One important goal of the semantic web is to make the meaning of
explicit through semantic mark-up, thus enabling more ective access to
edge contained in heterogeneous information environments, such as the
mantic search plays an important role in realizing this goal, as it
produce precise answers to user’s queries by taking advantage of the
of explicit semantics of information.
For example, when searching for news stories about phd students, with
ditional searching technologies, we often could only get news entries in
the term “phd students” appears. Those entries which mention the names
students but do not use the term “phd students” directly will be missed
Such news entries however are often the ones that the user is really
in. In the context of the semantic web, where the meaning of web content
made explicit, the semantic meaning of the keyword (which is a general
in the example of phd students) can be ﬁgured out. Furthermore, the
ing semantic relations of metadata can be exploited to support the
information which is closely related to the keyword. Thereby, the search
mance can be signiﬁcantly improved by expanding the query with instances
A number of semantic search tools have been recently developed.
Our overview of the state-of-art semantic search tools reveals that
these tools do enhance the performance of traditional search
are however not suitable for naive users, i.e. ordinary end users who
necessarily familiar with domain speciﬁc semantic data, ontologies, or
query languages. The semantic search engine we present here, SemSearch,
vides several means to address this issue.
– SemSearch tackles the problem of knowledge overhead by supporting a
like query interface. As will be described in Section 4, the proposed
interface provides a simple but powerful way of specifying queries.
– SemSearch addresses the problem of existing semantic-based keyword
engines by supporting complex queries. It provides comprehensive means
make sense of user queries and to translate them into formal queries.
– SemSearch takes the focus of user queries into consideration when
formal queries, thus being able to produce precise results that on the
hand satisfy user queries and on the other hand are self-explanatory and
understandable by end users.
Thus, SemSearch makes it possible for ordinary end users to harvest the
beneﬁts of semantic search and other semantic web technologies without
to know the underlying semantic data or to learn a SQL-like query
prototype of the search engine has been implemented and applied in the
web portal of our lab1. An initial evaluation shows promising results.
The rest of the paper is organized as follows. We begin in Section 2 by
investigating how current semantic search tools approach the issue of
support. We then present an overview of SemSearch in Section 3.
we explain the Google-like query interface in Section 4. We describe the
steps of the semantic search process in sections 5 and 6. In Section 7,
the implementation of SemSearch and the experimental evaluation.
Section 8, we conclude our paper with a discussion of our contributions