ONTOLOGY-BASED KNOWLEDGE MANAGEMENT

Graphical Query Editor for OWL Ontologies

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 from

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

in their daily business process handling. The system consists of four main parts. The document converters

prepare the different resources (EMails, Paperdocuments, PDFs etc.) that should be stored in the knowledge

archive for the enclosed inference system. The inference system is the core component and extracts the in-

formation from the preprocessed resources. Ontologies provide the necessary domain knowledge. In order

to exploit the available knowledge, a personal agent monitors the current activities of the user and tries to

infer the intention from his behaviors. At certain points it automatically offers the user helpful information.

Again ontologies are used to represent information about the business processes. In addition, the user has the

option to search for information in the archive through the graphical user interface. The importance of simple

query systems has already been identiﬁed in the area of database systems. This paper gives an overview of

the OnToBau research project presenting a ﬁrst approach to visual query for information in the knowledge

archive.

1 INTRODUCTION

The growing importance of computers in the 70s and

the associated opportunity to disseminate information

in digital form, is considered as 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 in 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 searching

for information (Spira, 2008).

Interviews with our project partners from the con-

struction industry provided similar results. In partic-

235

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

ONTOLOGY-BASED KNOWLEDGE MANAGEMENT - Graphical Query Editor for OWL Ontologies.

DOI: 10.5220/0003491202350240

In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), pages 235-240

ISBN: 978-989-8425-55-3

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

ular, the quotation preparation process has been de-

scribed as a knowledge-intensiveprocess. The reason

is that different resources are used to produce a quo-

tation. Basically, a product database is used. If the

product is not in the database, the employee searches

in product catalogs, previous invoices or web pages.

In addition, business documents are archived in paper

form in many small companies, as document manage-

ment systems are often not tailored to their needs. So

searching for information is often a very time con-

suming task (Schwinn, 2010).

In this paper we describe an architecture, which

should pro-actively provide these small companies

with relevant information stored in their knowledge

archive to assist them in their knowledge intensive

business processes. Because of the enormous variety

of different business processes our system focuses on

processes in the construction domain (e.g., the quo-

tation preparation process). The knowledge base is

built up from different resources a company has to

deal with (e.g. invoices, product catalogs etc.). To

offer the end-user an easy and effective search for in-

formation in the knowledge archive,we present an ap-

proach of a visual query editor.

The remainder of the paper is organized as fol-

lows. First we give an overview of related work in

pro-active knowledge management and visual query

editors in Section 2. The overallsystem architecture is

described in Section 3, with an overview of the main

components. In particular we discuss the visual query

editor in Section 4. Finally, we end up with the con-

clusion and outline some future work.

2 RELATED WORK

In this section we will give an overview of similar re-

search projects considered in process-oriented knowl-

edge management and we present previous work in

the area of graphical query construction, particularly

in the context of ontologies.

2.1 Process-oriented Knowledge

Management

There seems to be no other projects that speciﬁcally

consider a knowledge-based process support in con-

struction industry. However, the project DYONIPOS,

which has a strong similarity to the aims of the On-

ToBau research project as described above, tries to

optimize processes in public administration facilities

by providing pro-actively the available knowledge to

the employees (Makolm et al., 2007). DYONIPOS

has adopted a strict process-oriented approach that

moves the focus to the business processes (Tochter-

mann et al., 2006). This approach is appropriate in an

environment with highly structured processes, like in

public administration. Otherwise, in an environment

like the construction industry most of the processes

are semi-structured. In our approach we focus on

the documents and the knowledge contained within

them. Therefore, providing knowledge is more likely

to be tied to the user’s behavior than to rigid processes

(Schwinn, 2010).

2.2 Visual Query Systems

Most approaches to support the end-user with the

query formulation have focused on visual techniques

to hide the target query language like SQL for

databases or SPARQL in the context of ontologies.

(Catarci, 1997) present 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

support the user to query constructions for SPARQL.

