An agile development methodology for knowledge-based systems including a Java framework for knowledge modeling and appropriate tool support [Elektronische Ressource] / Holger Knublauch

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• Informatik

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• Publié par: universitat_ulm
• Publié le: 01 janvier 2002
• Nombre de lectures: 17
• Langue: English
• Poids de l'ouvrage: 4 Mo

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Universit¨ at Ulm, Fakult¨ at fur¨ Informatik,
Abteilung Programmiermethodik und Compilerbau
An Agile Development Methodology for Knowledge-Based Systems
Including a Java Framework for Knowledge Modeling
and Appropriate Tool Support
Dissertation zur Erlangung des Doktorgrades Dr.rer.nat.
der Fakult¨ at fur¨ Informatik der Universit¨ at Ulm
Holger Knublauch
aus Berlin-Lichterfelde
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NAmtierender Dekan:
Prof. Dr. Gun¨ ther Palm
Gutachter:
Prof. Dr. Helmuth Partsch
Prof. Dr. Dr. Franz-Josef Radermacher
Datum der Promotionsprufung:¨ 16.10.2002Abstract
The goal of this thesis is to help make the development of knowledge-based systems more
efficient. For that purpose, it proposes a new, agile software and knowledge engineering
methodology, called XP.K (eXtreme Programming of Kno systems). This is based on the four values simplicity, community, feedback,andcourage,and
applies object-oriented Round-Trip Engineering to knowledge modeling.
The thesis is founded on the observation that for most knowledge-based systems, knowl-
edge must necessarily be modeled evolutionary, in a close collaboration between domain
experts and engineers. The author argues that existing “heavy-weight” development meth-
odologies from object-oriented Software Engineering and Knowledge Engineering are often
inefficient, because they make changes in knowledge models too expensive. Furthermore,
they provide little support for the transitions between knowledge, knowledge models, and
the remaining executable system. The starting point of XP.K is the hypothesis that “light-
weight” – or agile – development processes (such as Extreme Programming) are suitable
for knowledge modeling, because they are optimized for projects with frequently chang-
ing requirements and models and rely on a minimum of modeling artifacts with smooth
transitions between them.
XP.K applies the main principles of Extreme Programming to knowledge modeling.
The development process relies heavily on communication. Domain experts and knowledge
engineers collaborate in the definition of metamodels (ontologies) and knowledge is acquired
and tested in pairs. Ontologies are implemented in the object-oriented language which is
also used for the remaining modules of the system. These ontologies transparently expose
their structure and semantics both at run-time and build-time. At run-time, reflection
is used to analyze the ontology, so that generic knowledge acquisition tools and inference
engines can be (re-)used, reducing the cost of changing ontologies. At build-time, Round-
Trip Engineering is used to extract and edit the structure of the ontology in UML, so
that all information and documentation can remain centrally in the code. Prototypes are
produced rapidly and frequently exposed to automated tests and constraint checks.
XP.K is supported by a comprehensive, ready-to-use framework for implementing know-
ledge-based systems in Java. This includes an extension to the Java component model
JavaBeans for the representation of semantic constraints on object states and a generic
framework for knowledge acquisition tools. Evidence for the efficiency of XP.K is provided
by case studies from areas such as clinical decision-support and process modeling for multi-
agent systems.
iPrologue
The wind will not stop. Gusts of sand swirl before me, stinging my
face. But there is still too much to see and marvel at, the world
very much alive in the bright light and wind, exultant with the
fever of spring, the delight of morning. Strolling on, it seems to
me that the strangeness and wonder of existence are emphasized
here, in the desert, by the comparative sparsity of the flora and
fauna: life not crowded upon life as in other places but scattered
abroad in spareness and simplicity, with a generous gift of space
for each herb and bush and tree, each steam of grass, as that the
living organism stands out bold and brave and vivid against the
lifeless sand and barren rock. The extreme clarity of the desert
light is equaled by the extreme individuation of desert life forms.
Love flowers best in openness and freedom.
Edward Abbey, Desert Solitaire (1968)
iiiPreface
This thesis is based on the results of two related research projects carried out at the
