AN APPROACH TO A GRAPHICAL QUERY EDITOR

For Ontology-based Knowledge Management

Markus Schwinn, Norbert Kuhn and Stefan Richter

Institut f¨ur Softwaresysteme in Wirtschaft, Umwelt und Verwaltung, Fachhochschule Trier

Campusallee, 55761 Birkenfeld, Germany

Keywords:

Knowledge management, Ontology, Visual query system, Ambient user support, Graphical query language.

Abstract:

The OnToBau research project aims to provide a way to classify, archive and effectively use business knowl-

edge with the assistance of an ontology-based knowledge archive for small and medium companies of the

construction industry. This archive is intended to pro-actively provide users with information which will assist

them in their daily business process handling. The targeted system consists of four main parts. In this paper

we mainly discuss a component for querying ontologies easily. We claim that such a component should allow

graphical queries in order to be suitable for non-experienced users in our application domain and we present

our approach to a visual query system and sketch how this can be embedded in the worker’s environment. This

graphical interface will allow a knowledge worker to actively search for information in an ontology and is a

ﬁrst step to a personal agent.

1 INTRODUCTION

The growing importance of computers in the 70s and

the associated opportunity to disseminate information

in digital form, is considered the turning point to to-

day’s information age. As with any other technolog-

ical revolution in history, the new information tech-

nologies spread within two decades all over the planet

(Castell, 2001). For the ﬁrst time, it was possible to

produce, to copy and to archive information in a sim-

ple way.

In the early 90s another technological milestone

was passed, that moved the world to a new era of

globalization (B¨oder, 2003). The Internet offered the

opportunity to access and produce information on an

increasing number of websites.

The largest ﬂow of information on the Internet is

produced by e-mails. A study of the Radicati Group

in 2009 stated that in the same year about 247 bil-

lion e-mails per day would be sent, and that this num-

ber would be doubled by the year 2013. However,

approximately 81% of those e-mails can be consid-

ered spam (Lyman and Varian, 2003)(Radicati and

Khmartseva, 2009). This information overload will

be a challenge for many companies in coming years.

This is also covered by the same study: it is estimated

that companies with 1,000 or more employees have to

invest 1.8 million dollars per year in the processing of

e-mails and spam (Radicati and Khmartseva, 2009).

However, not only the ﬂood of information from

the growing e-mail trafﬁc will challenge the com-

panies in the near future. A study of the Gartner

Inc. in 2002 amongst around 300 companies showed

that 96% suffer an information overload. To coun-

teract the information overload, companies will have

to spend 30 billion US-Dollars within the next years

(Goasduff, 2002). A similar investigation of the Ba-

sex Inc. in 2008 stated, that the consequences of in-

formation overload produce costs of about 900 bil-

lion US-Dollars a year for the economy of the United

States, caused by reduced productivity of the employ-

ees in knowledge intensive processes, which have to

spent about 25% of their daily work with the search

for information (Spira, 2008).

A signiﬁcant problem when searching for infor-

mation in digital data storages is, that the user has

to know the query language of the underlying stor-

age system (e.g. a database or an ontology). There-

fore, query systems simplify the query construction

by hiding the target query language. To offer the end-

user an easy and effective search for information in

the knowledge archive, we present our approach of a

diagram-based visual query editor.

The remainder of the paper is organized as fol-

lows. First we give an overview of related work in

visual query systems in Section 2. The overall sys-

436

Schwinn M., Kuhn N. and Richter S..

AN APPROACH TO A GRAPHICAL QUERY EDITOR - For Ontology-based Knowledge Management.

DOI: 10.5220/0003660904360441

In Proceedings of the International Conference on Knowledge Management and Information Sharing (RDBPM-2011), pages 436-441

ISBN: 978-989-8425-81-2

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

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

437

Figure 1: Overview of the OnToBau architecture.

also includes various ﬁlters (e.g. segmentation ﬁl-

ter, part-of-speech tagger, stop word ﬁlter), which are

used to simplify the subsequent processing. Interest-

ing e-mails can be converted into the ORL directly out

of the mail client.

