document: SCAD : collective discovery of attribute values Search engines today offer a rich user experience , no longer restricted to `` ten blue links '' . For example , the query `` Canon EOS Digital Camera '' returns a photo of the digital camera , and a list of suitable merchants and prices . Similar results are offered in other domains like food , entertainment , travel , etc. . All these experiences are fueled by the availability of structured data about the entities of interest . To obtain this structured data , it is necessary to solve the following problem : given a category of entities with its schema , and a set of Web pages that mention and describe entities belonging to the category , build a structured representation for the entity under the given schema . Specifically , collect structured numerical or discrete attributes of the entities . Most previous approaches regarded this as an information extraction problem on individual documents , and made no special use of numerical attributes . In contrast , we present an end-to-end framework which leverages signals not only from the Web page context , but also from a collective analysis of all the pages corresponding to an entity , and from constraints related to the actual values within the domain . Our current implementation uses a general and flexible Integer Linear Program ( ILP ) to integrate all these signals into holistic decisions over all attributes . There is one ILP per entity and it is small enough to be solved in under 38 milliseconds in our experiments . We apply the new framework to a setting of significant practical importance : catalog expansion for Commerce search engines , using data from Bing Shopping . Finally , we present experiments that validate the effectiveness of the framework and its superiority to local extraction .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O