Difference between revisions of "Analysing Entity Context in Multilingual Wikipedia to Support Entity-Centric Retrieval Applications"
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| − | '''Analysing Entity Context in Multilingual Wikipedia to Support Entity-Centric Retrieval Applications''' - scientific work related to Wikipedia quality published in 2015, written by Yiwei Zhou, Elena Demidova and Alexandra I. Cristea. | + | '''Analysing Entity Context in Multilingual Wikipedia to Support Entity-Centric Retrieval Applications''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Yiwei Zhou]], [[Elena Demidova]] and [[Alexandra I. Cristea]]. |
== Overview == | == Overview == | ||
| − | Representation of influential entities, such as famous people and multinational corporations, on the Web can vary across languages, reflecting language-specific entity aspects as well as divergent views on these entities in different communities. A systematic analysis of language-specific entity contexts can provide a better overview of the existing aspects and support entity-centric retrieval applications over multilingual Web data. An important source of cross-lingual information about influential entities is Wikipedia -- an online community-created encyclopaedia -- containing more than 280 language editions. In this paper authors focus on the extraction and analysis of the language-specific entity contexts from different Wikipedia language editions over multilingual data. Authors discuss alternative ways such contexts can be built, including graph-based and article-based contexts. Furthermore, authors analyse the similarities and the differences in these contexts in a case study including 80 entities and five Wikipedia language editions. | + | Representation of influential entities, such as famous people and multinational corporations, on the Web can vary across languages, reflecting language-specific entity aspects as well as divergent views on these entities in different communities. A systematic analysis of language-specific entity contexts can provide a better overview of the existing aspects and support entity-centric retrieval applications over [[multilingual]] Web data. An important source of [[cross-lingual]] information about influential entities is [[Wikipedia]] -- an online community-created encyclopaedia -- containing more than 280 language editions. In this paper authors focus on the extraction and analysis of the language-specific entity contexts from different Wikipedia language editions over multilingual data. Authors discuss alternative ways such contexts can be built, including graph-based and article-based contexts. Furthermore, authors analyse the similarities and the differences in these contexts in a case study including 80 entities and five Wikipedia language editions. |
Revision as of 08:19, 24 June 2019
Analysing Entity Context in Multilingual Wikipedia to Support Entity-Centric Retrieval Applications - scientific work related to Wikipedia quality published in 2015, written by Yiwei Zhou, Elena Demidova and Alexandra I. Cristea.
Overview
Representation of influential entities, such as famous people and multinational corporations, on the Web can vary across languages, reflecting language-specific entity aspects as well as divergent views on these entities in different communities. A systematic analysis of language-specific entity contexts can provide a better overview of the existing aspects and support entity-centric retrieval applications over multilingual Web data. An important source of cross-lingual information about influential entities is Wikipedia -- an online community-created encyclopaedia -- containing more than 280 language editions. In this paper authors focus on the extraction and analysis of the language-specific entity contexts from different Wikipedia language editions over multilingual data. Authors discuss alternative ways such contexts can be built, including graph-based and article-based contexts. Furthermore, authors analyse the similarities and the differences in these contexts in a case study including 80 entities and five Wikipedia language editions.
