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Even the entire Web corpus does not explicitly answer all questions, yet inference can uncover many implicit answers. But where do inference rules come from? This paper investigates the problem of learning inference rules from Web text in an un-supervised, domain-independent manner. The SHERLOCK system, described herein, is a first-order learner that(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)
The structure of a Markov network is typically learned in one of two ways. The first approach is to treat this task as a global search problem. However, these algorithms are slow as they require running the expensive operation of weight (i.e., parameter) learning many times. The second approach involves learning a set of local models and then combining them(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)
Greedy machine learning algorithms suffer from shortsight-edness, potentially returning suboptimal models due to limited exploration of the search space. Greedy search misses useful refinements that yield a significant gain only in conjunction with other conditions. Re-lational learners, such as inductive logic programming algorithms, are especially(More)
Probabilistic logical languages provide powerful formalisms for knowledge representation and learning. Yet performing inference in these languages is extremely costly, especially if it is done at the propositional level. Lifted inference algorithms , which avoid repeated computation by treating indistinguishable groups of objects as one, help mitigate this(More)