Universität Würzburg
Continuous Knowledge Engineering
with Semantic Wikis
Dr. Joachim Baumeister
Kumulative Habilitationsschrift
zur Erlangung der Lehrbefähigung für Informatik
Fachmentorat:
Prof. Dr. Frank Puppe, Universität Würzburg Prof. Dr. Klaus-Dieter Althoff, Universität Hildesheim
Prof. Dr. Dietmar Seipel, Universität Würzburg
Continuous Knowledge Engineering
with Semantic Wikis
(Habilitation Summary)
Joachim Baumeister (University of Würzburg)
This work summarizes the research body of the following publications:
1. Joachim Baumeister. Advanced measures for empirical testing. InFLAIRS’09: Proceed-ings of the 22th International Florida Artificial Intelligence Research Society Conference, pages 378–383. AAAI Press, 2009 . . . 27 2. Joachim Baumeister and Grzegorz J. Nalepa. Verification of distributed knowledge in semantic knowledge wikis. InFLAIRS’09: Proceedings of the 22th International Florida Artificial Intelligence Research Society Conference, pages 384–389. AAAI Press, 2009 . . . 33 3. Joachim Baumeister and Frank Puppe. Web-based knowledge engineering with knowl-edge wikis. InProceedings of Symbiotic Relationships between Semantic Web and Knowl-edge Engineering (AAAI 2008 Spring Symposium), 2008 . . . 39 4. Joachim Baumeister and Dietmar Seipel. Verification and refactoring of ontologies with rules. In EKAW’06: Proceedings of the 15th International Conference on Knowledge Engineering and Knowledge Management, pages 82–95, Berlin, 2006. Springer . . . 51 5. Joachim Baumeister and Dietmar Seipel. Anomalies in ontologies with rules. Web Se-mantics: Science, Services and Agents on the World Wide Web, 8(1):55–68, 2010 . . . . 67 6. Joachim Baumeister, Jürgen Bregenzer, and Frank Puppe. Gray box robustness testing of rule systems. InKI’06: Proceedings of the 29th Annual German Conference on Artificial Intelligence, LNAI 4314, pages 346–360. Springer, 2006 . . . .81 7. Joachim Baumeister, Thomas Kleemann, and Dietmar Seipel. Towards the verification of ontologies with rules. In FLAIRS’07: Proceedings of the 20th International Florida Artificial Intelligence Research Society Conference, pages 524–529, 2007 . . . 97 8. Joachim Baumeister, Martina Menge, and Frank Puppe. Visualization techniques for the evaluation of knowledge systems. InFLAIRS’08: Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference, pages 329–334. AAAI Press, 2008 . . . 103
9. Joachim Baumeister, Jochen Reutelshoefer, and Frank Puppe. Continuous knowledge engineering with semantic wikis. InCMS’09: Proceedings of 7th Conference on Computer Methods and Systems (Knowledge Engineering and Intelligent Systems), pages 163–168. Oprogramowanie Naukowo-Techniczne, 2009 . . . 109 10. Joachim Baumeister, Jochen Reutelshoefer, and Frank Puppe. KnowWE: A semantic wiki for knowledge engineering. Applied Intelligence, to appear, 2010 . . . 115 11. Jochen Reutelshoefer, Florian Lemmerich, Fabian Haupt, and Joachim Baumeister. An extensible semantic wiki architecture. InSemWiki’09: Fourth Workshop on Semantic Wikis – The Semantic Wiki Web (CEUR proceedings 464), 2009b . . . 145 12. Sebastian Schaffert, François Bry, Joachim Baumeister, and Malte Kiesel. Semantic wikis. IEEE Software, 25(4):8–11, 2008 . . . 161
1 Introduction
With the commercial success of knowledge-based technologies in the nineties, we see an in-creasing relevance in industry, nowadays. Intelligent systems are integrated and fully applied in industry: For example, systems cover service-support tasks in telecommunication domains as it is implemented by companies like IISY AG1. In the technical domain, Bosch GmbH2
pro-vides knowledge-based systems to support the diagnosis task in the automotive industry (Nghia and Puppe, 2009), whereas TIGER is an intelligent system for the fault diagnosis of gas tur-bines (Milne and Nicol, 2000). Traditionally, intelligent systens are well-known in the medical domain, see for example the system SmartCare for the automated ventilation of patients (Mers-mann and Dojat, 2004), SonoConsult for the adaptive documentation and consultation of sono-graphical examinations (Hüttig et al., 2004), or GIDEON for the treatment of febrile travel-ers (Kimura et al., 2005).
