The SemanticWeb - ISWC 2014 - 13th International SemanticWeb Conference, Proceedings

Autor: Bühmann, Lorenz, Usbeck, Ricardo, Ngomo, Axel Cyrille Ngonga, Saleem, Muhammad, Both, Andreas, CRESCENZI, VALTER, MERIALDO, PAOLO, QIU, DISHENG

Only a small fraction of the information on the Web is represented as Linked Data. This lack of coverage is partly due to the paradigms followed so far to extract Linked Data.While converting structured data to RDF is well supported by tools, most ap

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3668::87c4359e5c0afaff31ebb90b14ded25f
http://springerlink.com/content/0302-9743/copyright/2005/

Web-Scale Extension of RDF Knowledge Bases from Templated Websites.

Autor: Bühmann, Lorenz, Usbeck, Ricardo, Ngonga Ngomo, Axel-Cyrille, Saleem, Muhammad, Both, Andreas, Crescenzi, Valter, Merialdo, Paolo, Qiu, Disheng

Publikováno v: Semantic Web - ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I; 2014, p66-81, 16p

Big Data Linkage for Product Specification Pages

Autor: Disheng Qiu, Luciano Barbosa, Valter Crescenzi, Divesh Srivastava, Paolo Merialdo

Publikováno v: SIGMOD Conference

An increasing number of product pages are available from thousands of web sources, each page associated with a product, containing its attributes and one or more product identifiers. The sources provide overlapping information about the products, usi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de082d849fddd533be6f7532ddb26489
https://doi.org/10.1145/3183713.3183757

Crowdsourcing large scale wrapper inference

Autor: Disheng Qiu, Valter Crescenzi, Paolo Merialdo

We present a crowdsourcing system for large-scale production of accurate wrappers to extract data from data-intensive websites. Our approach is based on supervised wrapper inference algorithms which demand the burden of generating training data to wo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a1d1323a5aa01c69ecbee2bec592e639
https://hdl.handle.net/11590/307638

ALFRED

Autor: Disheng Qiu, Valter Crescenzi, Paolo Merialdo

Publikováno v: WWW (Companion Volume)
Scopus-Elsevier

The development of solutions to scale the extraction of data from Web sources is still a challenging issue. High accu- racy can be achieved by supervised approaches, but the costs of training data, i.e., annotations over a set of sam- ple pages, limi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb975f3db4d175082b5bb62f6974cf9f
https://doi.org/10.1145/2487788.2487927