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检索条件"任意字段=30th International Conference on Logic Programming"

共 1045 条记录，以下是641-650 订阅

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Pay-as-you-go reconciliation in schema matching networks

Pay-as-you-go reconciliation in schema matching networks

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international conference on Data Engineering

作者： Quoc Viet Hung Nguyen thanh Tam Nguyen Zoltán Miklós Karl Aberer Avigdor Gal Matthias Weidlich École Polytechnique Fédérale de Lausanne Switzerland Université de Rennes 1 École Polytechnique Fédérale de Lausanne Technion - Israel Institute of Technology Imperial College London

Schema matching is the process of establishing correspondences between the attributes of database schemas for data integration purposes. Although several automatic schema matching tools have been developed, their results are often incomplete or erroneous. To obtain a correct set of correspondences, a human expert is usually required to validate the generated correspondences. We analyze this reconciliation process in a setting where a number of schemas needs to be matched, in the presence of consistency expectations about the network of attribute correspondences. We develop a probabilistic model that helps to identify the most uncertain correspondences, thus allowing us to guide the expert's work and collect his input about the most problematic cases. As the availability of such experts is often limited, we develop techniques that can construct a set of good quality correspondences with a high probability, even if the expert does not validate all the necessary correspondences. We demonstrate the efficiency of our techniques through extensive experimentation using real-world datasets.

关键词： Probabilistic logic Uncertainty Data integration Approximation methods Measurement uncertainty Databases Computational modeling

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Subgraph pattern matching over uncertain graphs with identity linkage uncertainty

Subgraph pattern matching over uncertain graphs with identit...

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international conference on Data Engineering

作者： Walaa Eldin Moustafa Angelika Kimmig Amol Deshpande Lise Getoor Department of Computer Science University of Maryland USA Department of Computer Science KU Leuven Belgium

ISBN: (纸本)9781479925568

there is a growing need for methods that can represent and query uncertain graphs. these uncertain graphs are often the result of an information extraction and integration system that attempts to extract an entity graph or a knowledge graph from multiple unstructured sources [25], [7]. Such an integration typically leads to identity uncertainty, as different data sources may use different references to the same underlying real-world entities. Integration usually also introduces additional uncertainty on node attributes and edge existence. In this paper, we propose the notion of a probabilistic entity graph (PEG), a formal model that uniformly and systematically addresses these three types of uncertainty. A PEG is a probabilistic graph model that defines a distribution over possible graphs at the entity level. We introduce a general framework for constructing a PEG given uncertain data at the reference level and develop efficient algorithms to answer subgraph pattern matching queries in this setting. Our algorithms are based on two novel ideas: context-aware path indexing and reduction by join-candidates, which drastically reduce the query search space. A comprehensive experimental evaluation shows that our approach outperforms baseline implementations by orders of magnitude.

关键词： Uncertainty Probabilistic logic Random variables Probability distribution Pattern matching Markov random fields Equations

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Discriminative features for identifying and interpreting outliers

Discriminative features for identifying and interpreting out...

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international conference on Data Engineering

作者： Xuan Hong Dang Ira Assent Raymond T. Ng Arthur Zimek Erich Schubert Department of Computer Science Aarhus University Aarhus Denmark Department of Computer Science University of British Columbia Vancouver BC Canada Institut für Informatik Ludwig-Maximilians-Universität München Munich Germany

We consider the problem of outlier detection and interpretation. While most existing studies focus on the first problem, we simultaneously address the equally important challenge of outlier interpretation. We propose an algorithm that uncovers outliers in subspaces of reduced dimensionality in which they are well discriminated from regular objects while at the same time retaining the natural local structure of the original data to ensure the quality of outlier explanation. Our algorithm takes a mathematically appealing approach from the spectral graph embedding theory and we show that it achieves the globally optimal solution for the objective of subspace learning. By using a number of real-world datasets, we demonstrate its appealing performance not only w.r.t. the outlier detection rate but also w.r.t. the discriminative human-interpretable features. this is the first approach to exploit discriminative features for both outlier detection and interpretation, leading to better understanding of how and why the hidden outliers are exceptional.

