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Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including information extraction, entity resolution, and text mining. Current implementations of MLNs do not scale to large real-world data sets, which is preventing their widespread(More)
Concepts learned by neural networks are dif-cult to understand because they are represented using large assemblages of real-valued parameters. One approach to understanding trained neural networks is to extract symbolic rules that describe their classiica-tion behavior. There are several existing rule-extraction approaches that operate by searching for such(More)
Standard algorithms for explanation-based learning require complete and correct knowledge bases. The KBANN system relaxes this constraint through the use of empirical learning methods to reene approximately correct knowledge. This knowledge is used to determine the structure of an artiicial neural network and the weights on its links, thereby making the(More)
Neural networks, despite their empirically-proven abilities, have been little used for the re-nement of existing knowledge because this task requires a three-step process. First, knowledge in some form must be inserted into a neural network. Second, the network must be reened. Third, knowledge must be extracted from the network. We have previously described(More)
The primary goal of inductive learning is to generalize well { that is, induce a function that accurately produces the correct output for future inputs. Hansen and Salamon showed that, under certain assumptions, combining the predictions of several separately trained neu-ral networks will improve generalization. One of their key assumptions is that the(More)
Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are graphical models that extend dependency networks to relational domains. This higher expressivity, however, comes at the expense of a more complex model-selection problem: an(More)