The main difference to our approach is the interac-

tion 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 empirical studies on the different VQS categories,

so it is difﬁcult to compare this 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 of-

fers the possibilities of result ordering, ﬁltering and

limiting the results. NITELIGHT is a diagram-based

VQS that offers ontology browsing and drag-and-

drop functionality with a graph-based visualization.

Despite these similarties the following differences do

exist between NITELIGHT and our approach. First,

the VQL presented in this paper is richer compared

to the VQL supported by NITELIGHT. The VQL

presented in this paper offers further possibilities on

property restrictions like range and cardinality restric-

tions (e.g. a person with only invoices before 2010).

Interviews with our project partners from the con-

struction industry revealed that they often search for

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

236

information where partial statements are already

known (e.g. they search invoices with a particular

bathtube or a tender preparation from a speciﬁc per-

son). Therefore our VQL supports the query con-

struction including individual statements. The ontol-

ogy browser in NITELIGHT consists of a series of

columns that display the classes and subclasses of the

ontology (Russell and Smart, 2008). Whereas our on-

tology browser provides access to the individuals too.

The current approach is diagram-based, but we are

planning to develop a icon-based system similar to the

result view presented in ﬁgure 3. In addition, we use

a live result view to give the user a direct feedback on

his query construction, so he can remark early when

his query goes in the wrong direction.

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. Inter-

views with our project partners led to the same re-

sult. The managers in SMEs are in most cases the

owners. The result is, that the decision-making pro-

cess is shorter than in larger companies. They show

a ﬂat and less complex structure, with fewer layers

of management (Wong and Aspinwall, 2004). Pro-

cesses are often 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 com-

mon beliefs and values, resulting in shorter and often

less strategic ways for making decisions (Rasheed,

2005). Because of fewer human and ﬁnancial re-

sources, the introduction of a knowledge management

system should not cause ongoing costs. Especially in

SMEs there are no specialists for knowledge manage-

ment and additional staff costs are not manageable.

The goal of our project is to account for these special

requirements. In summary, we need to consider the

following requirements:

• the effort to record analogue documents has to be

minimal and should not disrupt the daily work

• intuitive usability

• there should be no running costs, in particular no

additional staff cost

• knowledge should be extracted from different re-

sources

• access to the knowledge archive should be possi-

ble at any time

• the extracted knowledge should be linked in an

effective manner to the usual business processes

During the 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. To

transfer analogue documents to the knowledge base,

we are planning to install special document cameras

directly at the workplace of every employee. In this

way we prevent the scanning process from disturb-

ing the workﬂows in the company and ensure that

every employee can easily add new resources to the

knowledge base. 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 subsec-

tions.

3.1 Document Converters

These pre-processing components will prepare the re-

sources for inclusion in the knowledge base. There-

fore the resources are converted to a general represen-

tation language, so called OnToBau Representation

Language (ORL). The pre-processing also includes

various ﬁlters (e.g. segmentation ﬁlter, part-of-speech

tagger, stop word ﬁlter), which are used to simplify

the subsequent processing. The relevant information

is extracted in the inference system component. Inter-

esting e-mails can be convertet into the ORL directly

out of the mail client like Thundebird or Mircosoft

Outlook.

3.2 Inference System

First, a given resource is classiﬁed into process related

categories (e.g. an invoice). The domain ontologies

contain information which relevant data have to be

extracted from this categorie. Once the information

has been extracted, it is stored in the knowledge base.

For this purpose, 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). If the resource is an

e-mail with an attachment, it will be removed from

the email and both are integrated into the knowledge

archive.

ONTOLOGY-BASED KNOWLEDGE MANAGEMENT - Graphical Query Editor for OWL Ontologies

237

Figure 1: Overview of the OnToBau architecture.

