Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen


Kern Roman, Juffinger A., Granitzer Michael

Application of Axiomatic Approaches to Crosslanguage Retrieval

Working Notes for the CLEF 2009 Workshop, 2009


Kern Roman, Granitzer Michael

Efficient linear text segmentation based on information retrieval techniques

MEDES '09: Proceedings of the International Conference on Management of Emergent Digital EcoSystems, ACM, 2009

The task of linear text segmentation is to split a large text document into shorter fragments, usually blocks of consecutive sentences. The algorithms that demonstrated the best performance for this task come at the price of high computational complexity. In our work we present an algorithm that has a computational complexity of O(n) with n being the number of sentences in a document. The performance of our approach is evaluated against algorithms of higher complexity using standard benchmark data sets and we demonstrate that our approach provides comparable accuracy.

Zechner Mario, Kern Roman, Granitzer Michael, Muhr M.

External and Intrinsic Plagiarism Detection Using Vector Space Models

Proceedings of the SEPLN'09 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse, 2009


Klieber Hans-Werner, Sabol Vedran, Kern Roman, Granitzer Michael, Muhr M., Ättl G.

Knowledge Discovery Using the Knowminer Framework

IADIS International Conference Information Systems 2009, 2009


Neidhart T., Granitzer Michael, Kern Roman, Weichselbraun A., Wohlgenannt G., Scharl A., Juffinger A.

Distributed Web2.0 Crawling for Ontology Evolution

Journal of Digital Information Management, 2009


Kern Roman, Granitzer Michael, Lindstaedt Stefanie , Ghidini C., Scheir Peter

ARS/SD: An Associative Retrieval Service for the Semantic Desktop

Networked Knowledge - Networked Media Integrating Knowledge Management, New Media Technologies and Semantic Systems, Studies in Computational Intelligence , Pellegrini, T., Auer, S., Tochtermann, K., Schaffert, S., Springer, 2009

While it is agreed that semantic enrichment of resources wouldlead to better search results, at present the low coverage of resources onthe web with semantic information presents a major hurdle in realizing thevision of search on the Semantic Web. To address this problem we investigatehow to improve retrieval performance in a setting where resources aresparsely annotated with semantic information. We suggest employing techniquesfrom associative information retrieval to find relevant material, whichwas not originally annotated with the concepts used in a query. We presentan associative retrieval service for the Semantic Desktop and evaluate if theuse of associative retrieval techniques increases retrieval performance.Evaluation of new retrieval paradigms, as retrieval in the Semantic Web oron the Semantic Desktop, presents an additional challenge as no off-the-shelftest corpora for evaluation exist. Hence we give a detailed description of the
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