Web Information Extraction for content augmentation

Today users have to cope with an overwhelming amount of TV channels and Web content sources. We introduce automatic content augmentation, as a novel approach to contextual information extraction on behalf of the user where the context is provided by the primary content source (i.e. TV channel) and tailored by user's preferences. A key aspect of this approach is Web Information Extraction (WebIE) which automatically derives structured information from unstructured Web documents. Our system executes WebIE tasks, each an instantiation of WebIE rules - our generic document processors. We present two WebIE approaches: Diffusion WebIE that crawls a wide set of Web pages and extracts information from a subset of the pertinent pages; and Laser WebIE that accesses a select set of Web pages and extracts narrowly defined information. We describe the architecture and the implementation details of the system and provide detailed Laser WebIE examples.