We present a generic framework to make wrapper induction algorithms tolerant to noise in the training data. This enables us to learn wrappers in a completely unsupervised manner from automatically and cheaply obtained noisy training data, e.g., using dictionaries and regular expressions. By removing the site-level supervision that wrapper-based techniques require, we are able to perform information extraction at web-scale, with accuracy unattained with existing unsupervised extraction techniques. Our system is used in production at Yahoo! and powers live applications. 1.

We present in this paper ObjectRunner, a system for extracting, integrating and querying structured data from the Web. Our system harvests real-world items from template-based HTML pages (the so-called structured Web). It illustrates a two-phase querying of the Web, in which an intentional description of the targeted data is first provided, in a flexible and widely applicable manner. ObjectRunner follows then a lightweight, best-effort approach, leveraging both the input description and the source structure. This process is domain-independent, in the sense that it applies to any relation, either flat or nested, describing real-world items. We advocate via our prototype that fully automatic extraction and integration of structured data can be done fast and effectively, when the redundancy of the Web meets knowledge over the to-be-extracted data. We present the technical details and the overall platform through several application scenarios on real-life Web sources. 1.

We present ANGIE, a system that can answer user queries by combining knowledge from a local database with knowledge retrieved from Web services. If a user poses a query that cannot be answered by the local database alone, ANGIE calls the appropriate Web services to retrieve the missing information. This information is integrated seamlessly and transparently into the local database, so that the user can query and browse the knowledge base while appropriate Web services are called automatically in the background. 1.

apport de recherche

Mashups are situational applications that join multiple sources to better meet the information needs of Web users. Web sources can be huge databases behind query interfaces, which triggers the need of ranking mashup results based on some user preferences. We present MashRank, a mashup authoring and processing system building on concepts from rank-aware processing, probabilistic databases, and information extraction to enable ranked mashups of (unstructured) sources with uncertain ranking attributes. MashRank is based on new semantics, formulations and processing techniques to handle uncertain preference scores, represented as intervals enclosing possible score values. MashRank integrates information extraction with query processing by asynchronously pushing extracted data on-the-fly into pipelined rank-aware query plans, and using ranking early-out requirements to limit extraction cost. To the best of our knowledge, both the technical problems and target applications of MashRank have not been addressed before. 1.

Abstract. In this tutorial we provide an insight into Web Mining, i.e., discovering knowledge from the World Wide Web, especially with reference to the latest developments in Web technology. The topics covered are: the Deep Web, also known as the Hidden Web or Invisible Web; the Semantic Web including standards such as RDFS and OWL; the eXtensible Markup Language XML, a widespread communication medium for the Web; and domain-specific markup languages defined within the context of XML We explain how each of these developments support knowledge discovery from data stored over the Web, thereby assisting several real-world applications. Keywords: Information Retrieval, Standards, Web Mining. 1

The World Wide Web no longer consists just of HTML pages. Our work sheds light on a number of trends on the Internet that go beyond simple Web pages. The hidden Web provides a wealth of data in semi-structured form, accessible through Web forms and Web services. These services, as well as numerous other applications on the Web, commonly use XML, the eXtensible Markup Language. XML has become the lingua franca of the Internet that allows customized markups to be defined for specific domains. On top of XML, the Semantic Web grows as a common structured data source. In this work, we first explain each of these developments in detail. Using real-world examples from scientific domains of great interest today, we then demonstrate how these new developments can assist the managing, harvesting, and organization of data on the Web. On the way, we also illustrate the current research avenues in these domains. We believe that this effort would help bridge multiple database tracks, thereby attracting researchers with a view to extend database technology.

c t i v it y e p o r t

In this paper, we analyze the nature and distribution of structured data on the Web. Web-scale information extraction, or the problem of creating structured tables using extraction from the entire web, is gathering lots of research interest. We perform a study to understand and quantify the value of Web-scale extraction, and how structured information is distributed amongst top aggregator websites and tail sites for various interesting domains. We believe this is the first study of its kind, and gives us new insights for information extraction over the Web.