Visualising semantic spaces and author co-citation networks in digital libraries

Abstract

AbstractThis paper describes the development and application of visualisation techniques for users to access and explore information in a digital library effectively and intuitively. Salient semantic structures and citation patterns are extracted from several collections of documents, including the ACM SIGCHI conference proceedings (19951997) and ACM Hypertext conference proceedings (19871998), using Latent Semantic Indexing and Pathfinder Network Scaling. The unique spatial metaphor leads to a natural combination of search and navigation within the same semantic space in a 3-dimensional virtual world. Author co-citation patterns are visualised through a number of author co-citation maps in attempts to reveal the structure of the field of hypertext, including an overall co-citation map of 367 authors and three periodical maps. These maps highlight predominant research areas in the field. This approach provides a means of transcending the boundaries of collections of documents and visualising more profound patterns in terms of semantic structures and co-citation networks.

Visualizing the marrow of science

“…In our opinion, PFNET with pruning parameters r ϭ ∞, and q ϭ n Ϫ 1 is the prime option for eliminating less significant relationships while preserving and highlighting the most essential ones, and capturing the underlying intellectual structure in a economical way. Although PFNET has been used in the fields of Bibliometrics, Informetrics, and Scientometrics since 1990 (Fowler & Dearhold, 1990), its introduction in citation was due to the hand of Chen (1998Chen ( , 1999, who introduced a new form of organizing, visualizing, and accessing information. The end effect is the pruning of all paths except those with the single highest (or tied highest) cocitation counts between categories (White, 2001).…”

Characterizing and Mining the Citation Graph of the Computer Science Literature

“…Redner (Redner 1998) has focused on the statistical distribution of the number of citations of the scientific literature. Chen (Chen 1999;Chen 2001) developed a set of methods that extends and transforms traditional author co-citation analysis by heuristically extracting structural patterns from scientific literature for visualization as a 3D virtual map. As to structural analysis of large scale networks, Broder et al (Broder, Kumar, Maghoul, Raghavan, Rajagopalan, Stata, Tomkins, and Wiener 2000) studied various properties of Web graph including its diameter, degree distributions, connected components, and macroscopic structure, proposing a bow tie model of the Web.…”

On Visualizing Heterogeneous Semantic Networks from Multiple Data Sources

“…In the digital library context, semantic networks have always been a useful paradigm for representing knowledge found in text and database records which in turn helps users to more effectively and quickly search and navigate information. Some often cited examples of semantic networks in digital libraries include author co-citation networks [2], keyword co-occurrence networks [10], etc. In this paper, we focus on social networks as kinds of semantic networks found in text collections and databases.…”