The prejudicial cost-benefit ratio of knowledge-based systems, however, hinders its wide-spread success. With thecost-benefit ratioof a knowledge-based system we refer to the relation between the costs of developing and maintaining a system and the resulting benefit of its applica-tion when put into daily use. Unfortunately, the costs are often the critical element, since the for-malization of domain knowledge is a very time-consuming and specialized task. To accomplish this task, development processes and tools are proposed in literature, see for instance (Angele et al., 1998; Schreiber et al., 2001), but these are rarely adapted to the specific needs and require-ments of the particular application domain. Moreover, todays knowledge engineering projects often face the challenge that knowledge is present at different levels of formalization, but pro-cesses and tools are mostly not sufficiently prepared to meet this challenge. Knowledge appears in different representations ranging from technical documents, construction plans, sheets, and experiences of human experts, but also in the explicit form of rules and models. In summary, the
1http://www.iisy.com 2http://www.bosch.com
knowledge acquisition bottleneck still exists today. In various knowledge engineering projects we frequently experienced the following dilemmas:
1. The Single/Multiple Experts Dilemma.
The motivation and sophistication ofsingle domain specialistsis often the driving force of successful knowledge acquisition and evolution. Although, high-quality experts can guar-antee the construction of high-quality knowledge bases, these persons are often short in time and motivational endurance. Distributing the workload over a number of specialists would decrease this problem, but at the same time would increase the risk of reducing the overall quality of the formalized knowledge.
In addition, the collaboration of a group of specialists is hardly supported by (academic and industrial) authoring tools. Here, the dilemma exists of favoring a distributed over a monolithic development process—involving multiple specialists instead of a single spe-cialist.
2. The Flexibility/Productivity Dilemma.
Current process models and state–of–the–art tools are often tailored to capture the domain knowledge in a specific knowledge representation and knowledge granularity. The mental model of domain specialists, however, often differs from the given representation. Tools often lack the flexibility to map the mental models to the acquisition interfaces of the tools and thus complicate the development process even more. Besides the mental model of the specialists, knowledge additionally is often already present in various forms, such as textual and tabular data, but also as explicit rules.
On the one hand, mapping the particular mental model of the specialists to the provided knowledge representation and interfaces, respectively, often turned out to be difficult and time-consuming. On the other hand, a tool, that offers the maximal flexibility regarding the user interfaces and the provided knowledge representations, typically would increase the complexity of its use and therefore decreases the productivity of the developers; this principle was described as theFlexibility-Usability Tradeoff (Lidwell et al., 2003, p. 86). In consequence, we face the dilemma of demanding a tool with maximal flexibility vs. a tool with maximal productivity.
3. Sophisticated Inference vs. Pragmatic Knowledge Formalization.
At the beginning of a project it is often very difficult to determine the appropriate level of formalization. Here, we face the dilemma of using expressive knowledge for sophisti-cated inferences vs. a broad but pragmatic knowledge formalization. When modeling the domain knowledge on a very precise level using an expressive knowledge representation, we are able to perform sophisticated inferences. As a drawback, knowledge elicitation at such a detailed level is a time-consuming and complex task; sometimes, it is actually not possible to formalize parts of the domain knowledge that precisely. In contrast, the pragmatic knowledge formalization processpropagates to start with a shallow but broad knowledge acquisition at the beginning of the project. Later, parts of the knowledge are refined when necessary. For example, it is sometimes sufficient that parts of the knowl-edge base are simply specified as natural text and remain at this formalization level. Other
parts of the knowledge base are required for automated inferences, so a specialization of the knowledge becomes necessary.
In the presented work, we contribute novel concepts and tools in order to lighten these dilem-mas. Certainly, we are not aiming for solving the dilemmas as a whole, but we claim to help simplifying the corresponding challenges.
In the following, we introduce the knowledge formalization continuum (Baumeister et al., 2009) as a mental metaphor, that intends to help the domain specialist in understanding the concept of knowledge and its gradual formalization. In summary, the knowledge formalization continuum proposes a flexible interpretation of knowledge, where the domain knowledge is not restricted to a specific formalization representation, but can range from multimedia and text to expressive logic formulae. With the introduction of the knowledge formalization continuum we alleviate Dilemma 2 and Dilemma 3. For the practical application, we see that current state– of–the–art tools are not capable to work on the knowledge formalization continuum. We con-sequently propose and demonstrate the implementation of a new generation ofknowledge engi-neering tools, that are derived from Semantic Wikis (Schaffert et al., 2008). This new generation of tools offers a flexible formalization process and provides the means to capture and reason with various sources of knowledge. Additionally, it scales with the number of users and domain spe-cialists, respectively. We introduce the extensible Semantic Wiki KnowWE (Baumeister et al., 2010) by describing its architecture and knowledge engineering approach. The availability of an appropriate and flexible tool such as KnowWE relieves Dilemma 1 and Dilemma 2.
