In this paper, we present a highly scalable algorithm for structurally clustering webpages for extraction. We show that, using only the URLs of the webpages and simple content features, it is possible to cluster webpages effectively and efficiently. At the heart of our techniques is a principled framework, based on the principles of information theory, that allows us to effectively leverage the URLs, and combine them with content and structural properties. Using an extensive evaluation over several large full websites, we demonstrate the effectiveness of our techniques, at a scale unattainable by previous techniques.

A large number of web sites publish pages containing structured information about recognizable concepts, but these data are only partially used by current applications. Although such information is spread across a myriad of sources, the web scale implies a relevant redundancy. We present a domain independent system that exploits the redundancy of information to automatically extract and integrate data from the Web. Our solution concentrates on sources that provide structured data about multiple instances from the same conceptual domain, e.g., financial data, product information. Our proposal is based on an original approach that exploits the mutual dependency between the data extraction and the data integration tasks. Experiments on a sample of 175,000 pages confirm the feasibility and quality of the approach.

A large number of web sites publish pages containing structured information about recognizable concepts, but these data are only partially used by current applications. Although such information is spread across a myriad of sources, the web scale implies a relevant redundancy. We present a domain independent system that exploits the redundancy of information to automatically extract and integrate data from the Web. Our solution concentrates on sources that provide structured data about multiple instances from the same conceptual domain, e.g. financial data, product information. Our proposal is based on an original approach that exploits the mutual dependency between the data extraction and the data integration tasks. Experiments confirmed the quality and the feasibility of the approach.

Several Web sites deliver a large number of pages, each publishing data about one instance of some real world entity, such as an athlete, a stock quote, a book. Even though it is easy for a human reader to recognize these instances, current search engines are unaware of them. Technologies for the Semantic Web aim at achieving this goal; however, so far they have been of little help in this respect, as semantic publishing is very limited.