Flint: From Web Pages to Probabilistic Semantic Data

Chapter
Part of the Data-Centric Systems and Applications book series (DCSA)

Abstract

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.

Keywords

Soccer Player Extraction Rule Probabilistic Database Target Entity Weak Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Dipartimento di Informatica e AutomazioneUniversità degli Studi Roma TreRomeItaly

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