| Henning Rode|
(Alternative names for this author)
|Co-authors||Aly R., De Vries A.P., Djoerd Hiemstra, Giuseppe Attardi, Hugo Zaragoza, Jordi Atserias, Massimiliano Ciaramita, Massimiliano Ciaramita Jordi Atserias, Mihajlovie V., Pavel Serdyukov, Peter Mika, Ramirez G., Tsikrika T., Van Os R., Westerveid T., Westerveld T.|
|Authorship||Publications (4), datasets (0), tools (0)|
|Citations||Total (0), average (0), median (0), max (0), min (0)|
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Henning Rode is an author.
PublicationsOnly 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|
|Structured document retrieval, multimedia retrieval, and entity ranking using PF/Tijah||Lecture Notes in Computer Science||English||2008||CWI and University of Twente used PF/Tijah, a flexible XML retrieval system, to evaluate structured document retrieval, multimedia retrieval, and entity ranking tasks in the context of INEX 2007. For the retrieval of textual and multimedia elements in the Wikipedia data, we investigated various length priors and found that biasing towards longer elements than the ones retrieved by our language modelling approach can be useful. For retrieving images in isolation, we found that their associated text is a very good source of evidence in the Wikipedia collection. For the entity ranking task, we used random walks to model multi-step relevance propagation from the articles describing entities to all related entities and further, and obtained promising results.||0||0|
|Evaluating structured information retrieval and multimedia retrieval using PF/Tijah||Lecture Notes in Computer Science||English||2007||We used a flexible XML retrieval system for evaluating structured document retrieval and multimedia retrieval tasks in the context of the INEX 2006 benchmarks. We investigated the differences between article and element retrieval for Wikipedia data as well as the influence of an elements context on its ranking. We found that article retrieval performed well on many tasks and that pinpointing the relevant passages inside an article may hurt more than it helps. We found that for finding images in isolation the associated text is a very good descriptor in the Wikipedia collection, but we were not very succesful at identifying relevant multimedia fragments consisting of a combination of text and images.||0||0|
|Ranking Very Many Typed Entities on Wikipedia||CIKM '07: Proceedings of the Sixteenth ACM International Conference on Information and Knowledge Management||2007||We discuss the problem of ranking very many entities of different types. In particular we deal with a heterogeneous set of types, some being very generic and some very speci??c. We discuss two approaches for this problem: i) exploiting the entity containment graph and ii) using a Web search engine to compute entity relevance. We evaluate these approaches on the real task of ranking Wikipedia entities typed with a state-of-the-art named-entity tagger. Results show that both approaches can greatly increase the performance of methods based only on passage retrieval.||0||0|
|Ranking very many typed entities on Wikipedia||English||2007||0||0|