Harvesting Application Information for Industry-Scale Relational Schema Matching

Consider the problem of migrating a company's CRM or ERP database from one application to another, or integrating two such databases as a result of a merger. This problem requires matching two large relational schemas with hundreds and sometimes thousands of fields. Further, the correct match is likely complex: rather than a simple one-to-one alignment, some fields in the source database may map to multiple fields in the target database, and others may have no equivalent fields in the target database. Despite major advances in schema matching, fully automated solutions to large relational schema matching problems are still elusive. This paper focuses on improving the accuracy of automated large relational schema matching. Our key insight is the observation that modern database applications have a rich user interface that typically exhibits more consistency across applications than the underlying schemas. We associate UI widgets in the application with the underlying database fields on which they operate and demonstrate that this association delivers new information useful for matching large and complex relational schemas. Additionally, we show how to formalize the schema matching problem as a quadratic program, and solve it efficiently using standard optimization and machine learning techniques. We evaluate our approach on real-world CRM applications with hundreds of fields and show that it improves the accuracy by a factor of 2-4x

Inverting Supervised Representations with Autoregressive Neural Density Models

We present a method for feature interpretation that makes use of recent advances in autoregressive density estimation models to invert model representations. We train generative inversion models to express a distribution over input features conditioned on intermediate model representations. Insights into the invariances learned by supervised models can be gained by viewing samples from these inversion models. In addition, we can use these inversion models to estimate the mutual information between a model's inputs and its intermediate representations, thus quantifying the amount of information preserved by the network at different stages. Using this method we examine the types of information preserved at different layers of convolutional neural networks, and explore the invariances induced by different architectural choices. Finally we show that the mutual information between inputs and network layers decreases over the course of training, supporting recent work by Shwartz-Ziv and Tishby (2017) on the information bottleneck theory of deep learning.Comment: Accepted for publication by AISTATS 201

Learning High-Level Planning from Text

Comprehending action preconditions and effects is an essential step in modeling the dynamics of the world. In this paper, we express the semantics of precondition relations extracted from text in terms of planning operations. The challenge of modeling this connection is to ground language at the level of relations. This type of grounding enables us to create high-level plans based on language abstractions. Our model jointly learns to predict precondition relations from text and to perform high-level planning guided by those relations. We implement this idea in the reinforcement learning framework using feedback automatically obtained from plan execution attempts. When applied to a complex virtual world and text describing that world, our relation extraction technique performs on par with a supervised baseline, yielding an F-measure of 66% compared to the baseline’s 65%. Additionally, we show that a high-level planner utilizing these extracted relations significantly outperforms a strong, text unaware baseline – successfully completing 80% of planning tasks as compared to 69% for the baseline.National Science Foundation (U.S.) (CAREER Grant IIS-0448168)United States. Defense Advanced Research Projects Agency. Machine Reading Program (FA8750-09-C-0172, PO#4910018860)Battelle Memorial Institute (PO#300662

SWIFT: A Narrowband-Friendly Cognitive Wideband Network

Wideband technologies in the unlicensed spectrum can satisfy the ever-increasing demands for wireless bandwidth created by emerging rich media applications. The key challenge for such systems, however, is to allow narrowband technologies that share these bands (say, 802.11 a/b/g/n, Zigbee) to achieve their normal performance, without compromising the throughput or range of the wideband network.This paper presents SWIFT, the first system where high-throughput wideband nodes are shown in a working deployment to coexist with unknown narrowband devices, while forming a network of their own. Prior work avoids narrowband devices by operating below the noise level and limiting itself to a single contiguous unused band. While this achieves coexistence, it sacrifices the throughput and operating distance of the wideband device. In contrast, SWIFT creates high throughput wireless links by weaving together non-contiguous unused frequency bands that change as narrowband devices enter or leave the environment. This design principle of cognitive aggregation allows SWIFT to achieve coexistence, while operating at normal power, and thereby obtaining higher throughput and greater operating range. We implement SWIFT on a wideband hardware platform, and evaluate it in the presence of 802.11 devices. In comparison to a baseline that coexists with narrowband devices by operating below their noise level, SWIFT is equally narrowband-friendly but achieves 3.6x-10.5x higher throughput and 6x greater range

Learning to Automatically Solve Algebra Word Problems

We present an approach for automatically learning to solve algebra word problems. Our algorithm reasons across sentence boundaries to construct and solve a system of linear equations, while simultaneously recovering an alignment of the variables and numbers in these equations to the problem text. The learning algorithm uses varied supervision, including either full equations or just the final answers. We evaluate performance on a newly gathered corpus of algebra word problems, demonstrating that the system can correctly answer almost 70% of the questions in the dataset. This is, to our knowledge, the first learning result for this task.Battelle Memorial Institute (PO 300662)National Science Foundation (U.S.) (Grant IIS-0835652

Learning to Solve Arithmetic Word Problems with Verb Categorization

This paper presents a novel approach to learning to solve simple arithmetic word problems. Our system, ARIS, analyzes each of the sentences in the problem state-ment to identify the relevant variables and their values. ARIS then maps this infor-mation into an equation that represents the problem, and enables its (trivial) so-lution as shown in Figure 1. The pa-per analyzes the arithmetic-word problems “genre”, identifying seven categories of verbs used in such problems. ARIS learns to categorize verbs with 81.2 % accuracy, and is able to solve 77.7 % of the problems in a corpus of standard primary school test questions. We report the first learning re-sults on this task without reliance on pre-defined templates and make our data pub-licly available.