Kate Revoredo

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ProbLog is a recently introduced probabilistic extension of Prolog (De Raedt, et al. in Proceedings of the 20th international joint conference on artificial intelligence, pp. 2468–2473, 2007). A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs to a randomly sampled program, and these(More)
Various approaches for services development in SOA propose business processes as a starting point. However, there is a lack of systematic methods for services identification during business analysis. We believe that there has to exist a integrated view of organizational business processes to promote an effective SOA approach, which will improve IS(More)
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(More)
Foundational Ontologies help maintaining and expanding ontologies expressivity power, thus enabling them to be more precise and free of ambiguities. The use of modeling languages based on these ontologies, such as OntoUML, requires not only the modeler's experience regarding such languages, but also a good understanding about the domain being modeled.(More)
It is widely accepted that presenting data in the form of pictures or models can enhance comprehension, decision making and communication of the underlying information. However, there are few systematic studies that examine whether graphical models are more effective than other representation (such as textual descriptions). Process models provide an(More)
The representation of uncertainty in the semantic web can be eased by the use of learning techniques. To completely induce a probabilistic ontology (that is, an ontology encoded through a probabilistic description logic) from data, two basic tasks must be solved: (1) learning concept definitions and (2) learning probabilistic inclusions. In this paper we(More)