Generative semantic clustering in spatial hypertext

Abstract

This paper presents an iterative method for generative semantic clustering of related information elements in spatial hypertext documents. The goal is to automatically organize them in ways that are meaningful to the user. We consider a process in which elements are gradually added to a spatial hypertext. The method for generating meaningful layout is based on a quantitative model that measures and represents the mutual relatedness between each new element and those already in the document. The measurement is based on attributes such as metadata, term vectors, user interest expressions, and document locations. We call this model relatedness potential, because it represents how much the new element is related and thus attracted to existing elements as a vector field across the space. Using this field as a gradient potential, the new element will be placed near the most attracted elements, forming clusters of related elements. The relative magnitude of contribution of attributes to relatedness potential can be controlled through an interactive interface.Unlike prior clustering methods such as k-means and selforganizing-maps, relatedness potential works well in iterative systems, in which the collection of elements is not defined a priori. Further, users can invoke relatedness potential to re-cluster elements, as they engage in on-the-fly provisional acts of direct manipulation reorganization and latching of a few most significant elements. A preliminary study indicates that users find this method generates spatial hypertext documents that are easier to read.

Browsing Hierarchy Construction by Minimum Evolution

“…Other clustering approaches allow users to tweak the features or constraints in clusters. For example, Kerne et al clusters documents based on meta-data such as document location [Kerne et al 2005]. This process allows the user to participate in feature selection and interact with the clustering algorithm by setting relative weights for the features.…”

“…The patterns can be obtained either manually [Berland and Charniak 1999;Kozareva et al 2008] or via automatic bootstrapping [Hearst 1992;Etzioni et al 2005;Girju et al 2003;Ravichandran and Hovy 2002;Pantel and Pennacchiotti 2006]. Traditional research on taxonomy construction focuses on extracting local relations between node pairs [Hearst 1992;Berland and Charniak 1999;Ravichandran and Hovy 2002;Girju et al 2003;Etzioni et al 2005;Pantel and Pennacchiotti 2006;Kozareva et al 2008]. Recent efforts have involved building full taxonomies.…”