Add to ORKGInternational audienceIn this paper, we investigate a principled approach for defining and discovering probabilistic mappings between two taxonomies. First, we compare two ways of modeling probabilistic mappings which are compatible with the logical constraints declared in each taxonomy. Then we describe a generate and test algorithm which minimizes the number of calls to the probability estimator for determining those mappings whose probability exceeds a certain threshold. Finally, we provide an experimental analysis of this approach
In Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the ...
Abstract. Semantic web ontologies are based on crisp logic, and do not provide well-defined means fo...
KEY MESSAGE: Probabilistic graphical models show great potential for robust and reliable constructio...
International audienceIn this paper, we investigate a principled approach for defining and discoveri...
Abstract. In this paper, we investigate a principled approach for defin-ing and discovering probabil...
National audienceIn this paper, we investigate a principled approach for de?ning and discovering pro...
In this thesis, we investigate a principled approach for defining and discovering probabilistic incl...
Creating mappings between ontologies is a common way of approaching the semantic heterogeneity probl...
Abstract. Creating mappings between ontologies is a common way of approaching the semantic heterogen...
IEEE We propose a probabilistic approach to the problem of schema mapping. Our approach is declarati...
Abstract. Creating mappings between ontologies is a common way of approaching the semantic heterogen...
We propose a new declarative approach to schema mapping discovery, that is, the task of identifying ...
In the semantic web environment, where several independent ontologies are used in order to describe...
Several authors have developed relation extraction methods for automatically learning or refining ta...
Key message: Probabilistic graphical models show great potential for robust and reliable constructio...
In Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the ...
Abstract. Semantic web ontologies are based on crisp logic, and do not provide well-defined means fo...
KEY MESSAGE: Probabilistic graphical models show great potential for robust and reliable constructio...
International audienceIn this paper, we investigate a principled approach for defining and discoveri...
Abstract. In this paper, we investigate a principled approach for defin-ing and discovering probabil...
National audienceIn this paper, we investigate a principled approach for de?ning and discovering pro...
In this thesis, we investigate a principled approach for defining and discovering probabilistic incl...
Creating mappings between ontologies is a common way of approaching the semantic heterogeneity probl...
Abstract. Creating mappings between ontologies is a common way of approaching the semantic heterogen...
IEEE We propose a probabilistic approach to the problem of schema mapping. Our approach is declarati...
Abstract. Creating mappings between ontologies is a common way of approaching the semantic heterogen...
We propose a new declarative approach to schema mapping discovery, that is, the task of identifying ...
In the semantic web environment, where several independent ontologies are used in order to describe...
Several authors have developed relation extraction methods for automatically learning or refining ta...
Key message: Probabilistic graphical models show great potential for robust and reliable constructio...
In Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the ...
Abstract. Semantic web ontologies are based on crisp logic, and do not provide well-defined means fo...
KEY MESSAGE: Probabilistic graphical models show great potential for robust and reliable constructio...