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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)
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 Probabi-listic Description Logic from data. We argue that one must learn both concept definitions and(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)
Predicting potential links between nodes in a network is a problem of great practical interest. Link prediction is mostly based on graph-based features and, recently, on approaches that consider the semantics of the domain. However, there is uncertainty in these predictions; by modeling it, one can improve prediction results. In this paper, we propose an(More)
Business processes are dynamic and constantly evolving. Contextual elements that had not yet been identified and represented can arise and influence the execution of each process instance in diverse manners. In this scenario, the identification of these elements is considered of great importance. This paper proposes a formalism for context associated with(More)
The representation of uncertainty in the semantic web can be eased by the use of learning techniques. To completely induce a pro-babilistic 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)
In this paper we argue that through a computer game named Supermarket Game it is possible to perform a test that can aid in the diagnosis of Attention Deficit Hyperactivity Disorder (ADHD). OBJECTIVE: To evaluate the predictive capability of the game to detect ADHD cases through the analysis of its data by data mining techniques. METHOD: Eighty children,(More)
Due to the growing interest in social networks, link prediction has received significant attention. Link prediction is mostly based on graph-based features, with some recent approaches focusing on domain semantics. We propose algorithms for link prediction that use a probabilistic ontology to enhance the analysis of the domain and the unavoidable(More)