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 an W3C

recommendation since February 2004, so we can ben-

eﬁt from existing libraries and tools. The T-Box of

our ontology deﬁnes the most relevant concepts and

relations of the construction domain and in general

of business documents, thus providing the OnToBau-

system with the necessary knowledge to decide which

information to extract from the resources (e.g. recip-

ient details, product information etc.). The extracted

information is stored as individuals in 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 (e.g. show me all in-voices of Mr. Brown

with an invoice date later than December 2009). For

this purpose we implement a graphical tool for query

construction described in the following chapter. Sec-

ond, the agent should monitor the employees behavior

and provide him pro-actively with information to ful-

ﬁll his task.

4 GRAPHICAL INTERFACE

FOR SEMANTIC QUERY

CONSTRUCTION

According to (Spira, 2008) employees in knowledge

intensive processes have to spent about 25% of their

daily work with searching for information. Particu-

larly in small companies knowledgemanagement sys-

tems are 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

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.

Using the example of searching an invoice with

some parameters we will describe our approach: the

knowledge worker is searching for an invoice that was

sent to a recipient who is known as ’Markus Schwinn’

and the invoice date is before january, 1st 2011. He

additionally wants only the invoices that contain or-

dered products of a bathtube or a wash-bowl. In

pseudo code the query string for receiving this infor-

mation from an ontology could look like this:

SELECT ? invoic e WHERE {

? in voi ce a ( in voi ce and

( h asI nvo i ce Rec ipi ent value

MarkusSchwinn ) and

invDate some ? date and

( hasProd uct some

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

238

( bathtub or wash−bowl ) ) ) .

f i l t e r ( dateTime (? date ) < dateTime

( ”2011−01−01T00 :00:00Z” ) ) .

}

This seems very familiar to a technically experi-

enced person. But you can not expect that the end-

user is familiar with it too. Visual query systems try

to support an easier way for constructing such a query

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

use SPARQL as the target query language for our ap-

proach. The user can select the entities, data and ob-

ject properties of the ontology from a treeview. Con-

trary to NITELIGHT we decided to use individuals

for query constructions too. This reﬂects the fact that

the knowledge worker often knows some details ex-

actly. In our example he knows the individual of the

invoice recipient. Possibly this will lead to a more

precise and unique query result.

The knowledge worker could drag-and-drop the

objects onto the query editor frame where they are

presented as graphical nodes. They can be dropped

onto the coresponding nodes and are connected au-

tomatically with this node by an edge. The type of

the edges depends on the node types and reﬂects the

query structure of SPARQL. When needed you can

edit the value of a node to specify cardinalities or

ranges (e.g. when you are searching for a person

with at least three invoices). Figure 2 shows the same

query above but much more intuitive for the knowl-

edge worker.

Figure 2: Example of graphical query structure.

After constructing the graphical query with all the

information he knows, ideally, the knowledge worker

get back one result or a list with all results match-

ing the query. Choosing an item will present the se-

lected individual with all its connections in a well-

arranged graph. The implementation of this resulting

graph is based on the Protege-Plugin Ontograf, which

is slightly enhanced to improve the user experience

(see ﬁgure 3). In our example this is the requested

invoice with its connection to the individual ’Markus

Schwinn’. Also two products from this invoice are

noticable, a bathtube and a wash-bowl,represented by

an icon and their name. The result graph shows addi-

tional connections, like the mail which contained the

invoice, the corresponding proposal and the company

sending it.

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 undertake a better acceptance on the

user-side because of the re-improved usability.

Figure 3: View of the query result.

A feature called live search is currently under de-

velopment. With every change on the graphical query

frame, like adding or removing nodes or changing the

type of the edges, the knowledge worker should get an

updated list with results matching the current query

term. Thus he get an instant reply if the query makes

sense or not. With this feature the workﬂow will be

more ﬂuently.

