Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen


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


Körner C., Kröll Mark, Strohmaier M.

Intentional Query Suggestion: Making User Goals More Explicit During Search

Workshop on Web Search Click Data WSCD'09, 2009


Kröll Mark, Strohmaier M.

Extracting Human Goals from Weblogs

Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML) 2009, 2009


Kröll Mark, Koerner C.

Automatically Annotating Textual Resources with Human Intentions

Hypertext 2009, 20th ACM Conference on Hypertext and Hypermedia (HT'09), 2009


Kröll Mark

Studying Databases of Intentions: Do Search Query Logs Capture Knowledge about Common Human Goals?

The Fifth International Conference on Knowledge Capture (K-CAP'09), 2009


Jeanquartier Fleur, Kröll Mark, Strohmaier M.

Intent Tag Clouds: An Intentional Approach To Visual Text Analysis

Proceedings of the Workshop on Semantic Multimedia Database Technologies, 10th International Workshop of the Multimedia Metadata Community (SeMuDaTe2009), CEUR Workshop Proceedings Volume 539, 2009

Getting a quick impression of the author's intention of a text is a task often performed. An author's intention plays a major role in successfully understanding a text. For supporting readers in this task, we present an intentional approach to visual text analysis, making use of tag clouds. The objectiveof tag clouds is presenting meta-information in a visually appealing way. However there is also much uncertainty associated with tag clouds, such as giving the wrong impression. It is not clear whether the author's intent can be grasped clearly while looking at a corresponding tag cloud. Therefore it is interesting to ask to what extent, with tag clouds, it is possible to support the user in understanding intentions expressed. In order to answer this question, we construct an intentional perspective on textual content. Based on an existing algorithm for extracting intent annotations from textual content we present a prototypical implementation to produce intent tag clouds, and describe a formative testing, illustrating how intent visualizations may support readers in understanding a text successfully. With the initial prototype, we conducted user studies of our intentional tag cloud visualization and a comparison with a traditional one that visualizes frequent terms. The evaluation's results indicate, that intent tag clouds have a positive effect on supporting users in grasping an author's intent.

Granitzer Michael, Rath Andreas S., Kröll Mark, Ipsmiller D., Devaurs Didier, Weber Nicolas, Lindstaedt Stefanie , Seifert C.

Machine Learning based Work Task Classification

Journal of Digital Information Management, 2009

Increasing the productivity of a knowledgeworker via intelligent applications requires the identification ofa user’s current work task, i.e. the current work context a userresides in. In this work we present and evaluate machine learningbased work task detection methods. By viewing a work taskas sequence of digital interaction patterns of mouse clicks andkey strokes, we present (i) a methodology for recording thoseuser interactions and (ii) an in-depth analysis of supervised classificationmodels for classifying work tasks in two different scenarios:a task centric scenario and a user centric scenario. Weanalyze different supervised classification models, feature typesand feature selection methods on a laboratory as well as a realworld data set. Results show satisfiable accuracy and high useracceptance by using relatively simple types of features.
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