3.2 Inference System

First, a given resource is classiﬁed into process re-

lated categories (e.g. an invoice). The domain on-

tologies contain the information which relevant data

should be extracted from these categories. To store

the information in the knowledge base we try to ﬁnd

relevant relationships between the new data and the

existing knowledge in the knowledge base (e.g. a link

between an invoice and a corresponding quotation).

3.3 Ontologies

The knowledge in the OnToBau-system is represented

by using ontologies. For this, we decided to use the

Web Ontology Language (OWL), which is a W3C

recommendation since February 2004, so we can ben-

eﬁt from existing libraries and tools. The TBox of our

ontology deﬁnes the terminology of the construction

domain and of the business documents, thus provid-

ing the OnToBau-system with the necessary knowl-

edge to decide which information to extract from the

resources. The extracted information is stored as in-

dividuals in the ABox of the ontology.

3.4 Personal Agent and User Interfaces

The personal agent of the employee performs two

main tasks. First, he should allow the employee to

access the knowledge base by making speciﬁc search

queries. For this purpose we implement a graphical

tool for query construction described in the following

chapter. Second, the agent should monitor the em-

ployees behavior and provide him pro-actively with

information to fulﬁll his task. The second task won’t

be part of this paper.

Our system is based on the Eclipse RCP Frame-

work which guarantees the ability to extend the sys-

tem easily with new functions, new converters and

even new graphical interface elements. Thus it will

be much easier to respond to user feedback and addi-

tional requirements.

4 APPROACH TO A GRAPHICAL

INTERFACE FOR COMPOSING

SEMANTIC QUERIES

According to (Spira, 2008) employees in knowledge

intensive processes have to spent about 25% of their

daily work with the search for information. Particu-

larly in small companies knowledgemanagement sys-

tems can hardly be found due to the reasons mentio-

nend in section 3. To use the corporate knowledge in

the OnToBau-system actively, the employees need an

KMIS 2011 - International Conference on Knowledge Management and Information Sharing

438

intuitive search facility. Since the knowledge is rep-

resented using OWL ontologies the queries for infor-

mation are restricted to a specialized query language

for ontologies.

We will describe our approach by using the exam-

ple of searching a document with certain parameters:

the knowledge worker is searching for quotations that

have a direct relation to invoices with a recipient who

is known as ’Markus Schwinn’. Additionally the

query should only consider invoices which were sent

from a company called ’Schottler’. The query string

for receiving this information from an ontology could

look like this:

Listing 1: Query example.

Select ? q u otat i o n Where {

? quo t a t i o n a ( qu o t a tion

and ( q u otatio n For

some ( inv o ice

and ( h a sInv o i c eRecipi e n t

value MarkusSchwinn ) and

( hasI nvoiceSender

value S c h o t t l e r ) ) ) ) .

}

This query might be obvious for a technically ex-

perienced person. But we can not expect the end-user

to be able to state queries like this because they lack

the required technical skills. Visual query systems try

to establish an intuitive way to construct such a query

string. Like (Russell and Smart, 2008) we decided

to use SPARQL as the target query language for our

approach. SPARQL is based on Turtle which isn’t in-

tended for OWL, therefore we use Terp, which com-

bines Turtle and Manchester syntax to provide more

conciseness. The user can select the entities, data

and object properties of the ontology from a treeview.

Contrary to NITELIGHT we decided to use individu-

als for query constructions too. This reﬂects the fact

that the knowledge worker often knows some details

exactly. In our example he knows the individual of

the invoice recipient. Possibly this will lead to a more

precise and unique query results.

The knowledge worker can drag-and-drop con-

cepts, roles or individuals onto the query editor frame

where they are represented as graphical nodes. If

they are dropped onto existing nodes they are auto-

matically connected with this node by an edge. The

type of the edge depends on the different node types.

When necessary the value of a node can be constraint

to specify cardinalities or ranges (e.g. when searching

for an invoice send by a speciﬁc company).

Figure 2 shows the same query from the exam-

ple above but much more intuitive for the end-user.

The queries are modelled as a directed graph. Every

node represents an ontology resource which is in fact

a concept, a role or an individual. These resources

were encapsulated in objects which have an additional

statement object to indicate cardinalities and values,

e.g. min=5, exactly xyz. If a statement object has no

value (e.g. and, not, or) it becomes an edge connect-

ing two nodes. Unlike (Fadhil and Haarslev, 2007),

our VQS basic operators are not modelled as spe-

cial nodes. The user can drop a concept onto another

which already has a connection to another one. By

clicking on the edge the user can deﬁne the connec-

tion.

