Learning probabilistic logic programming languages is receiving an increasing attention, and systems are available for learning the parameters (PRISM, LeProbLog, LFI-ProbLog and EMBLEM) or both structure and parameters (SEM-CP-logic and SLIPCASE) of these languages. In this paper we present the algorithm SLIPCOVER for “Structure LearnIng of Probabilistic logic programs by searChing OVER the clause space.” It performs a beam search in the space of probabilistic clauses and a greedy search in the space of theories using the log likelihood of the data as the guiding heuristics. To estimate the log likelihood, SLIPCOVER performs Expectation Maximization with EMBLEM. The algorithm has been tested on five real world datasets and compared with SLIPCASE, SEM-CP-logic, Aleph and two algorithms for learning Markov Logic Networks (Learning using Structural Motifs (LSM) and ALEPH++ExactL1). SLIPCOVER achieves higher areas under the precision-recall and receiver operating characteristic curves in most cases.

Representing uncertain information is crucial for modeling real world domains. In this paper we present a technique for the integration of probabilistic information in Description Logics (DLs) that is based on the distribution semantics for probabili

We studied the impact of a clinical decision support system (CDSS) implemented in a few wards of two Italian health care organizations on the ordering of redundant laboratory tests under different perspectives: (1) analysis of the volume of tests, (2) cost analysis, (3) end-user satisfaction before and after the installation of the CDSS. (1) and (2) were performed by comparing the ordering of laboratory tests between an intervention group of wards where a CDSS was in use and a second (control) group where a CDSS was not in use; data were compared during a 3-month period before (2014) and a 3-month period after (2015) CDSS installation. To measure end-user satisfaction (3), a questionnaire based on POESUS was administered to the medical staff. After the introduction of the CDSS, the number of laboratory tests requested decreased by 16.44% and costs decreased by 16.53% in the intervention group, versus an increase in the number of tests (+3.75%) and of costs (+1.78%) in the control group. Feedback from practice showed that the medical staff was generally satisfied with the CDSS and perceived its benefits, but they were less satisfied with its technical performance in terms of slow response time. The implementation of CDSSs can have a positive impact on both the efficiency of care provision and health care costs. The experience of using a CDSS can also result in good practice to be implemented by other health care organizations, considering the positive result from the first attempt to gather the point of view of end-users in Italy.

Nowadays, water leak control at different levels is a necessary tool for sustainable water resource management. Research shows that more than one third of the world's drinking water is lost during its transfer to users, and that leakages on users' properties vary between 2 and 13% of total residential water demand, are very frequent and difficult to detect. Thanks to the advances in Internet of Things solutions for smart metering devices, it is possible to gather household water consumption information with high spatial and temporal resolution and to analyse them. This article applies several supervised Machine Learning (ML) techniques for the automatic detection of leakages of different magnitudes - even smaller than the meter sensitivity - in pipes within the dwelling, by using data collected by smart meters installed at the connection of users to the distribution network in an Italian town. The results obtained are compared with the performance of an “empirical algorithm” previously presented by the authors, able to automatically identify leakages by checking if the hourly flow rate is never zero during the whole day, but not able to distinguish the size of the leakages. Experimental results over about 40,500 records show that ML techniques significantly improve the detection performance both in discriminating between presence and absence of leakages and in discriminating different-size leakages.