In this paper we propose a new knowledge management task which aims to map Web pages to their corresponding records in a structured database. For example, the DBLP database contains records for many computer scientists, and most of these persons have public Web pages; if we can map the database record with the appropriate Web page then the new information could be used to further describe the person’s database record. To accomplish this goal we employ link paths which contain anchor texts from multiple paths through the Web ending at the Web page in question. We hypothesize that the information from these link paths can be used to generate an accurate Web page to database record mapping. Experiments on two large, real world data sets, DBLP and IMDB for the structured data and computer science faculty members ’ Web pages and official movie homepages for the Web page data, show that our method does provide an accurate mapping. Finally, we conclude by issuing a call for further research on this promising new task. Categories and Subject Descriptors

The discovery and extraction of general lists on the Web continues to be an important problem facing the Web mining community. There have been numerous studies that claim to automatically extract structured data (i.e. lists, record sets, tables, etc.) from the Web for various purposes. Our own recent experiences have shown that the list-finding methods used as part of these larger frameworks do not generalize well and therefore ought to be reevaluated. This paper briefly describes some of the current approaches, and tests them on various list-pages. Based on our findings, we conclude that analyzing a Web page’s DOM-structure is not sufficient for the general list finding task. A B

The World-Wide Web consists not only of a huge number of unstructured texts, but also a vast amount of valuable structured data. Web tables [2] are a typical type of structured information that are pervasive on the web, and Web-scale methods that automatically extract web tables have been studied extensively [1]. Many powerful systems (e.g., OCTOPUS [4], Mesa [3]) use extracted web tables as a fundamental component. In the database vernacular, a table is defined as a set of tuples which have the same attributes. Similarly, a web table is defined as a set of rows (corresponding to database tuples) which have the same column headers (corresponding to database attributes). Therefore, to extract a web table is to extract a relation on the web. In databases, tables often contain foreign keys which refer to other tables. Therefore, it follows that hyperlinks inside a web table sometimes function as foreign keys to other relations whose tuples are contained in the hyperlink’s target pages. In this paper, we explore this idea by asking: can we discover new attributes for web tables by exploring hyperlinks inside web tables? This poster proposes a solution that takes a web table as input. Frequent patterns are generated as new candidate relations by following hyperlinks in the web table. The confidence of candidates are evaluated, and trustworthy candidates are selected to become new attributes for the table. Finally, we show the usefulness of our method by performing experiments on a variety of web domains.

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Abstract — This paper is concerned with information extraction from top-k web pages, which are web pages that describe top k instances of a topic which is of general interest. Examples include “the 10 tallest buildings in the world”, “the 50 hits of 2010 you don’t want to miss”, etc. Compared to other structured information on the web (including web tables), information in top-k lists is larger and richer, of higher quality, and generally more interesting. Therefore top-k lists are highly valuable. For example, it can help enrich open-domain knowledge bases (to support applications such as search or fact answering). In this paper, we present an efficient method that extracts top-k lists from web pages with high performance. Specifically, we extract more than 1.7 million top-k lists from a web corpus of 1.6 billion pages with 92.0 % precision and 72.3 % recall. I.