Know Center Research

Publikationen

Stanisavljevic Darko, Hasani-Mavriqi Ilire, Lex Elisabeth, Strohmaier M., Helic Denis
2016
In this paper we assess the semantic stability of Wikipedia by investigat-ing the dynamics of Wikipedia articles’ revisions over time. In a semantically stablesystem, articles are infrequently edited, whereas in unstable systems, article contentchanges more frequently. In other words, in a stable system, the Wikipedia com-munity has reached consensus on the majority of articles. In our work, we measuresemantic stability using the Rank Biased Overlap method. To that end, we prepro-cess Wikipedia dumps to obtain a sequence of plain-text article revisions, whereaseach revision is represented as a TF-IDF vector. To measure the similarity betweenconsequent article revisions, we calculate Rank Biased Overlap on subsequent termvectors. We evaluate our approach on 10 Wikipedia language editions includingthe five largest language editions as well as five randomly selected small languageeditions. Our experimental results reveal that even in policy driven collaborationnetworks such as Wikipedia, semantic stability can be achieved. However, there aredifferences on the velocity of the semantic stability process between small and largeWikipedia editions. Small editions exhibit faster and higher semantic stability than large ones. In particular, in large Wikipedia editions, a higher number of successiverevisions is needed in order to reach a certain semantic stability level, whereas, insmall Wikipedia editions, the number of needed successive revisions is much lowerfor the same level of semantic stability.
Yusuke Fukazawa, Kröll Mark, Strohmaier M., Ota Jun
2016
Task-models concretize general requests to support users in real-world scenarios. In this paper, we present an IR based algorithm (IRTML) to automate the construction of hierarchically structured task-models. In contrast to other approaches, our algorithm is capable of assigning general tasks closer to the top and specific tasks closer to the bottom. Connections between tasks are established by extending Turney’s PMI-IR measure. To evaluate our algorithm, we manually created a ground truth in the health-care domain consisting of 14 domains. We compared the IRTML algorithm to three state-of-the-art algorithms to generate hierarchical structures, i.e. BiSection K-means, Formal Concept Analysis and Bottom-Up Clustering. Our results show that IRTML achieves a 25.9% taxonomic overlap with the ground truth, a 32.0% improvement over the compared algorithms.
Kröll Mark, Strohmaier M.
2015
People willingly provide more and more information about themselves on social media platforms. This personal information about users’ emotions (sentiment) or goals (intent) is particularly valuable, for instance, for monitoring tools. So far, sentiment and intent analysis were conducted separately. Yet, both aspects can complement each other thereby informing processes such as explanation and reasoning. In this paper, we investigate the relation between intent and sentiment in weblogs. We therefore extract ~90,000 human goal instances from the ICWSM 2009 Spinn3r dataset and assign respective sentiments. Our results indicate that associating intent with sentiment represents a valuable addition to research areas such as text analytics and text understanding.
Kröll Mark, Strohmaier M.
2010
In this paper, we introduce the idea of Intent Analysis, which is to create a profile of the goals and intentions present in textual content. Intent Analysis, similar to Sentiment Analysis, represents a type of document classification that differs from traditional topic categorization by focusing on classification by intent. We investigate the extent to which the automatic analysis of human intentions in text is feasible and report our preliminary results, and discuss potential applications. Inaddition, we present results from a study that focused on evaluating intent profiles generated from transcripts of American presidential candidate speeches in 2008.
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
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.
Strohmaier M., Prettenhofer P., Kröll Mark
2008
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.
Strohmaier M., Horkoff Jennifer, Yu E., Aranda Jorge, Easterbrook Steve
2008
A considerable amount of effort has been placed into the investigation of i* modeling as a tool for early stage requirements engineering. However, widespread adoption of i* models in the requirements process has been hindered by issues such as the effort required to create the models, coverage of the problem context, and model complexity. In this work, we explore the feasibility of pattern application to address these issues. To this end, we perform both an exploratory case study and initial experiment to investigate whether the application of patterns improves aspects of i* modeling. Furthermore, we develop a methodology which guides the adoption of patterns for i* modeling. Our findings suggest that applying model patterns can increase model coverage, but increases complexity, and may increase modeling effort depending on the experience of the modeler. Our conclusions indicate situations where pattern application to i* models may be beneficial.
