KnowSe: Fostering user interaction context awareness

The CSCW area has recognized the concept of awareness as a critical issue to focus on (Schmidt et al., 2002) since “users who work together require adequate information about their environment” (Gross and Prinz, 2003). The environment of an individual encompasses her connections with other people, as well as with digital resources and actions (tasks or processes). If connections are not clear or hidden to the individual or to the group, the cost is a lack of awareness in the organization (McArthur and Bruza, 2003), which not only leads to inefficient cooperation but can even prevent it from being started. Unveiling the relations between persons, topics, tasks and processes to computer workers facilitates cooperative work by increasing the awareness of the personal social networks and the role of an individual in the organization, a project, or a group. These connections can be created and modeled manually but a better approach is to develop semi-automatic or even automatic tools to create and share them (McArthur and Bruza, 2003). Based on emails, McArthur and Bruza (2003) have computed such kind of connections, and suggest using more global corpora as well as taking into account dynamic ones

Detecting real user tasks by training on laboratory contextual attention metadata

Detecting the current task of a user is essential for providing her with contextualized and personalized support, and using Contextual Attention Metadata (CAM) can help doing so. Some recent approaches propose to perform automatic user task detection by means of task classifiers using such metadata. In this paper, we show that good results can be achieved by training such classifiers offline on CAM gathered in laboratory settings. We also isolate a combination of metadata features that present a significantly better discriminative power than classical ones

Exploiting the user interaction context for automatic task detection

Detecting the task a user is performing on her computer desktop is important for providing 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 (i) capturing simple user interaction events on the computer desktop and (ii) 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 `online' 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

Automatic detection of accommodation steps as an indicator of knowledge maturing

Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed

A network model approach to document retrieval taking into account domainknowledge

We preset a network model for context-based retrieval allowing for integrating domain knowledge into document retrieval. Based on the premise that the results provided by a network model employing spreading activation are equivalent to the results of a vector space model, we create a network representation of a document collection for retrieval. We extended this well explored approach by blending it with techniques from knowledge representation. This leaves us with a network model for finding similarities in a document collection by content-based as well as knowledge-based similarities

S.N.: Improving search on the semantic desktop using associative retrieval techniques

Abstract: While it is agreed that semantic enrichment of resources would lead to better search results, at present the low coverage of resources on the web with semantic information presents a major hurdle in realizing the vision of search on the Semantic Web. To address this problem we investigate how to improve retrieval performance in a setting where resources are sparsely annotated with semantic information. We suggest employing techniques from associative information retrieval to find relevant material, which was not originally annotated with the concepts used in a query. We present an associative retrieval system for the Semantic Desktop and show how the use of associative retrieval increased retrieval performance. Key Words: semantic desktop, associative information retrieval Category: H.3.3, I.2.4, I.2.6, I.2.1

Evaluation of an Information Retrieval System for the Semantic Desktop using Standard Measures from Information Retrieval

Evaluation of information retrieval systems is a critical aspect of information retrieval research. New retrieval paradigms, as retrieval in the Semantic Web, present an additional challenge for system evaluation as no off-the-shelf test corpora for evaluation exist. This paper describes the approach taken to evaluate an information retrieval system built for the Semantic Desktop and demonstrates how standard measures from information retrieval research are employed for evaluation. 1 Semantic Web information retrieval and evaluation Despite the youthfulness of Semantic Web information retrieval, a growing amount of proposed models and implemente