Topic Space Research Articles

Lexically-generated subject hierarchies for browsing large collections

Developing intuition for the content of a digital collection is difficult. Hierarchies of subject terms allow users to explore the space of topics that a collection covers, to form and specialize u...

A Model for Distributed Curriculum on the World Wide Web

Educational material abounds on the Web, but still most designers of instructional material must start from scratch. Emerging interoperability standards for educational resources will accelerate progress toward reusability of this material. We describe a framework called Model for Distributed Curriculum (MDC) that uses a server architecture to allow one Web-based tutorial to include a specification for another a virtual tutorial, where the best fit to this specification will automatically be found at run time. This allows a designer of instructional material to re-use other Web-based tutorials without having to know what tutorials will exist at run time, i.e. without having to search and analyze material on the Web. Key to MDC is organizing Web-based curriculum into a topic space rather than a page space, and making use of emerging metadata standards for educational resources to describe the pedagogically relevant properties of Web-based tutorials. Reviewers: Franz Schmalhofer (U. Kaiserslautern), Markus Stolze (IBM Research), Arthur Stutt (Open U.) This research is based on work supported by the National Science Foundation and the Advanced Research Projects Agency under Cooperative Agreement No. CDA-940860

The Society for Vascular Surgery: Then and now

The Society for Vascular Surgery: Then and now

情報管理入門

Transfer learning utilizes labeled data available from some related domain (source domain) for achieving effective knowledge transformation to the target domain. However, most state-of-the-art cross-domain classification methods treat documents as plain text and ignore the hyperlink (or citation) relationship existing among the documents. In this paper, we propose a novel cross-domain document classification approach called Link-Bridged Topic model (LBT). LBT consists of two key steps. Firstly, LBT utilizes an auxiliary link net- work to discover the direct or indirect co-citation relationship among documents by embedding the background knowledge into a graph kernel. The mined co-citation relation- ship is leveraged to bridge the gap across different domains. Secondly, LBT simultaneously combines the content information and link structures into a unified latent topic model. The model is based on an assumption that the documents of source and target domains share some common topics from the point of view of both content information and link struc- ture. By mapping both domains data into the latent topic spaces, LBT encodes the knowl- edge about domain commonality and difference as the shared topics with associated differential probabilities. The learned latent topics must be consistent with the source and target data, as well as content and link statistics. Then the shared topics act as the bridge to facilitate knowledge transfer from the source to the target domains. Experiments on different types of datasets show that our algorithm significantly improves the general- ization performance of cross-domain document classification.