For a collection of digitized monographs in a subject domain, a domain meta-index provides a summary of domain concepts, and a structured vocabulary to support a scholar’s navigation and search. We present a prototype of a Meta-index User Interface (MUI) that provides views of a domain at three levels: summarizing and comparing domains, exposing the regularities of a domain’s vocabulary, and displaying book information and page content related both to objectively-representative books, and to specific user searches.
Concept taxonomies such as MeSH, the ACM Computing Classifcation System and the NY Times Subject Headings are frequently used to help organize data. They typically consist of a set of concept names organized in a hierarchy. However, these names and structure are often not sufficient to fully capture the intended meaning of a taxonomy node, and particularly non-experts may have difficulty navigating and placing data into the taxonomy. This paper introduces two semi-supervised topic models that automatically augment a given taxonomy with many additional keywords by leveraging a corpus of multi-labeled documents. Our experiments show that users nd the topics benecial for taxonomy interpretation, substantially increasing their cataloging accuracy. Furthermore, the models provide a better information rate compared to Labeled LDA.