关键词： Vectors Linear programming Eigenvalues and eigenfunctions Feature extraction Data mining Sparse matrices Face

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SLICE: Reviving regions-based pruning for reverse k nearest neighbors queries

SLICE: Reviving regions-based pruning for reverse k nearest ...

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international conference on Data Engineering

作者： Shiyu Yang Muhammad Aamir Cheema Xuemin Lin Ying Zhang School of Computer Science and Engineering The University of New South Wales Australia Monash University Clayton VIC AU Shanghai Key Laboratory of Trustworthy Computing East China Normal University China

ISBN: (纸本)9781479925568

Given a set of facilities and a set of users, a reverse k nearest neighbors (RkNN) query q returns every user for which the query facility is one of the k-closest facilities. Due to its importance, RkNN query has received significant research attention in the past few years. Almost all of the existing techniques adopt a pruning-and-verification framework. Regions-based pruning and half-space pruning are the two most notable pruning strategies. the half-space based approach prunes a larger area and is generally believed to be superior. Influenced by this perception, almost all existing RkNN algorithms utilize and improve the half-space pruning strategy. We observe the weaknesses and strengths of both strategies and discover that the regions-based pruning has certain strengths that have not been exploited in the past. Motivated by this, we present a new RkNN algorithm called SLICE that utilizes the strength of regions-based pruning and overcomes its limitations. Our extensive experimental study on synthetic and real data sets demonstrate that SLICE is significantly more efficient than the existing algorithms. We also provide a detailed theoretical analysis to analyze various aspects of our algorithm such as I/O cost, the unpruned area, and the cost of its verification phase etc. the experimental study validates our theoretical analysis.

关键词： Algorithm design and analysis Educational institutions Extraterrestrial measurements Australia Context Computational complexity Probabilistic logic

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Combining Equilibrium logic and Dynamic logic

Combining Equilibrium Logic and Dynamic Logic

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12th international conference on logic programming and Nonmonotonic Reasoning (LPNMR)

作者： del Cerro, Luis Farinas Herzig, Andreas Su, Ezgi Iraz Univ Toulouse CNRS IRIT Toulouse France

ISBN: (纸本)9783642405648

We extend the language of here-and-there logic by two kinds of atomic programs allowing to minimally update the truth value of a propositional variable here or there, if possible. these atomic programs are combined by the usual dynamic logic program connectives. We investigate the mathematical properties of the resulting extension of equilibrium logic: we prove that the problem of logical consequence in equilibrium models is EXPTIME complete by relating equilibrium logic to dynamic logic of propositional assignments.

关键词： answer-set programming here-and-there logic equilibrium logic propositional dynamic logic dynamic logic of propositional assignments

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A new frontier of kernel design for structured data

A new frontier of kernel design for structured data

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30th international conference on Machine Learning, ICML 2013

作者： Shin, Kilho University of Hyogo 7-1-28 Minatojima-Minami Kobe 6500047 Japan

ISBN: (纸本)9781629933061

Many kernels for discretely structured data in the literature are designed within the framework of the convolution kernel and its generalization, the mapping kernel. the two most important advantages to use this framework are an easy-to-check criteria of positive definiteness of the resulting kernels and efficient computation based on the dynamic programming methodology. On the other hand, the recent theory of partitionable kernels reveals that the known kernels only take advantage of a very small portion of the potential of the framework. In fact, we have good opportunities to find novel and important kernels in the unexplored area. In this paper, we shed light on a novel important class of kernels within the framework: We give a mathematical characterization of the class, and then, based on the characterization, we show a parametric method to optimize kernels of the class to specific problems. Also, we present some experimental results that show that the new kernels are promising in both accuracy and efficiency. Copyright 2013 by the author(s).

