Improving browsing in digital libraries with keyphrase indexes
Browsing accounts for much of people's interaction with digital libraries, but it is poorly supported by standard search engines. Conventional systems often operate at the wrong level, indexing words when people think in terms of topics, and returning documents when people want a broader view. As a result, users cannot easily determine what is in a collection, how well a particular topic is covered, or what kinds of queries will provide useful results. We have built a new kind of search engine, Keyphind, that is explicitly designed to support browsing. Automatically extracted keyphrases form the basic unit of both indexing and presentation, allowing users to interact with the collection at the level of topics and subjects rather than words and documents. The keyphrase index also provides a simple mechanism for clustering documents, refining queries, and previewing results. We compared Keyphind to a traditional query engine in a small usability study. Users reported that certain kinds of browsing tasks were much easier with the new interface, indicating that a keyphrase index would be a useful supplement to existing search tools.
digital libraries browsing interfaces text mining keyphrase extraction machine learning