Modern Information Retrieval Chapter 10: User Interfaces and Visualization

Contents

   
5. Categories for Results Set Context

In section we saw the use of category or directory information for providing overviews of text collection content. Category metadata can also be used to place the results of a query in context.

For example, the original formulation of SuperBook allowed navigation within a highly structured document, a computer manual. The CORE project  extended the main idea to a collection of over 1000 full-text chemistry articles. A study of this representation demonstrated its superiority to a standard search system on a variety of task types [#!egan91!#]. Since a table of contents is not available for this collection, context is provided by placing documents within a category hierarchy containing terms relevant to chemistry. Documents assigned a category are listed when that category is selected for more detailed viewing, and the categories themselves are organized into a hierarchy, thus providing a hierarchical view on the collection.

  

Figure: The DynaCat interface for viewing category labels that correspond to query types [#!pratt97!#].

retrieval results!DynaCat DynaCat

Another approach to using predefined categories to provide context for retrieval results is demonstrated by the DynaCat  system [#!pratt97!#]. The DynaCat system organizes retrieved documents according to which types of categories, selected from the large MeSH taxonomy, are known in advance to be important for a given query type. DynaCat begins with a set of query types known to be useful for a given user population and collection. One query type can encompass many different queries. For example, the query type `Treatment-Adverse Effects' covers queries such as `What are the complications of a mastectomy?' as well as `What are the side-effects of aspirin?' Documents are organized according to a set of criteria associated with each query type. These criteria specify which types of categories that are acceptable to use for organizing the documents and consequently, which categories should be omitted from the display. Once categories have been assigned to the retrieved documents, a hierarchy is formed based on where the categories exist within MeSH. The algorithm selects only a subset of the category labels that might be assigned to the document to be used in the organization.=-1

Figure shows the results for a query on breast cancer prevention. The interface is tiled into three windows. The top window displays the user's query and the number of documents found. The left window shows the categories in the first two levels of the hierarchy, providing a table of contents view of the organization of search results. The right pane displays all the categories in the hierarchy and the titles of the documents that belong in those categories.

An obstacle to using category labels to organize retrieval results is the requirement of precompiled knowledge about which categories are of interest for a particular user or a particular query type. The SONIA system  [#!sahami98!#] circumvents this problem by using a combination of unsupervised and supervised methods to organize a set of documents. The unsupervised method (document clustering similar to Scatter/Gather) imposes an initial organization on a user's personal information collection or on a set of documents retrieved as the result of a query. The user can then invoke a direct manipulation interface to make adjustments to this initial clustering, causing it to align more closely with their preferences (because unsupervised methods do not usually produce an organization that corresponds to a human-derived category structure [#!hearst98a!#]). The resulting organization is then used to train a supervised text categorization algorithm which automatically classifies any new documents that are added to the collection. As the collection grows it can be periodically reorganized by rerunning the clustering algorithm and redoing the manual adjustments.