Difference between revisions of "Large Scale Semantic Relation Discovery: Toward Establishing the Missing Link Between Wikipedia and Semantic Network"
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| − | '''Large Scale Semantic Relation Discovery: Toward Establishing the Missing Link Between Wikipedia and Semantic Network''' - scientific work related to Wikipedia quality published in 2016, written by Xianpei Han, Xiliang Song and Le Sun. | + | '''Large Scale Semantic Relation Discovery: Toward Establishing the Missing Link Between Wikipedia and Semantic Network''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Xianpei Han]], [[Xiliang Song]] and [[Le Sun]]. |
== Overview == | == Overview == | ||
| − | Wikipedia has been the largest knowledge repository on the Web. However, most of the semantic knowledge in Wikipedia is documented in natural language, which is mostly only human readable and incomprehensible for computer processing. To establish the missing link from Wikipedia to semantic network, this paper proposes a relation discovery method, which can: (1) discover and characterize a large collection of relations from Wikipedia by exploiting the relation pattern regularity, the relation distribution regularity and the relation instance redundancy; and (2) annotate the hyperlinks between Wikipedia articles with the discovered semantic relations. Finally authors discover 14,299 relations, 105,661 relation patterns and 5,214,175 relation instances from Wikipedia, and this will be a valuable resource for many NLP and AI tasks. | + | Wikipedia has been the largest knowledge repository on the Web. However, most of the [[semantic knowledge]] in [[Wikipedia]] is documented in natural language, which is mostly only human readable and incomprehensible for computer processing. To establish the missing link from Wikipedia to semantic network, this paper proposes a relation discovery method, which can: (1) discover and characterize a large collection of relations from Wikipedia by exploiting the relation pattern regularity, the relation distribution regularity and the relation instance redundancy; and (2) annotate the hyperlinks between Wikipedia articles with the discovered semantic relations. Finally authors discover 14,299 relations, 105,661 relation patterns and 5,214,175 relation instances from Wikipedia, and this will be a valuable resource for many NLP and AI tasks. |
Revision as of 09:28, 11 August 2019
Large Scale Semantic Relation Discovery: Toward Establishing the Missing Link Between Wikipedia and Semantic Network - scientific work related to Wikipedia quality published in 2016, written by Xianpei Han, Xiliang Song and Le Sun.
Overview
Wikipedia has been the largest knowledge repository on the Web. However, most of the semantic knowledge in Wikipedia is documented in natural language, which is mostly only human readable and incomprehensible for computer processing. To establish the missing link from Wikipedia to semantic network, this paper proposes a relation discovery method, which can: (1) discover and characterize a large collection of relations from Wikipedia by exploiting the relation pattern regularity, the relation distribution regularity and the relation instance redundancy; and (2) annotate the hyperlinks between Wikipedia articles with the discovered semantic relations. Finally authors discover 14,299 relations, 105,661 relation patterns and 5,214,175 relation instances from Wikipedia, and this will be a valuable resource for many NLP and AI tasks.
