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The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have been developed. ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological networks. In ProbLog, facts can be labeled with probabilities.(More)
One of the most popular techniques for multi-relational data mining is Inductive Logic Programming (ILP). Given a set of positive and negative examples, an ILP system ideally finds a logical description of the underlying data model that discriminates the positive examples from the negative examples. However, in multi-relational data mining, one often has to(More)
In Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentially­ quantified variables, are represented by terms built from Skolem functors. In an analogy to probabilistic relational models (PRMs), we wish to represent the joint probability distribution over missing values in a database or(More)
Logic Programming languages, such as Prolog, provide a high-level, declarative approach to programming. Logic Programming offers great potential for implicit parallelism, thus allowing parallel systems to often reduce a program's execution time without programmer intervention. We believe that for complex applications that take several hours, if not days, to(More)
Precision-recall (PR) curves and the areas under them are widely used to summarize machine learning results, especially for data sets exhibiting class skew. They are often used analogously to ROC curves and the area under ROC curves. It is known that PR curves vary as class skew changes. What was not recognized before this paper is that there is a region of(More)