How People Re-find Information When the Web Changes

This paper investigates how people return to information in a dynamic information environment. For example, a person might want to return to Web content via a link encountered earlier on a Web page, only to learn that the link has since been removed. Changes can benefit users by providing new information, but they hinder returning to previously viewed information. The observational study presented here analyzed instances, collected via a Web search, where people expressed difficulty re-finding information because of changes to the information or its environment. A number of interesting observations arose from this analysis, including that the path originally taken to get to the information target appeared important in its re-retrieval, whereas, surprisingly, the temporal aspects of when the information was seen before were not. While people expressed frustration when problems arose, an explanation of why the change had occurred was often sufficient to allay that frustration, even in the absence of a solution. The implications of these observations for systems that support re-finding in dynamic environments are discussed.

Prototype of a Configurable Web-Based Assessment System

The MIT Prototype Educational Assessment System provides subjects and courses at MIT with the ability to perform online assessment. The system includes polices to handle harassment and electronic "flaming" while protecting privacy. Within these frameworks, individual courses and subjects can make their own policy decisions about such matters as to when assessments can occur, who can submit assessments, and how anonymous assessments are. By allowing assessment to take place continually and allowing both students and staff to participate, the system can provide a forum for the online discussion of subjects. Even in the case of scheduled assessments, the system can provide advantages over end-of-term assessment, since the scheduled assessments can occur several times during the semester, allowing subjects to identify and adjust those areas that could use improvement. Subjects can also develop customized questionnaires, perhaps in response to previous assessments, to suit their needs.

Context Mediation in the Semantic Web: Handling OWL Ontology and Data Disparity through Context Interchange

The COntext INterchange (COIN) strategy is an approach to solving the problem of interoperability of semantically heterogeneous data sources through context mediation. COIN has used its own notation and syntax for representing ontologies. More recently, the OWL Web Ontology Language is becoming established as the W3C recommended ontology language. We propose the use of the COIN strategy to solve context disparity and ontology interoperability problems in the emerging Semantic Web – both at the ontology level and at the data level. In conjunction with this, we propose a version of the COIN ontology model that uses OWL and the emerging rules interchange language, RuleML.

Validating Co-Training Models for Web Image Classification

Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known to work well when the multiple descriptions are conditional independent given the class of the object. The presence of multiple descriptions of objects in the form of text, images, audio and video in multimedia applications appears to provide redundancy in the form that may be suitable for co-training. In this paper, we investigate the suitability of utilizing text and image data from the Web for co-training. We perform measurements to find indications of conditional independence in the texts and images obtained from the Web. Our measurements suggest that conditional independence is likely to be present in the data. Our experiments, within a relevance feedback framework to test whether a method that exploits the conditional independence outperforms methods that do not, also indicate that better performance can indeed be obtained by designing algorithms that exploit this form of the redundancy when it is present.