Large linked data repositories have been built by leveraging semi-structured data in Wikipedia (e.g., DBpedia) and through extracting information from natural language text (e.g., YAGO). However, the Web contains many other vast sources of linked data, such as structured HTML tables and spreadsheets. Often, the semantics in such tables is hidden, preventing one from extracting triples from them directly. This paper describes a probabilistic method that augments an existing knowledge base with facts from tabular data by leveraging a Web text corpus and natural language patterns associated with relations in the knowledge base. A preliminary evaluation shows high potential for this technique in augmenting linked data repositories.

Sekhavat, Y.A., Di Paolo, F., Barbosa, D., Merialdo, P. (2014). Knowledge base augmentation using tabular data. In CEUR Workshop Proceedings. CEUR-WS.

Knowledge base augmentation using tabular data

MERIALDO, PAOLO
2014-01-01

Abstract

Large linked data repositories have been built by leveraging semi-structured data in Wikipedia (e.g., DBpedia) and through extracting information from natural language text (e.g., YAGO). However, the Web contains many other vast sources of linked data, such as structured HTML tables and spreadsheets. Often, the semantics in such tables is hidden, preventing one from extracting triples from them directly. This paper describes a probabilistic method that augments an existing knowledge base with facts from tabular data by leveraging a Web text corpus and natural language patterns associated with relations in the knowledge base. A preliminary evaluation shows high potential for this technique in augmenting linked data repositories.
2014
Sekhavat, Y.A., Di Paolo, F., Barbosa, D., Merialdo, P. (2014). Knowledge base augmentation using tabular data. In CEUR Workshop Proceedings. CEUR-WS.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/307637
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 23
  • ???jsp.display-item.citation.isi??? ND
social impact