Paolo Papotti

Flint: Google-basing the Web

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.n We have developed a system, called Flint, for automatically searching, collecting and indexing Web pages that publish data representing an instance of a certain conceptual entity. Flint takes as input a small set of labeled sample pages: it automatically infers a description of the underlying conceptual entity and then searches the Web for other pages containing data representing the same entity. Flint automatically extracts data from the collected pages and stores them into a semi-structured self-describing database, such as Google Base. Also, the collected pages can be used to populate a custom search engine; to this end we rely on the facilities provided by Google Co-op.

Flint: From Web Pages to Probabilistic Semantic Data

A large and increasing number of web sites publish structured data about recognizable concepts (such as stock quotes, movies, restaurants). The great chance to create applications that rely on the huge amount of data taken from these sites has been discussed for more than a decade now, but in practice, only a small fraction of such information is currently used. The main reason is that extracting and integrating web data of good quality is an expensive task, which often requires human intervention. In this chapter, we present the main results of the Flint project, which aims at developing automatic and domain-independent tools to perform all the steps required to benefit from Web data: discovering data-intensive web sites containing information about entities of interest, extracting and integrating the published data, and performing a probabilistic analysis to characterize the impreciseness of the data and the accuracy of the sources. The results of the processing are semantically annotated data that can be used to populate a probabilistic database and to develop novel applications.