when emerging technologies such as search engine marketing (sem) face tasks that require human level intelligence, it is inevitable to use the knowledge repositories to endow the machine with the breadth of knowledge available to humans. keyword suggestion for search engine advertising is an important problem for sponsored search and sem that requires a goldmine repository of knowledge. a recen...

Wikipedia, a collaborative Wiki-based encyclopedia, has become a huge phenomenon among Internet users. It covers huge number of concepts of various fields such as Arts, Geography, History, Science, Sports and Games. Since it is becoming a database storing all human knowledge, Wikipedia mining is a promising approach that bridges the Semantic Web and the Social Web (a. k. a. Web 2.0). In fact, i...

We present a narrative theory-based approach to data mining that generates cohesive stories from a Wikipedia corpus. This approach is based on a data mining-friendly view of narrative derived from narratology, and uses a prototype mining algorithm that implements this view. Our initial test case and focus is that of field-based educational tour narrative generation, for which we have successful...

Transliteration mining is aimed at building high quality multi-lingual named entity (NE) lexicons for improving performance in various Natural Language Processing (NLP) tasks including Machine Translation (MT) and Cross Language Information Retrieval (CLIR). In this paper, we apply two Dynamic Bayesian network (DBN)-based edit distance (ED) approaches in mining transliteration pairs from Wikipe...

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 paper presents a novel study of geographic information implicit in the English Wikipedia archive. This project demonstrates a method to extract data from the archive with data mining, map the global distribution of Wikipedia editors through geocoding in GIS, and proceed with a spatial analysis of Wikipedia use in metropolitan cities.