Department of Business Processes and Telematics at the Research Institute for Applied
Knowledge Processing (FAW) at the University of Ulm, Germany. The project ANIS
(1998–2000, funded by the German governmental department of Education and Research
(BMBF)) aimed at developing a prototypical knowledge-based patient monitor for decision
support in anesthesia. The project AGIL (2000–2002, funded by the German Research
Foundation (DFG)) focused on the development of a multi-agent system to optimize the
clinical information flow. The author was principal researcher in both projects which
were conducted in cooperation with clinical experts andhers from the Institute for
Psychology and Ergonomics at the Technical University of Berlin.
Acknowledgements
I am indebted to Dr. Thomas Rose, head of the Department of Business Processes and
Telematics at the FAW, Ulm, and the project leader in AGIL and ANIS. Thomas has
made valuable critical comments and other contributions to this research and my scientific
work. Furthermore, he has generated an inspiring working environment in which I could
unfold freely. An essential part of this environment was my long running roomate Gerhard
Peter, who taught me many important lessons on scientific research and contributed to a
friendly and motivating workplace. Special thanks to Martin Sedlmayr, who has started as
a student research assistant in the ANIS project and later became a leading creative mind
at the FAW. Several discussions with Martin have lead to important advances in ANIS and
have laid the foundation of the KBeans approach. On this occasion, I would also like to
express my gratitude to my (former) colleagues in the department, in particular to Martin
F¨ unffinger, Dr. Christian Greiner and Dr. Christian Rupprecht.
I would like to acknowledge Holger K¨ oth, M.D., who was my partner in the AGIL
project in Berlin, and who has contributed to our knowledge modeling approach and the
resulting clinical multi-agent system. Many thanks also to the eager participants of the
Extreme Programming course at the University Ulm in fall, 2001, in which I had the
opportunity to evaluate parts of my Extreme Programming approach with a real-world
project.
Last but not least, I would like to express my gratitude to my supervisors Prof. Dr.
Helmuth Partsch and Prof. Dr. Dr. Franz-Josef Radermacher for supporting this thesis
and the Extreme Programming courses despite the rather unconventional topic. Prof.
vDr. Partsch has also laid the foundation of my scientific career by introducing me to the
basics of compiler construction, requirements engineering and software technology, and by
supervising my masters thesis in which I learned to appreciate the benefits of systematic
engineering approaches – in those cases when requirements are relatively stable...
viContents
1. Introduction 1
1.1. Problem ..................................... 1
1.2. Existing Approaches and their Limitations .................. 3
1.3. My Approach .................................. 4
1.4. Research Contributions............................. 6
1.5. Overview of this Thesis 7
2. Requirements of Development Methodologies for Knowledge-Based Systems 9
2.1. Development Methodologies .......................... 9
2.2. From Knowledge to Knowledge-Based Systems (and back) ......... 1
2.2.1. Knowledge-Based Systems ....................... 1
2.2.2. Roles and Artifacts in the Development Process ........... 12
2.2.3. Knowledge Modeling as a Collaborative Process 14
2.2.4. Kno Mo as an Evolutionary Process 15
2.3. Requirements of Development Methodologies................. 18
2.3.1. Requirements of the Modeling Process ................ 18
2.3.2.ts of the Mo Languages............... 19
2.3.3.ts of the Modeling Tools 20
2.4.Summary .................................... 21
3. Object-Oriented Software Engineering 23
3.1. Paradigms 23
3.1.1. The Four Principles of Object-Orientation .............. 23
3.1.2. Objects, Components, Patterns, Architectures, Frameworks..... 24
3.2. Processes 26
3.2.1. Systematic Engineering Processes ................... 26
3.2.2. Agile Processes ............................. 31
3.3. Languages 36
3.3.1. The Unified Modeling Language (UML) ............... 36
3.3.2. Metamodels and the Meta Object Facility (MOF) .......... 38
3.3.3. Object-Oriented Programming Languages .............. 39
3.4.Tools....................................... 39
3.4.1. Integrated Development Environments (IDEs) ............ 39
viiContents
3.4.2. CASE Tools and Round-Trip Engineering .............. 40
3.5.Summary .................................... 41
4. Knowledge Engineering 43
4.1. Paradigms 43
4.1.1.Ontologies................................ 4
4.1.2. Problem-Solving Methods ....................... 4<