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tem architecture is described in Section 3, with an
overview of the main components. In Section 4 we
particularly discuss the visual query editor. Finally,
we end up with the conclusion and outline some fu-
ture work.
2 RELATED WORK
In this section we will give an overview of similar
research projects considered in the area of graphical
query construction, particularly in the context of on-
tologies.
Most approaches to support the end-user with
the query formulation have focused on visual tech-
niques to hide the target query language, like SQL
for databases or SPARQL in the context of ontolo-
gies. (Catarci, 1997) presents a classification scheme
of 4 different graphical query construction categories
of visual query systems (VQS). The tool described in
this paper belongs to the category of diagram-based
systems, that tend to be the most popular. There have
been a few previous approaches to support a visual
query construction particularly for ontologies. Some
examples include SPARQLViz (Borsje and Embregts,
2006) and NITELIGHT (Russell and Smart, 2008).
SPARQLViz (Borsje and Embregts, 2006) aims to
help the user in query constructions for SPARQL. The
main difference to our approach is the interaction with
the user interface. SPARQLViz relies on a form-based
VQS with a wizard-like interface design, guiding the
user through different forms. In contrast, we present
a diagram-based system. There seem to be no empir-
ical studies on the different VQS categories, so it is
difficult to compare these different approaches.
NITELIGHT (Russell and Smart, 2008) is a VQS
that has much in common with the VQS presented
in this paper and influenced our research to some de-
gree. NITELIGHT supports the user with respect to
the specification of all SPARQL query result forms
(like SELECT, CONSTRUCT etc.). NITELIGHT
also offers the possibilities of result ordering, filter-
ing and limiting the results. It is a diagram-based
VQS that offers ontology browsing and drag-and-
drop functionality with a graph-based visualization.
Despite these similarities the following differences do
exist between NITELIGHT and our approach. First,
the visual query language (VQL) presented in this
paper is richer compared to the VQL supported by
NITELIGHT. The VQL presented in this paper of-
fers further possibilities on property restrictions like
range and cardinality restrictions (e.g. a person,
that only recieved invoices before 2010). Interviews
with our project partners from the construction in-
dustry revealed that they often search for information
where partial statements are already known (e.g. they
search invoices with a particular bathtube or a tender
preparation from a specific person). Therefore our
VQL supports query construction including individ-
ual statements.
3 OVERVIEW OF THE
ONTOBAU-ARCHITECTURE
There has been much literature about knowledge
management systems (KMS) within large enterprises
and little information available on KMS within SMEs
(Rasheed, 2005). According to (Rasheed, 2005)
SMEs have special requirements on KMS. Interviews
with our project partners led to the same result. The
managers in SMEs are in most cases the owners. The
result is, that the decision-making process is shorter
than in larger companies. They show a flat and
less complex structure, with fewer layers of manage-
ment (Wong and Aspinwall, 2004). Processes are of-
ten not as strongly structured as in larger enterprises
and knowledge is distributed at various points in the
company (file folder, product catalogs, databases)
(Schwinn, 2010). A smaller number of people within
a company is usually united by common beliefs and
values, resulting in shorter and often less strategic
ways of decision making (Rasheed, 2005). Because
fewer human and financial resources are available,
the introduction of a knowledge management system
should not cause ongoing costs. Especially in SMEs
there are no specialists for knowledge management
and additional staff costs are not manageable. The
goal of our project is to account for these special re-
quirements (Schwinn et al., 2011).
During his daily work, the employee can decide
whether certain resources should be transferred into
the knowledge base. Those resources (e.g. an e-mail
or a PDF document) are then passed to the OnToBau-
System using the interface of his personal agent or
plug-ins integrated in his office or e-mail software.
The purpose of this section is to give an overview of
the architecture of our approach. As shown in Figure
1, the OnToBau-system consists of four main parts
described in the following subsections.
3.1 Document Converters
As the name implies these pre-processing compo-
nents will prepare the information resources for in-
clusion in the knowledge base. They are converted to
a general representation language, so called OnToBau
Representation Language (ORL). The pre-processing
AN APPROACH TO A GRAPHICAL QUERY EDITOR - For Ontology-based Knowledge Management
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