Abstract :
The Web provides a massive knowledge source, as do
intranets and other electronic document collections.
However, much of that knowledge is encoded implicitly
and cannot be applied directly without processing into
some more appropriate structures. Searching, browsing,
question answering, for example, could all benefit from
domain-specific knowledge contained in the documents,
and in applications such as simple search we do not
actually need very “deep” knowledge structures such as
ontologies, but we can get a long way with a model of the
domain that consists of term hierarchies. We combine
domain knowledge automatically acquired by exploiting
the documents’ markup structure with knowledge
extracted on the fly to assist a user with ad hoc search
requests. Such a search system can suggest query modification
options derived from the actual data and thus
guide a user through the space of documents. This article
gives a detailed account of a task-based evaluation
that compares a search system that uses the outlined
domain knowledge with a standard search system. We
found that users do use the query modification suggestions
proposed by the system. The main conclusion we
can draw from this evaluation, however, is that users prefer
a system that can suggest query modifications over a
standard search engine, which simply presents a ranked
list of documents. Most interestingly, we observe this
user preference despite the fact that the baseline system
even performs slightly better under certain criteria.
