Andreas S. Rath

Analysis of machine learning techniques for context extraction

dasiaContext is keypsila conveys the importance of capturing the digital environment of a knowledge worker. Knowing the userpsilas context offers various possibilities for support, like for example enhancing information delivery or providing work guidance. Hence, user interactions have to be aggregated and mapped to predefined task categories. Without machine learning tools, such an assignment has to be done manually. The identification of suitable machine learning algorithms is necessary in order to ensure accurate and timely classification of the userpsilas context without inducing additional workload. This paper provides a methodology for recording user interactions and an analysis of supervised classification models, feature types and feature selection for automatically detecting the current task and context of a user. Our analysis is based on a real world data set and shows the applicability of machine learning techniques.

Synergizing standard and ad-hoc processes

In a knowledge-intensive business environment, knowledge workers perform their tasks in highly creative ways. This essential freedom required by knowledge workers often conflicts with their organizations need for standardization, control, and transparency. Within this context, the research project DYONIPOS aims to mitigate this contradiction by supporting the process engineer with insights into the process executers working behavior. These insights constitute the basis for balanced process modeling. DYONIPOS provides a process engineer support environment with advanced process modeling services, such as process visualization, standard process validation, and ad-hoc process analysis and optimization services.

Automating Knowledge Transfer and Creation in Knowledge Intensive Business Processes

It is a well known fact that a wealth of knowledge lies in the head of employees making them one of the most or even the most valuable asset of organisations. But often this knowledge is not documented and organised in knowledge systems as required by the organisation, but informally shared. Of course this is against the organisation’s aim for keeping knowledge reusable as well as easily and permanently available independent of individual knowledge workers.

htmlButler – Wrapper Usability Enhancement through Ontology Sharing and Large Scale Cooperation

The htmlButler project aims at enhancing the usability of visual wrapper technology while preserving versatility. htmlButler will allow, for an untrained user who has only the most basic web knowledge, to visually specify simple but useful wrappers and, for a more tech-savvy user, to visually or otherwise specify more complex wrappers. htmlButler was started 2005/2 and is based on visual wrapping technology research carried out in the Lixto project since 2000. What is new in htmlButler is that (a) the application is entirely server based, the user accessing it through his or her standard browser, (b) because of the centralized wrapper configuration and processing, the knowledge about popular wrappers can be leveraged to facilitate the specification of wrappers for new users, and (c) users can contribute narrow and precise ontologies that help the system in recognizing potential meaning in web pages, thereby alleviating the complexity of future wrapper configurations

EXPLOITING THE USER INTERACTION CONTEXT FOR AUTOMATIC TASK DETECTION

Detecting the task a user is performing on his/her computer desktop is important in order to provide him/her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (1) capturing simple user interaction events on the computer desktop and (2) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task-detection performance. We also argue that good results can be achieved by training task classifiers “offline” on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones. 1

Context-Aware Recommendation for Work-Integrated Learning

Within this chapter we first outline the important role learning plays within knowledge work and its impact on productivity. As a theoretical background we introduce the paradigm of Work-Integrated Learning (WIL) which conceptualizes informal learning at the workplace and takes place tightly intertwined with the execution of work tasks. Based on a variety of in-depth knowledge work studies we identify key requirements for the design of work-integrated learning support. Our focus is on providing learning support during the execution of work tasks (instead of beforehand), within the work environment of the user (instead of within a separate learning system), and by repurposing content for learning which was not originally intended for learning (instead of relying on the expensive manual creation of learning material). In order to satisfy these requirements we developed a number of context-aware knowledge services. These services integrate semantic technologies with statistical approaches which perform well in the face of uncertainty. These hybrid knowledge services include the automatic detection of a user’s work task, the ‘inference’ of the user’s competencies based on her past activities, context-aware recommendation of content and colleagues, learning opportunities, etc. A summary of a 3 month in-depth summative workplace evaluation at three testbed sites concludes the chapter.