In the background we use the Terp template inter-

preter to generate the SPARQL query from the graph

(Sirin et al., 2010). Terp is a syntax that combines

Turtle and Manchester syntaxes to provide maximum

legibility and conciseness when querying OWL with

SPARQL. It allows class, property and data range ex-

pressions to be used in SPARQL queries. The inter-

preter parses the nodes and edges in the graph and

builds recursively the query string. The use of recur-

sion offers the possibility for deeply nested queries.

Every class element in the graph allows the interpreter

to set a pair of penclosing brackets in the query. A

graph with two class nodes (e.g. person and dog)

would be translated like this:

Listing 2: Example for the conciseness of Terp syntax.

Select ? Person where {? Person a

( ns0 : Person and

( ns0 : h a s pet some ns0 : dog ) ) .

}

The use of Manchester syntax makes the query

easy to read. The edges of the graph reﬂect the cross-

linkage of the objects with the keywords and, or, not,

min, max, exactly, value, some, only as shown in the

listing above.

Graphs with nothing but the root element and

without an edge as well as the following keywords:

optional, ﬁlter, order by and owl:equivalentClass,

rdfs:subClassOf will not be implemented. For these

cases it will be necessary to think of additional mech-

anisms in the graphical user interface.

After constructing the graphical query, the results

are visualized to the knowledge worker. Choosing

a possible result will present the selected individ-

ual with all its connections in a well-arranged graph.

The implementation of this network is based on the

Protege-Plugin Ontograf, which is slightly enhanced

to improve the user experience (see ﬁgure 3). In our

example these are the quotations matching the query.

AN APPROACH TO A GRAPHICAL QUERY EDITOR - For Ontology-based Knowledge Management

439

Figure 2: Complete visual query graph.

Figure 3: View of the query result.

The result graph shows additional connections, like

the corresponding invoice and the company.

It is planned, but not yet implemented, to use this

icon-based approach also for the query editor. In

changing the diagram-based approach to a real graph-

ical approach we expect a better acceptance on the

user-side because of the improved usability.

A feature called live search is currently under de-

velopment. With every change on the graphical query

frame, like adding or removing nodes, changing the

type of the edges or changing values, the knowledge

worker will get an updated result list matching the

current query term. Thus he gets an instant feedback

if the query makes sense or not. This feature will cre-

ate a more ﬂuent the workﬂow.

5 CONCLUSIONS AND FUTURE

WORK

Because there are no speciﬁc projects supporting the

knowledge management process of companies in the

targeted domain, the presented architecture is our ap-

proach to provide small and medium companies with

an ontology-based knowledge management and infor-

mation retrieval system.

An integral part of the OnToBau system, which is

not yet implemented, will be the personal user agent.

The agent will monitor the users activities and pro-

vide him with the relevant information for the process

in real-time. To achieve this, it needs underlying be-

havior patterns and must try to anticipate the users

KMIS 2011 - International Conference on Knowledge Management and Information Sharing

440

intention.

The editor for the visual query construction is a

ﬁrst step to this personal agent. It can provide the user

with necessary knowledge, but he still has to search

pro-activily. The current approach is diagram-based,

but we are planning to develop an icon-based system

similar to the result view presented in ﬁgure 3. This

will improvethe user experience because there will be

no ”visual gap” between query construction and result

graph. We have not performed any user evaluations

with our project partners, but aim to undertake such

studies in the near future.

Another extension is the live search functionality.

When ﬁnished it will provide the knowledge worker

with a direct response on what he does in the query

editor. Currently the query editor and the presenta-

tion of the result is divided in two program parts. For

a ﬂuently workﬂow it will be neccessary to combine

these into one.

Our research project focuses on the needs of con-

struction industry but of course this approach could

be adapted in other domains dealing with ontologies.

ACKNOWLEDGEMENTS

The authors would like to thank Nima Pourjahedi for

the valuable assistance in implementing the visual

query editor. This work is supported by the German

Ministry of Research and Technology.

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