关键词： Dynamic programming

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∝SVM for learning with label proportions

∝SVM for learning with label proportions

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30th international conference on Machine Learning, ICML 2013

作者： Yu, Felix X. Liu, Dong Kumar, Sanjiv Jebara, Tony Chang, Shih-Fu Columbia University New York NY 10027 United States Google Research New York NY 10011 United States

ISBN: (纸本)9781629933061

We study the problem of learning with label proportions in which the training data is provided in groups and only the proportion of each class in each group is known. We propose a new method called proportion-SVM, or ∝SVM, which explicitly models the latent unknown instance labels together with the known group label proportions in a large-margin framework. Unlike the existing works, our approach avoids making restrictive assumptions about the data. the ∝SVM model leads to a non-convex integer programming problem. In order to solve it efficiently, we propose two algorithms: one based on simple alternating optimization and the other based on a convex relaxation. Extensive experiments on standard datasets show that ∝SVM outperforms the state-of-the-art, especially for larger group sizes. Copyright 2013 by the author(s).

关键词： Integer programming

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Convex adversarial collective classification

Convex adversarial collective classification

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30th international conference on Machine Learning, ICML 2013

作者： Torkamani, MohamadAli Lowd, Daniel Department of Computer and Information Science University of Oregon United States

ISBN: (纸本)9781629933061

In this paper, we present a novel method for robustly performing collective classification in the presence of a malicious adversary that can modify up to a fixed number of binary-valued attributes. Our method is formulated as a convex quadratic program that guarantees optimal weights against a worst-case adversary in polynomial time. In addition to increased robustness against active adversaries, this kind of adversarial regularization can also lead to improved generalization even when no adversary is present. In experiments on real and simulated data, our method consistently outperforms both non-adversarial and non-relational baselines. Copyright 2013 by the author(s).

关键词： Quadratic programming

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A machine learning framework for programming by example

A machine learning framework for programming by example

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30th international conference on Machine Learning, ICML 2013

作者： Menon, Aditya Krishna Tamuz, Omer Gulwani, Sumit Lampson, Butler Kalai, Adam Tauman University of California San Diego 9500 Gilman Drive San Diego CA 92093 United States Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel Microsoft Research One Microsoft Way Redmond WA 98052 United States Microsoft Research One Memorial Drive Cambridge MA 02142 United States

ISBN: (纸本)9781629933061

Learning programs is a timely and interesting challenge. In programming by Example (PBE), a system attempts to infer a program from input and output examples alone, by searching for a composition of some set of base functions. We show how machine learning can be used to speed up this seemingly hopeless search problem, by learning weights that relate textual features describing the provided input-output examples to plausible sub-components of a program. this generic learning framework lets us address problems beyond the scope of earlier PBE systems. Experiments on a prototype implementation show that learning improves search and ranking on a variety of text processing tasks found on help forums. Copyright 2013 by the author(s).

关键词： Text processing

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Learning heteroscedastic models by convex programming under group sparsity

Learning heteroscedastic models by convex programming under ...

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30th international conference on Machine Learning, ICML 2013

作者： Dalalyan, Arnak S. Hebiri, Mohamed Meziani, Katia Salmon, Joseph ENSAE-CREST-GENES France LAMA Université Paris Est France CEREMADE Université Paris Dauphine France Institut Mines-Télécom Télécom ParisTech CNRS LTCI France

ISBN: (纸本)9781629933061

Popular sparse estimation methods based on 1-relaxation, such as the Lasso and the Dantzig selector, require the knowledge of the variance of the noise in order to properly tune the regularization parameter. this constitutes a major obstacle in applying these methods in several frameworks - such as time series, random fields, inverse problems - for which the noise is rarely homoscedastic and its level is hard to know in advance. In this paper, we propose a new approach to the joint estimation of the conditional mean and the conditional variance in a high-dimensional (auto-) regression setting. An attractive feature of the proposed estimator is that it is efficiently computable even for very large scale problems by solving a second-order cone program (SOCP). We present theoretical analysis and numerical results assessing the performance of the proposed procedure. Copyright 2013 by the author(s).

关键词： Convex optimization

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