Measuring graph topology for interactive temporal event detection
The Web and other text collections are full of “stories”: sets of statements that evolve over time, made in fast-growing streams of documents. Even if one reads a specific source every day and/or subscribes to a selection of feeds, one may easily lose track; in addition, it is difficult to reconstruct a story already in the past. In this paper, we present the STORIES methods and tool for (a) learning an abstracted story representation from a collection of time-indexed documents; (b) visualizing it in a way that encourages users to interact and explore in order to discover temporal “story stages” depending on their interests; (c) supporting the search for documents and facts that pertain to the user-constructed story stages; and (d) navigating in document space along multiple meaningful dimensions of document similarity and relatedness. This combination provides users with more control of their customized story understanding, semantically in story space as well as between the underlying documents. In an evaluation, we investigated whether it is possible to use topological properties of the temporal story graphs for pointing users to more promising parts of the story space. The results indicated that global properties of the graphs are useful for this purpose, while properties local to individual nodes are less meaningful.