Kröll Mark, Prettenhofer P., Strohmaier M.
2009
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
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
Kröll Mark, Koerner C.
2009
Annotations represent an increasingly popular means for organizing, categorizing and finding resources on the “social” web. Yet, only a small portion of the total resources available on the web are annotated. In this paper, we describe a prototype - iTAG - for automatically annotating textual resources with human intent, a novel dimension of tagging. We investigate the extent to which the automatic analysis of human intentions in textual resources is feasible. To address this question, we present selected evidence from a study aiming to automatically annotate intent in a simplified setting, that is transcripts of speeches given by US presidential candidates in 2008
Kröll Mark
2009
Access to knowledge about common human goals has been found critical for realizing the vision of intelligent agents acting upon user intent on the web. Yet, the ac-quisition of knowledge about common human goals rep-resents a major challenge. In a departure from existing approaches, this paper investigates a novel resource for knowledge acquisition: The utilization of search query logs for this task. By relating goals contained in search query logs with goals contained in existing com-monsense knowledge bases such as ConceptNet, we aim to shed light on the usefulness of search query logs for capturing knowledge about common human goals. The main contribution of this paper consists of an empirical study comparing common human goals contained in two large search query logs (AOL and Microsoft Research) with goals contained in the commonsense knowledge base ConceptNet. The paper sketches ways how goals from search query logs could be used to address the goal acquisition and goal coverage problem related to com-monsense knowledge bases
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.
Granitzer Michael, Rath Andreas S., Kröll Mark, Ipsmiller D., Devaurs Didier, Weber Nicolas, Lindstaedt Stefanie , Seifert C.
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.