Generative semantic clustering in spatial hypertext

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 rela...

• Type: Conference
• Year: 2005
• Where: DOCENG
• Authors: Andruid Kerne, Eunyee Koh, Vikram Sundaram, J. Michael Mistrot
• Keywords: DOCENG 2005, Document Analysis, Relatedness Potential, Spatial Hypertext, Spatial Hypertext Documents