Wikipedia, a collaborative Wiki-based encyclopedia, has become a huge phenomenon among Internet users. It covers a huge number of concepts of various fields such as Arts, Geography, History, Science, Sports and Games. As a corpus for knowledge extraction, Wikipedia’s impressive characteristics are not limited to the scale, but also include the dense link structure, word sense disambiguation bas...

Since Wikipedia has become a huge scale database storing wide-range of human knowledge, it is a promising corpus for knowledge extraction. A considerable number of researches on Wikipedia mining have been conducted and the fact that Wikipedia is an invaluable corpus has been confirmed. Wikipedia’s impressive characteristics are not limited to the scale, but also include the dense link structure...

This report documents the details of the Transliteration Mining Shared Task that was run as a part of the Named Entities Workshop (NEWS 2010), an ACL 2010 workshop. The shared task featured mining of name transliterations from the paired Wikipedia titles in 5 different language pairs, specifically, between English and one of Arabic, Chinese, Hindi Russian and Tamil. Totally 5 groups took part i...

Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. This article provides a comprehensive descr...

In this paper, we design a processing flow to produce linked data in articles, providing anchorbased term’s additional information and related terms in different languages (English to Chinese). Wikipedia has been a very important corpus and knowledge bank. Although Wikipedia describes itself not a dictionary or encyclopedia, it is if high potential values in applications and data mining researc...

The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a ...

This paper describes our work in NTCIR-9 on the task of Cross-Lingual Link Discovery (Crosslink/CLLD). The work mainly focuses on two aspects to accomplish this task: (1) How to collect useful data for Crosslink and (2) How to use the data correctly and effectively. The system firstly uses online data collecting and text mining in Chinese Wikipedia articles to build the basic Crosslink database...

Mining and explaining relationships between concepts are challenging tasks in the field of knowledge search. We propose a new approach for the tasks using disjoint paths formed by links in Wikipedia. Disjoint paths are easy to understand and do not contain redundant information. To achieve this approach, we propose a naive method, as well as a generalized flow based method, and a technique for ...