Difference between revisions of "Leveraging Wikipedia Table Schemas for Knowledge Graph Augmentation"
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| + | | title = Leveraging Wikipedia Table Schemas for Knowledge Graph Augmentation | ||
| + | | date = 2018 | ||
| + | | authors = [[Matteo Cannaviccio]]<br />[[Lorenzo Ariemma]]<br />[[Denilson Barbosa]]<br />[[Paolo Merialdo]] | ||
| + | | doi = 10.1145/3201463.3201468 | ||
| + | | link = http://doi.acm.org/10.1145/3201463.3201468 | ||
| + | }} | ||
'''Leveraging Wikipedia Table Schemas for Knowledge Graph Augmentation''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Matteo Cannaviccio]], [[Lorenzo Ariemma]], [[Denilson Barbosa]] and [[Paolo Merialdo]]. | '''Leveraging Wikipedia Table Schemas for Knowledge Graph Augmentation''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Matteo Cannaviccio]], [[Lorenzo Ariemma]], [[Denilson Barbosa]] and [[Paolo Merialdo]]. | ||
== Overview == | == Overview == | ||
General solutions to augment Knowledge Graphs (KGs) with facts extracted from Web tables aim to associate pairs of columns from the table with a KG relation based on the matches between pairs of entities in the table and facts in the KG. These approaches suffer from intrinsic limitations due to the in[[completeness]] of the KGs. In this paper authors investigate an alternative solution, which leverages the patterns that occur on the schemas of a large corpus of [[Wikipedia]] tables. Authors experimental evaluation, which used [[DBpedia]] as reference KG, demonstrates the advantages of approach over state-of-the-art solutions and reveals that authors can extract more than 1.7M of facts with an estimated accuracy of 0.81 even from tables that do not expose any fact on the KG. | General solutions to augment Knowledge Graphs (KGs) with facts extracted from Web tables aim to associate pairs of columns from the table with a KG relation based on the matches between pairs of entities in the table and facts in the KG. These approaches suffer from intrinsic limitations due to the in[[completeness]] of the KGs. In this paper authors investigate an alternative solution, which leverages the patterns that occur on the schemas of a large corpus of [[Wikipedia]] tables. Authors experimental evaluation, which used [[DBpedia]] as reference KG, demonstrates the advantages of approach over state-of-the-art solutions and reveals that authors can extract more than 1.7M of facts with an estimated accuracy of 0.81 even from tables that do not expose any fact on the KG. | ||
Revision as of 16:57, 21 June 2020
Authors | Matteo Cannaviccio Lorenzo Ariemma Denilson Barbosa Paolo Merialdo |
|---|---|
Publication date | 2018 |
DOI | 10.1145/3201463.3201468 |
Links | Original |
Leveraging Wikipedia Table Schemas for Knowledge Graph Augmentation - scientific work related to Wikipedia quality published in 2018, written by Matteo Cannaviccio, Lorenzo Ariemma, Denilson Barbosa and Paolo Merialdo.
Overview
General solutions to augment Knowledge Graphs (KGs) with facts extracted from Web tables aim to associate pairs of columns from the table with a KG relation based on the matches between pairs of entities in the table and facts in the KG. These approaches suffer from intrinsic limitations due to the incompleteness of the KGs. In this paper authors investigate an alternative solution, which leverages the patterns that occur on the schemas of a large corpus of Wikipedia tables. Authors experimental evaluation, which used DBpedia as reference KG, demonstrates the advantages of approach over state-of-the-art solutions and reveals that authors can extract more than 1.7M of facts with an estimated accuracy of 0.81 even from tables that do not expose any fact on the KG.
