Group items tagged

understanding this content and making decisions based on it (especially in mission-critical situations) is not just a simple matter of consuming information. To effectively "make sense" of large, heterogeneous, and often unstructured content collections requires: - efficient, accurate, and context-based ways of extracting, filtering, and summarizing information; - better and more meaningful ways of organizing, visualizing, and interacting with the information; - faster, more objective methods for investigating hypotheses, detecting trends or patterns across multiple sources, and otherwise analyzing or interpreting information.

Tagging is a process that associates keywords with specific content. We did a rough analysis in our paper (reference below), and computed how often a keyword used by a user to tag an URL appears in the page content. We found that, on average, the chance that a tag comes from the content is 49%. This process produced a conservative estimate of tag occurrence in content, since we did not account for situations such as content changes for a given URL (e.g., dynamic content), typos (e.g., "Ajaz" instead of "Ajax"), abbreviations (e.g., "ad" instead of "advertisement"), compound tags (e.g., "SearchEngine"), and tags written in languages other than that of the content.

In the process of knowledge discovery, workers examine available information in order to make sense of it. By sensemaking, we mean interacting with and operating on the information with a variety of information processing mechanisms [3,18]. Previously, we introduced a concept that uses the spreadsheet metaphor with cells containing visualizations of complex data. In this paper, we extend and apply a cognitive model called "visual sensemaking " to the Visualization Spreadsheet. We use the task of making sense of a large Web site as a concrete example through out the paper for demonstration. Using a variety of visualization techniques, such as the Disk Tree and Cone Tree, we show that the interactions of the Visualization Spreadsheet help users draw conclusions from the overall relationships of the entire information set.

Sensemaking systems don't only help people find stuff faster. That's just the information retrieval part. The bigger story is about augmenting and amplifying our abilities to make sense. Sensemaking adds things like skimming, power reading, organizing, spotting patterns, tracing social networks, taking notes, summarizing, drilling for details, and flagging biases. Reading an article is different from reading a book, and that's different from reading from a collection or stream. Radically new forms of human-information interaction are being enabled by these new technologies. Sensemaking systems not only have front ends (visualization), but also back ends (content analytics and reasoning).