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 way

to provide small and medium companies with an

ontology-based knowledge management system. To

reach this goal we have developed the ORL. This rep-

resentation language is the link between different re-

sources used within companies and the information

extraction. With speciﬁc converters the system is able

to transform the resources to a unitary format which

is needed for extraction. Currently we are working

mainly on the extraction step, to enable the system to

ONTOLOGY-BASED KNOWLEDGE MANAGEMENT - Graphical Query Editor for OWL Ontologies

239

build up a knowledge base for a company. This base

will be represented by using ontologies.

An integral part of the OnToBau system which is

not yet im-plemented will be the personal user agent.

The agent will moni-tor the users activities and pro-

vide him with the relevant infor-mation for the pro-

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

behavior patterns and must try to anticipate the users

intention.

The editor for the visual query construction is still

under development. Currently it has a diagram-based

approach, but it will change to an icon-based visual

style. This might improve the user experience with

our tool because there will be no ”visual gap” be-

tween 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 function-

ality. 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 pre-

sentation of the result is divided in two program parts.

For a ﬂuently workﬂow it will be neccessary to com-

bine this in one part.

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

This work is supported by the German Ministry of

Research and Technology under grant FKZ 1731X09.

REFERENCES

B¨oder, J. (2003). Das Internet und seine Folgen - Was

Luthers Reformation und unsere Zukunft gemeinsam

haben. http://www.intelligenzia.de/Texte/Internet.pdf.

17. August 2009.

Borsje, J. and Embregts, H. (2006). Graphical Query Com-

position and Natural Language Processing in an RDF

Visualization Interface. Bachelor, Erasmus University

Rotterdam.

Castell, M. (2001). Das Informationszeitalter. Wirtschaft,

Gesellschaft, Kultur, volume 1. Leske + Budrich Ver-

lag.

Catarci, T. (1997). Visual Query Systems for Databases: A

Survey. Journal of Visual Languages & Computing,

8(2):215–260.

Goasduff, L. (2002). Gartner Says 90 Percent of

Businesses Suffer from Information Overload.

http://www.gartner.com/5 about/press releases/2002 -

05/pr20020507c.jsp. 24. August 2009.

Lyman and Varian (2003). How Much Information?

http://www2.sims.berkeley.edu/research/projects/how-

much-info-2003/printable report.pdf. 19. August

2009.

Makolm, J., Weiß, S., and Reisinger, D. (2007). Proactive

knowledge management: the DYONIPOS research

and use-case project. In Janowski, T. and Pardo, T. A.,

editors, Proceedings of the 1st international confer-

ence on Theory and practice of electronic governance

(ICEGOV ’07), pages 85–88, New York, NY, USA.

ACM.

Radicati, S. and Khmartseva, M. (2009). Email Statistics

Report, 2009-2013. http://www.radicati.com/wp/wp-

content/uploads/2009/05/email-stats-report-exec-

summary.pdf. 19. August 2009.

Rasheed, N. (2005). The Impact Of Knowledge Manage-

ment On Smes. Technical report, Knowledgeboard.

Russell, A. and Smart, P. R. (2008). NITELIGHT: A Graph-

ical Editor for SPARQL Queries. In International Se-

mantic Web Conference (Posters & Demos).

Schwinn, M. (2010). Automatic Information Extraction

from Documents for Setting Up a Knowledge Archive

as Basis for an Automated Tender Preparation. Mas-

ters thesis, University of Applied Sciences, Trier.

Spira, J. B. (2008). Information Overload: Now $ 900

Billion - What is Your Organizations Exposure?

http://www.basexblog.com/2008/12/19/information-

overload-now-900-billion-what-is-your-

organizations. 10. December 2010.

Tochtermann, K., Reisinger, D., Granitzer, M., and Lind-

staedt, S. (2006). Integrating Ad Hoc Processes and

Standard Processes in Public Administrations. In Pro-

ceedings of the OCG eGovernment Conference, Linz

(Austria),.

Wong, K. and Aspinwall, E. (2004). Characterizing knowl-

edge management in the small business environment.

Journal of Knowledge Management, 8:44–61.

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

240