Maike Erdmann

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Maike Erdmann is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Calculating Wikipedia article similarity using machine translation evaluation metrics Bilingual dictionary
Cross-language Document Similarity
Data mining
Proceedings - 25th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2011 English 2011 Calculating the similarity of Wikipedia articles in different languages is helpful for bilingual dictionary construction and various other research areas. However, standard methods for document similarity calculation are usually very simple. Therefore, we describe an approach of translating one Wikipedia article into the language of the other article, and then calculating article similarity with standard machine translation evaluation metrics. An experiment revealed that our approach is effective for identifying Wikipedia articles in different languages that are covering the same concept. 0 0
Extending SMW+ with a linked data integration framework Data integration
Linked data
Semantic MediaWiki
CEUR Workshop Proceedings English 2010 In this paper, we present a project which extends a SMW+ semantic wiki with a Linked Data Integration Framework that performs Web data access, vocabulary mapping, identity resolution, and quality evaluation of Linked Data. As a result, a large collection of neurogenomicsrelevant data from the Web can be flexibly transformed into a unified ontology, allowing unified querying, navigation, and visualization; as well as support for wiki-style collaboration, crowdsourcing, and commentary on chosen data sets. 0 0
Improving the extraction of bilingual terminology from Wikipedia Bilingual dictionary
Data mining
Link analysis
ACM Trans. Multimedia Comput. Commun. Appl. English 2009 Research on the automatic construction of bilingual dictionaries has achieved impressive results. Bilingual dictionaries are usually constructed from parallel corpora, but since these corpora are available only for selected text domains and language pairs, the potential of other resources is being explored as well. In this article, we want to further pursue the idea of using Wikipedia as a corpus for bilingual terminology extraction. We propose a method that extracts term-translation pairs from different types of Wikipedia link information. After that, an {SVM} classifier trained on the features of manually labeled training data determines the correctness of unseen term-translation pairs. 2009 {ACM. 0 0
A bilingual dictionary extracted from the Wikipedia link structure DASFAA English 2008 0 0
An approach for extracting bilingual terminology from Wikipedia DASFAA English 2008 0 1
Wikipedia Mining: Wikipedia as a Corpus por Knowledge Extraction Wikimania English 2008 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 based on URL and brief anchor texts. Because of these characteristics, Wikipedia has become a promising corpus and a big frontier for researchers. A considerable number of researches on Wikipedia Mining such as semantic relatedness measurement, bilingual dictionary construction, and ontology construction have been conducted. In this paper, we take a comprehensive, panoramic view of Wikipedia as a Web corpus since almost all previous researches are just exploiting parts of the Wikipedia characteristics. The contribution of this paper is triple-sum. First, we unveil the characteristics of Wikipedia as a corpus for knowledge extraction in detail. In particular, we describe the importance of anchor texts with special emphasis since it is helpful information for both disambiguation and synonym extraction. Second, we introduce some of our Wikipedia mining researches as well as researches conducted by other researches in order to prove the worth of Wikipedia. Finally, we discuss possible directions of Wikipedia research. 0 0