Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen


Lindstaedt Stefanie , Stern Hermann, Beham Günter, Prettenhofer P., Scheir Peter

Applying Language Technologies to Support Work-Integrated Learning

SDV. Sprache und Datenverarbeitung: International Journal for Language Data Processing, Current Trends in Technology Enhanced Learning, Schmitz, H.-C., Wolpers, M., Universitätsverlag Rhein-Ruhr, 2011


Scheir Peter, Prettenhofer Peter, Lindstaedt Stefanie , Ghidini Chiara

An associative and adaptive network model for information retrieval in the Semantic Web

Progressive Concepts for Semantic Web Evolution: Applications and Developments, IGI Global, 2010

While it is agreed that semantic enrichment of resources would lead to better search results, at present the low coverage of resources on the web with semantic information presents a major hurdle in realizing the vision of search on the Semantic Web. To address this problem, this chapter investigates how to improve retrieval performance in settings where resources are sparsely annotated with semantic information. Techniques from soft computing are employed to find relevant material that was not originally annotated with the concepts used in a query. The authors present an associative retrieval model for the Semantic Web and evaluate if and to which extent the use of associative retrieval techniques increases retrieval performance. In addition, the authors present recent work on adapting the network structure based on relevance feedback by the user to further improve retrieval effectiveness. The evaluation of new retrieval paradigms - such as retrieval based on technology for the Semantic Web - presents an additional challenge since no off-the-shelf test corpora exist. Hence, this chapter gives a detailed description of the approach taken to evaluate the information retrieval service the authors have built.

Kröll Mark, Prettenhofer P., Strohmaier M.

Equipping intelligent agents with commonsense knowledge acquired from search query logs: Results from an exploratory study

"Data Mining and Multi-agent Integration", Springer Publishing, 2009


Strohmaier M., Prettenhofer P., Lux M.

Different Degrees of Explicitness in Intentional Artifacts - Studying User Goals in a Large Search Query Log

proceedings of CSKGOI 08 International Workshop on Commonsense Knowledge and Goal Oriented Interfaces, 2008

On the web, search engines represent a primary instrument through which users exercise their intent. Understanding the specific goals users express in search queries could improve our theoretical knowledge about strategies for search goal formulation and search behavior, and could equip search engine providers with better descriptions of users’ information needs. However, the degree to which goals are explicitly expressed in search queries can be suspected to exhibit considerable variety, which poses a series of challenges for researchers and search engine providers. This paper introduces a novel perspective on analyzing user goals in search query logs by proposing to study different degrees of intentional explicitness. To explore the implications of this perspective, we studied two different degrees of explicitness of user goals in the AOL search query log containing more than 20 million queries. Our results suggest that different degrees of intentional explicitness represent an orthogonal dimension to existing search query categories and that understanding these different degrees is essential for effective search. The overall contribution of this paper is the elaboration of a set of theoretical arguments and empirical evidence that makes a strong case for further studies of different degrees of intentional explicitness in search query logs.

Strohmaier M., Prettenhofer P., Kröll Mark

Different Degrees of Explicitness in Intentional Artifacts - Studying User Goals in a Large Search Query Log

International Workshop on Agents and Data Mining Interaction ADMI'08, 2008

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