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
Körner C., Kröll Mark, Strohmaier M.
2009
Intentional Query Suggestion: Making User Goals More Explicit During Search
Workshop on Web Search Click Data WSCD'09
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
Extracting Human Goals from Weblogs
Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML) 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
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
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.