Visualizing a large journal collection using semantic indexing for use in search query refinement
We examine the scalability and utility of semantically mapping (visualizing) journals in a large scale (5.7+ million) science, technology and medical article digital library. This work is part of a larger research effort to evaluate semantic journal and article mapping for search query results refinement and visual contextualization in a large scale digital library. In this work the Semantic Vectors software package is parallelized and evaluated to create semantic distances between 2365 journals, from the sum of their full-text. This is used to create a journal semantic map whose production does scale and whose results are comparable to other maps of the scientific literature.
This presentation represents the state of the project, and will discuss the new work planned while at ANU.