Difference between revisions of "Utilization of Dbpedia Mapping in Cross Lingual Wikipedia Infobox Completion"

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'''Utilization of Dbpedia Mapping in Cross Lingual Wikipedia Infobox Completion''' - scientific work related to Wikipedia quality published in 2016, written by Megawati, Saemi Jang and Mun Yong Yi.
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'''Utilization of Dbpedia Mapping in Cross Lingual Wikipedia Infobox Completion''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Megawati]], [[Saemi Jang]] and [[Mun Yong Yi]].
  
 
== Overview ==
 
== Overview ==
Wikipedia plays a central role in the web as one of the biggest knowledge source due to its large coverage of information that comes from various domains. However, due to the enormous number of pages and limited number of contributors to maintain all of the pages, the problem of missing information among Wikipedia articles has emerged, especially articles in multiple language versions. Several approaches have been studied to fix information gap in between cross- language Wikipedia articles. However, they can only be applied for languages that came from the same root. In this paper, authors propose an approach to generate new information for Wikipedia infoboxes written in different languages with different roots by utilizing the existing DBpedia mappings. Authors combined mapping information from DBpedia with an instance-based method to align the existing Korean-English infobox attribute-value pairs as well as to generate new pairs from the Korean version to fill missing information in the English version. The results showed that authors could expand up to 38% of the existing English Wikipedia attribute-value pairs from datasets with 61% of accuracy.
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Wikipedia plays a central role in the web as one of the biggest knowledge source due to its large coverage of information that comes from various domains. However, due to the enormous number of pages and limited number of contributors to maintain all of the pages, the problem of missing information among [[Wikipedia]] articles has emerged, especially articles in multiple [[language versions]]. Several approaches have been studied to fix information gap in between cross- language Wikipedia articles. However, they can only be applied for languages that came from the same root. In this paper, authors propose an approach to generate new information for Wikipedia [[infoboxes]] written in [[different language]]s with different roots by utilizing the existing [[DBpedia]] mappings. Authors combined mapping information from DBpedia with an instance-based method to align the existing Korean-English infobox attribute-value pairs as well as to generate new pairs from the Korean version to fill missing information in the English version. The results showed that authors could expand up to 38% of the existing [[English Wikipedia]] attribute-value pairs from datasets with 61% of accuracy.

Latest revision as of 09:13, 11 February 2020

Utilization of Dbpedia Mapping in Cross Lingual Wikipedia Infobox Completion - scientific work related to Wikipedia quality published in 2016, written by Megawati, Saemi Jang and Mun Yong Yi.

Overview

Wikipedia plays a central role in the web as one of the biggest knowledge source due to its large coverage of information that comes from various domains. However, due to the enormous number of pages and limited number of contributors to maintain all of the pages, the problem of missing information among Wikipedia articles has emerged, especially articles in multiple language versions. Several approaches have been studied to fix information gap in between cross- language Wikipedia articles. However, they can only be applied for languages that came from the same root. In this paper, authors propose an approach to generate new information for Wikipedia infoboxes written in different languages with different roots by utilizing the existing DBpedia mappings. Authors combined mapping information from DBpedia with an instance-based method to align the existing Korean-English infobox attribute-value pairs as well as to generate new pairs from the Korean version to fill missing information in the English version. The results showed that authors could expand up to 38% of the existing English Wikipedia attribute-value pairs from datasets with 61% of accuracy.