the effectiveness of the various methods. Prescriptions are given for conducting text re-trieval operations iteratively using relevance feedback. Introduction to Relevance Feedback It is well known that the original query formulation process is not transparent to most information system users. In particular

of uncertain data. It requires only minimal user involve-ment during data integration, because most semantic uncer-tainty and conflicts can be resolved by exploiting user feed-back on query results, thus effectively postponing user in-volvement to query time when a user already interacts with the system. We

to contributing information; specifically, we investigate semi-automated means to extract useful information from standard types of user feedback. We explore this approach in the con-text of a geographic wiki and route-planning system for bi-cyclists. We analyzed naturally occurring textual route feed-back

Heuristic evaluation is an informal method of usability analysis where a number of evaluators are presented with an interface design and asked to comment on it. Four ex-periments showed that individual evaluators were mostly quite bad at doing such heuristic evaluations and that they only found between 20 and 51 % of the usability problems in the interfaces they evaluated. On the other hand, we could aggregate the evaluations from several evaluators to a single evaluation and such aggregates do rather well, even when they consist of only three to five people.

segmentation, and accounting for user-feedback during a retrieval session. We present a new learning algorithm that relies on belief propagation to account for both positive and negative examples of the user’s interests. 1

The potential for machine learning systems to improve via a mutually beneficial exchange of information with users has yet to be explored in much detail. Previously, we found that users were willing to provide a generous amount of rich feedback to machine learning systems, and that the types

the communication path, while still providing full protection. The model presented by U-Net allows for the construction of protocols at user level whose performance is only limited by the capabilities of network. The architecture is extremely flexible in the sense that traditional protocols like TCP and UDP

missing arguments, contain synonyms or is otherwise sloppy and determines possible interpretations. It then presents these interpretations to the user, who can then pick one to execute. The interpretations are presented to the user as she types, providing responsive feedback on the state of the system

and other research outputs

incrementally is represented uniformly in the same knowledge base with the domain plans that are under discussion. In this way, the text plan and the domain plans are both accessible for analyzing the listener’s feedback. IDP can interpret vaguely articulated feedback, generate concise replies and metacomments