This paper describes a simple heuristic approach to solving large-scale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a v alue-ordering heuristic, the min-con icts heuristic, that attempts to minimize the number of constraint violations after each step. The heuristic can be used with a variety of di erent search strategies.We demonstrate empirically that on the n-queens problem, a technique based on this approach performs orders of magnitude better than traditional backtracking techniques. We also describe a scheduling application where the approach has been used successfully. A theoretical analysis is presented both to explain why this method works well on certain types of problems and to predict when it is likely to be most e ective.

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With the tremendous amount of information that becomes available on the Web on a daily basis, the ability to quickly develop information agents has become a crucial problem.A vital component of any Web-based information agent is a set of wrappers that can extract the relevant data from semistructured information sources. Our novel approach to wrapper induction is based on the idea of hierarchical information extraction, which turns the hard problem of extracting data from an arbitrarily complex document into a series of easier extraction tasks. We introduce an inductive algorithm, STALKER, that generates high accuracy extraction rules based on user-labeled training examples. Labeling the training data represents the major bottleneck in using wrapper induction techniques, and our experimental results show that STALKER does significantly better then other approaches; on one hand, STALKER requires up to two orders of magnitude fewer examples than other algorithms, while on the other hand it can handle information sources that could not be wrapped by existing techniques.

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The proliferation of online information sources has led to an increased use of wrappers for extracting data from Web sources. While most of the previous research has focused on quick and efficient generation of wrappers, the development of tools for wrapper maintenance has received less attention. This is an important research problem because Web sources often change in ways that prevent the wrappers from extracting data correctly. We present an efficient algorithm that learns structural information about data from positive examples alone. We describe how this information can be used for two wrapper maintenance applications: wrapper verification and reinduction. The wrapper verification system detects when a wrapper is not extracting correct data, usually because the Web source has changed its format. The reinduction algorithm automatically recovers from changes in the Web source by identifying data on Web pages so that a new wrapper may be generated for this source. To validate our approach, we monitored 27 wrappers over a period of a year. The verification algorithm correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes, resulting in precision of 0.73 and recall of 0.95. We validated the reinduction algorithm on ten Web sources. We were able to successfully reinduce the wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data extraction task.

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Many Web sites, especially those that dynamically generate HTML pages to display the results of a user's query, present information in the form of list or tables. Current tools that allow applications to programmatically extract this information rely heavily on user input, often in the form of labeled extracted records. The sheer size and rate of growth of the Web make any solution that relies primarily on user input is infeasible in the long term. Fortunately, many Web sites contain much explicit and implicit structure, both in layout and content, that we can exploit for the purpose of information extraction. This paper describes an approach to automatic extraction and segmentation of records from Web tables. Automatic methods do not require any user input, but rely solely on the layout and content of the Web source. Our approach relies on the common structure of many Web sites, which present information as a list or a table, with a link in each entry leading to a detail page containing additional information about that item. We describe two algorithms that use redundancies in the content of table and detail pages to aid in information extraction. The first algorithm encodes additional information provided by detail pages as constraints and finds the segmentation by solving a constraint satisfaction problem. The second algorithm uses probabilistic inference to find the record segmentation. We show how each approach can exploit the web site structure in a general, domain-independent manner, and we demonstrate the effectiveness of each algorithm on a set of twelve Web sites.

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