Körner C., Kröll Mark, Strohmaier M.
2009
Understanding search intent is often assumed to represent a critical barrier to the level of service that search engine providers can achieve. Previous research has shown that search queries differ with regard to intentional explicitness. We build on this observation and introduce Intentional Query Suggestion as a novel idea that aims to make searcher’s intent more explicit during search. In this paper, we present an algorithm for Intentional Query Suggestion and corresponding data from comparative experiments with traditional query suggestion mechanisms. Our results suggest that Intentional Query Suggestion 1) diversifies search result sets (i.e. it reduces result set overlap) and 2) exhibits interesting differences in terms of click-through rates
Kröll Mark, Strohmaier M.
2009
Knowledge about human goals has been found to be an important kind of knowledge for a range of challenging problems, such as goal recognition from peoples’ actions or reasoning about human goals. Necessary steps towards conducting such complex tasks involve (i) ac-quiring a broad range of human goals and (ii) making them accessible by structuring and storing them in a knowledge base. In this work, we focus on extracting goal knowledge from weblogs, a largely untapped resource that can be expected to contain a broad variety of hu-man goals. We annotate a small sample of web-logs and devise a set of simple lexico-syntactic patterns that indicate the presence of human goals. We then evaluate the quality of our pat-terns by conducting a human subject study. Re-sulting precision values favor patterns that are not merely based on part-of-speech tags. In fu-ture steps, we intend to improve these prelimi-nary patterns based on our observations
Jeanquartier Fleur, Kröll Mark, Strohmaier M.
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.