Recent advances in information extraction have led to huge knowledge bases (KBs), which capture knowledge in a machine-readable format. Inductive Logic Programming (ILP) can be used to mine logical rules from these KBs, such as "If two persons are married, then they (usually) live in the same city". While ILP is a mature field, mining logical rules from KBs is difficult, because KBs make an open world assumption. This means that absent information cannot be taken as counterexamples. Our approach AMIE [16] has shown how rules can be mined effectively from KBs even in the absence of counterexamples. In this paper, we show how this approach can be optimized to mine even larger KBs with more than 12M statements. Extensive experiments show how our new approach, AMIE+, extends to areas of mining that were previously beyond reach.

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We study exact and approximate methods for maximum inner product search, a fundamental problem in a number of data mining and information retrieval tasks. We propose the LEMP framework, which supports both exact and approximate search with quality guarantees. At its heart, LEMP transforms a maximum inner product search problem over a large database of vectors into a number of smaller cosine similarity search problems. This transformation allows LEMP to prune large parts of the search space immediately and to select suitable search algorithms for each of the remaining problems individually. LEMP is able to leverage existing methods for cosine similarity search, but we also provide a number of novel search algorithms tailored to our setting. We conducted an extensive experimental study that provides insight into the performance of many state-of-the-art techniques-including LEMP-on multiple real-world datasets. We found that LEMP often was significantly faster or more accurate than alternative methods.

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We study the problem of efficiently retrieving large entries in the product of two given matrices, which arises in a number of data mining and information retrieval tasks. We focus on the setting where the two input matrices are tall and skinny, i.e., with millions of rows and tens to hundreds of columns. In such settings, the product matrix is large and its complete computation is generally infeasible in practice. To address this problem, we propose the LEMP algorithm, which efficiently retrieves only the large entries in the product matrix without actually computing it. LEMP maps the large-entry retrieval problem to a set of smaller cosine similarity search problems, for which existing methods can be used. We also propose novel algorithms for cosine similarity search, which are tailored to our setting. Our experimental study on large real-world datasets indicates that LEMP is up to an order of magnitude faster than state-of-the-art approaches.

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