State of the art
In this section, we investigate how current semantic search approaches
user support. We have identiﬁed four categories of semantic search
cording to the user interface they provide: i) form-based search
provide sophisticated web forms that allow users to specify queries, in
of choosing ontologies, classes, properties, and values; ii) RDF-based
languages fronted search engines, which provide sophisticated querying
A Search Engine for the Semantic Web 3
to support semantic search; iii) semantic-based keyword search engines,
hance the performance of traditional keyword search techniques by making
available semantic data; and iv) question answering tools, which exploit
semantic mark-up to answer questions asked in natural language format.
The SHOE search engine is one of the ﬁrst form-based semantic search
engines. It provides sophisticated web forms that allow users to specify
Such forms however are only suitable for those users who are fairly
the back-end ontologies and knowledge bases. Naive users have diﬃculties
understanding these forms. Further, they have diﬃculties in formulating
using their own view on the information they aim to ﬁnd.
The Corese search engine is an example of RDF-based querying language
fronted search engines. Other examples include the engines built in CS
Space and the SemanticWeb.org portal
2. Such search engines usually provide
a sophisticated querying language to support semantic data querying.
in order to be able to ask queries with these search engines, end users
will have to
be fairly familiar with both the back-end ontologies and the provided
The TAP search engine and the search engine presented in are examples
of semantic-based keyword search engines. The search process of such
engines often comprises two major steps: i) ﬁnding an instance match for
user keyword and ii) retrieving instances which are closely related to
match of the user keyword. Such search engines often provide
means to support the clustering of search results.
AquaLog and ORAKEL are examples of ontology-based question an-
swering engines. They make use of natural language processing
reformulate natural language queries into ontological triples (e.g., in
or into speciﬁc query languages (e.g., in ORAKEL). While these tools
be ideal for naive users, their performance on searching is heavily
the performance of the used natural language processing techniques.
All the tools described above are able to enhance the search performance
making use of available semantic data and their underlying ontologies.
partial exception of ontology-based question answering tools,
are however not suitable for naive users. One problem is knowledge
which is requiring users to be equipped with extensive knowledge of the
ontologies and knowledge bases (e.g. form-based search engines) or
like querying languages (e.g. RDF-based query language fronted search
in order to be able to formulate queries or to understand the search
Another problem is the lack of support for answering complex queries
presented by current semantic-based keyword search engines. These search
gines are often only able to accept one keyword as input and give back
semantic entities which are related to the keyword as results. Relation
search that ﬁnds relations between multiple keywords is not supported.
greatly limits the scope of user queries. For example, current
keyword search engines typically could not even handle simple queries
two keywords are involved such as news about phd students.