Scheir Peter, Prettenhofer Peter, Lindstaedt Stefanie , Ghidini Chiara
2010
An associative and adaptive network model for information retrieval in the Semantic Web
Progressive Concepts for Semantic Web Evolution: Applications and Developments IGI Global
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.
2009
Equipping intelligent agents with commonsense knowledge acquired from search query logs: Results from an exploratory study
"Data Mining and Multi-agent Integration" Springer Publishing
Access to knowledge about user goals represents a critical component for
realizing the vision of intelligent agents acting upon user intent on the web. Yet, the
manual acquisition of knowledge about user goals is costly and often infeasible. In
a departure from existing approaches, this paper proposes Goal Mining as a novel
perspective for knowledge acquisition. The research presented in this chapter makes
the following contributions: (a) it presents Goal Mining as an emerging field of
research and a corresponding automatic method for the acquisition of user goals
from web corpora, in the case of this paper search query logs (b) it provides insights
into the nature and some characteristics of these goals and (c) it shows that the goals
acquired from query logs exhibit traits of a long tail distribution, thereby providing
access to a broad range of user goals. Our results suggest that search query logs
represent a viable, yet largely untapped resource for acquiring knowledge about
explicit user goals
Strohmaier M., Prettenhofer P., Kröll Mark
2008
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
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.