world domains. In this paper we pres...
Towards uncertainty reasoning in the Rules, Logic, and Proof layers of the Semantic Web, we present ...
Representing uncertain information is crucial for modeling real world domains. This has been fully r...
Abstract. In previous work, we have introduced probabilistic description logic programs (or pdl-prog...
Towards sophisticated representation and reasoning techniques that allow for probabilistic uncertain...
AbstractTowards sophisticated representation and reasoning techniques that allow for probabilistic u...
We present a semantics for Probabilistic Description Logics that is based on the distribution semant...
Abstract. Creating mappings between ontologies is a common way of approaching the semantic heterogen...
We present a novel approach to probabilistic description logic programs for the Semantic Web in whic...
The combination of logic programming and probability has proven useful for modeling domains with com...
We present a novel approach to probabilistic description logic programs for the Semantic Web in whic...
Abstract. Representing uncertain information is crucial for modeling real world domains. In this pap...
Abstract. We present a novel approach to probabilistic description logic pro-grams for the Semantic ...
Creating mappings between ontologies is a common way of approaching the semantic heterogeneity probl...
AbstractTowards uncertainty reasoning in the Rules, Logic, and Proof layers of the Semantic Web, we ...
Representing uncertain information is crucial for modeling real world domains. In this paper we pres...
Towards uncertainty reasoning in the Rules, Logic, and Proof layers of the Semantic Web, we present ...
Representing uncertain information is crucial for modeling real world domains. This has been fully r...
Abstract. In previous work, we have introduced probabilistic description logic programs (or pdl-prog...
Towards sophisticated representation and reasoning techniques that allow for probabilistic uncertain...
AbstractTowards sophisticated representation and reasoning techniques that allow for probabilistic u...
We present a semantics for Probabilistic Description Logics that is based on the distribution semant...
Abstract. Creating mappings between ontologies is a common way of approaching the semantic heterogen...
We present a novel approach to probabilistic description logic programs for the Semantic Web in whic...
The combination of logic programming and probability has proven useful for modeling domains with com...
We present a novel approach to probabilistic description logic programs for the Semantic Web in whic...
Abstract. Representing uncertain information is crucial for modeling real world domains. In this pap...
Abstract. We present a novel approach to probabilistic description logic pro-grams for the Semantic ...
Creating mappings between ontologies is a common way of approaching the semantic heterogeneity probl...
AbstractTowards uncertainty reasoning in the Rules, Logic, and Proof layers of the Semantic Web, we ...
Representing uncertain information is crucial for modeling real world domains. In this paper we pres...
Towards uncertainty reasoning in the Rules, Logic, and Proof layers of the Semantic Web, we present ...
Representing uncertain information is crucial for modeling real world domains. This has been fully r...