Strohmaier M., Lindstaedt Stefanie
2007
Purpose: The purpose of this contribution is to motivate a new, rapid approachto modeling knowledge work in organizational settings and to introducea software tool that demonstrates the viability of the envisioned concept.Approach: Based on existing modeling structures, the KnowFlowr Toolsetthat aids knowledge analysts in rapidly conducting interviews and in conductingmulti-perspective analysis of organizational knowledge work is introduced.Findings: It is demonstrated how rapid knowledge work visualization can beconducted largely without human modelers by developing an interview structurethat allows for self-service interviews. Two application scenarios illustrate thepressing need for and the potentials of rapid knowledge work visualizations inorganizational settings.Research Implications: The efforts necessary for traditional modeling approachesin the area of knowledge management are often prohibitive. Thiscontribution argues that future research needs to take economical constraintsof organizational settings into account in order to be able to realize the fullpotential of knowledge work management.Practical Implications: This work picks up a problem identified in practiceand proposes the novel concept of rapid knowledge work visualization for makingknowledge work modeling in organizations more feasible.Value: This work develops a vision of rapid knowledge work visualization andintroduces a tool-supported approach that addresses some of the identified challenges.
Rollett H., Lux M., Strohmaier M., Dösinger G.
2007
While there is a lot of hype around various concepts associated with the term Web 2.0 in industry, little academic research has so far been conducted on the implications of this new approach for the domain of education. Much of what goes by the name of Web 2.0 can, in fact, be regarded as new kinds of learning technologies, and can be utilised as such. This paper explains the background of Web 2.0, investigates the implications for knowledge transfer in general, and then discusses its particular use in eLearning contexts with the help of short scenarios. The main challenge in the future will be to maintain essential Web 2.0 attributes, such as trust, openness, voluntariness and self-organisation, when applying Web 2.0 tools in institutional contexts.
Strohmaier M., Lux M., Granitzer Michael, Scheir Peter, Liaskos S., Yu E.
2007
Strohmaier M.
2005
Hrastnik J., Rollett H., Strohmaier M.
2004
Lindstaedt Stefanie , Farmer J., Hrastnik J., Rollett H., Strohmaier M.
2003
Personalisierbare Portale als Fenster zu Unternehmensgedachtnissen finden ¨in der Praxis immer haufiger Anwendung. Bei dem Design dieser Portale stellt sich die ¨Frage nach der Strukturierung der Informationen: auf der einen Seite soll der tagliche ¨Arbeitsprozess unterstutzt werden, auf der anderen Seite sollen aber auch Informa- ¨tionen zuganglich gemacht werden, die den Prozess in einen gr ¨ oßeren Kontext set- ¨zen. Unsere Erfahrungen bei der Portalentwicklungen haben gezeigt, dass zwei unterschiedlicheStrategien Anwendung finden: Prozessfokus und Wissensfokus. Die Wahlder individuellen Strategie hangt einerseits von den Zielen ab, die das Portal erf ¨ ullen ¨soll. Andererseits hangen sie aber auch von der Ausgangssituation im anwendenden ¨Unternehmen ab. Dieser Beitrag stellt die beiden Designstrategien vor und identifiziertRahmenbedingungen, die bei der Wahl der Stategie helfen konnen.
Strohmaier M.
2003
Strohmaier M.
2003
Lindstaedt Stefanie , Strohmaier M., Rollett Herwig, Hrastnik Janez, Bruhnsen Karin, Droschl Georg, Gerold Markus
2002
One of the first question each knowledge management project facesis: Which concrete activities are referred to under the name of knowledgemanagement and how do they relate to each other? To help answer this questionand to provide guidance when introducing knowledge management we havedeveloped KMap. KMap is an environment which supports a practitioner in theinteractive exploration of a map of knowledge management activities. Theinteraction helps trigger interesting questions crucial to the exploration of thesolution space and makes hidden argumentation lines visible. KMap is not anew theory of knowledge management but a pragmatic “object to think with”and is currently in use in two case studies.
Lindstaedt Stefanie , Strohmaier M.
2002