HYKE LogoSemantic Search


Home
Introduction
State of the art
An overview of SemSearch
The Google-like query interface
Making sense of the user query
Translating the user query into formal queries
Implementation and experimental evaluation
Conclusions and future work
1st Workshop on Semantic Search
2nd Workshop on Semantic Search
3rd Workshop on Semantic Search
4th Workshop on Semantic Search
 Introduction

One important goal of the semantic web is to make the meaning of information
explicit through semantic mark-up, thus enabling more ective access to knowl-
edge contained in heterogeneous information environments, such as the web. Se-
mantic search plays an important role in realizing this goal, as it promises to
produce precise answers to user’s queries by taking advantage of the availability
of explicit semantics of information.
For example, when searching for news stories about phd students, with tra-
ditional searching technologies, we often could only get news entries in which
the term “phd students” appears. Those entries which mention the names of
students but do not use the term “phd students” directly will be missed out.
Such news entries however are often the ones that the user is really interested
in. In the context of the semantic web, where the meaning of web content is
made explicit, the semantic meaning of the keyword (which is a general concept
in the example of phd students) can be figured out. Furthermore, the underly-
ing semantic relations of metadata can be exploited to support the retrieving of
information which is closely related to the keyword. Thereby, the search perfor-
mance can be significantly improved by expanding the query with instances and
relations.
A number of semantic search tools have been recently developed.
Our overview of the state-of-art semantic search tools reveals that while
these tools do enhance the performance of traditional search technologies, they
are however not suitable for naive users, i.e. ordinary end users who are not
necessarily familiar with domain specific semantic data, ontologies, or SQL-like
query languages. The semantic search engine we present here, SemSearch, pro-
vides several means to address this issue.
– SemSearch tackles the problem of knowledge overhead by supporting a Google-
like query interface. As will be described in Section 4, the proposed query
interface provides a simple but powerful way of specifying queries.
– SemSearch addresses the problem of existing semantic-based keyword search
engines by supporting complex queries. It provides comprehensive means to
make sense of user queries and to translate them into formal queries.
– SemSearch takes the focus of user queries into consideration when generating
formal queries, thus being able to produce precise results that on the one
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 having
to know the underlying semantic data or to learn a SQL-like query language. A
prototype of the search engine has been implemented and applied in the semantic
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 end user
support. We then present an overview of SemSearch in Section 3. Thereafter,
we explain the Google-like query interface in Section 4. We describe the major
steps of the semantic search process in sections 5 and 6. In Section 7, we describe
the implementation of SemSearch and the experimental evaluation. Finally, in
Section 8, we conclude our paper with a discussion of our contributions and
future work.