We demonstrate a system to automatically grab data from data intensive web sites. The system first infers a model that describes at the intensional level the web site as a collection of classes; each class represents a set of structurally homogeneous pages, and it is associated with a small set of representative pages. Based on the model a library of wrappers, one per class, is then inferred, with the help an external wrapper generator. The model, together with the library of wrappers, can thus be used to navigate the site and extract the data.
We demonstrate a system to automatically grab data from data intensive web sites. The system first infers a model that describes at the intensional level the web site as a collection of classes; each class represents a set of structurally homogeneous pages, and it is associated with a small set of representative pages. Based on the model a library of wrappers, one per class, is then inferred, with the help an external wrapper generator. The model, together with the library of wrappers, can thus be used to navigate the site and extract the data.
Information extraction from websites is nowadays a relevant problem, usually performed by software modules called wrappers. A key requirement is that the wrapper generation process should be automated to the largest extent, in order to allow for large-scale extraction tasks even in presence of changes in the underlying sites. So far, however, only semi-automatic proposals have appeared in the literature.We present a novel approach to information extraction from websites, which reconciles recent proposals for supervised wrapper induction with the more traditional field of grammar inference. Grammar inference provides a promising theoretical framework for the study of unsupervised-that is, fully automatic-wrapper generation algorithms. However, due to some unrealistic assumptions on the input, these algorithms are not practically applicable to Web information extraction tasks.The main contributions of the article stand in the definition of a class of regular languages, called the prefix mark-up languages, that abstract the structures usually found in HTML pages, and in the definition of a polynomial-time unsupervised learning algorithm for this class. The article shows that, differently from other known classes, prefix mark-up languages and the associated algorithm can be practically used for information extraction purposes.A system based on the techniques described in the article has been implemented in a working prototype. We present some experimental results on known Websites, and discuss opportunities and limitations of the proposed approach.
The chase is a family of algorithms used in a number of data management tasks, such as data exchange, answering queries under dependencies, query reformulation with constraints, and data cleaning. It is well established as a theoretical tool for understanding these tasks, and in addition a number of prototype systems have been developed. While individual chase-based systems and particular optimizations of the chase have been experimentally evaluated in the past, we provide the first comprehensive and publicly available benchmark-test infrastructure and a set of test scenarios-for evaluating chase implementations across a wide range of assumptions about the dependencies and the data. We used our benchmark to compare chase-based systems on data exchange and query answering tasks with one another, as well as with systems that can solve similar tasks developed in closely related communities. Our evaluation provided us with a number of new insights concerning the factors that impact the performance of chase implementations.
Data-cleaning (or data-repairing) is considered a crucial problem in many database-related tasks. It consists in making a database consistent with respect to a set of given constraints. In recent years, repairing methods have been proposed for several classes of constraints. However, these methods rely on ad hoc decisions and tend to hard-code the strategy to repair conflicting values. As a consequence, there is currently no general algorithm to solve database repairing problems that involve different kinds of constraints and different strategies to select preferred values. In this paper we develop a uniform framework to solve this problem. We propose a new semantics for repairs, and a chase-based algorithm to compute minimal solutions. We implemented the framework in a DBMSbased prototype, and we report experimental results that confirm its good scalability and superior quality in computing repairs.
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