The introduction of a new conceptual model and a corresponding tool, however, does not tackle the question about thequalityof the formalized knowledge. Having the conceptual model of multimodal knowledge sources, traditional evaluation techniques cannot be fully applied any-more. We discuss advanced evaluation methods that are applicable and practically relevant in the context of the presented approach. Evaluation is often defined as the upper class of the validationandverificationtasks (Ayel and Laurent, 1991). More recently, the additional task assessmentis added as a third subdiscipline. Regarding evaluation, we distinguish the following sub-tasks:
1. Validationas a black-box test investigates the reasonable behavior of the system, i.e., by checking whether the system yields the expected outputs for given inputs.
2. Verificationas a white-box test analyzes the knowledge base in order to detect anomalies or further deficiencies that may affect the correct and maintainable implementation of the system. Here, the system is often tested against a previously defined specification. 3. Assessmentas a soft category of tests mostly considering the utility and the effectivity of
the built system.
Advanced knowledge engineering tools—such as the Semantic Wiki KnowWE—represent the fundamental concepts of the domain knowledge in an ontology. We motivated above, that advanced processes and tools should be capable to combine different knowledge formalizations. As a popular example, the Semantic Web initiative currently proposes the extension of ontolo-gies by a rule-based layer, e.g., see (Horrocks et al., 2005). First, we discuss implications of the
verificationprocess when ontologies are mixed with rules. The implications of traditional ver-ification methods, when applied to Semantic Wikis, were discussed in (Baumeister and Seipel, 2010). Second, the validation of knowledge bases is an important aspect of knowledge engi-neering. We introduce the notion of a sequential test case as an extension of classic test cases, that proved to be appropriate for the validation of industrial knowledge bases. Consequently, we discuss corresponding adaptations of the precision and recall measures (Baumeister, 2009), that are applied to the results of empirical testingruns. Besides the plain validation of knowledge bases by empirical testing methods, the robustness of the built system is an interesting feature. We introduce the concept of grey-box robustness testing (Baumeister et al., 2006), that is an extension of the previously introduced robustness testing by degradation studies (Groot et al., 2003, 2000). Also, adapted visualization methods (Baumeister et al., 2008) showed a significant improvement during the evaluation and manual inspection of knowledge bases.
Each of the following sections summarizes and highlights the important aspects of the specific topics covered in this thesis.
2 The Knowledge Formalization Continuum
As we motivated in the introduction, traditional knowledge engineering approaches require the formalization of knowledge at an early stage and—more importantly—at a fixed level of formal-ization. Experiences in many real-world applications, however, showed that both requirements are often not necessary but rather hinder the successful development of the project. We con-tribute the concept of theknowledge formalization continuum in order to give domain special-ists a flexible mental model of the knowledge to be used in the application project. This mental model frees all involved parties to commit to a particular kind of knowledge formalization at an early stage, but offers a versatile understanding of the formalization process.
Acontinuum can be seen as “a nonspatial whole in which no part or portion is distinct or distinguishable from adjacent parts”; alternatively a continuum can be understood as “anything that goes through a gradual transition from one condition, to a different condition, without any abrupt changes”3.
We use these definitions of a continuum to explain the idea of theknowledge formalization continuum, where gradual transitions on formalization degrees of the same knowledge are possi-ble, but where the knowledge to be modelled experiences no abrupt changes or “discontinuities”. It is important to notice that the knowledge formalization continuum is neither a physical model nor a methodology for developing knowledge bases. Rather, it should be seen as a metaphor of the knowledge development process in order to help the domain specialists to see even raw data, such as text and multimedia, as first-class knowledge. In the extreme cases, domain knowledge is provided as very informal data (images, text), or is represented by formal representations such as decision trees or functional models. See Figure 1 for a (non-exhaustive) depiction of the different knowledge representations possible in the knowledge formalization continuum. Each formalization alternative has its own advantages and drawbacks. For example, textual knowl-edge can be easily elicited and often is already available in the domain. No prior knowlknowl-edge with
Knowledge Formalization Continuum Text Tags Semantic annotations Fault models Functional models Decision trees Cases Segmented text Tabular data Semantically equivalent transitions Images Mindmaps / Flow charts Logic Rules
Figure 1: Possible knowledge transitions within the knowledge formalization continuum. respect to tools or knowledge representation is necessary. However, automated reasoning using textual knowledge is not possible with current state–of–the–art methods: The knowledge can be retrieved only by using string-based matching methods but not by semantic queries. Logic rules or models are well-suited for automated reasoning, and queries can be processed on the se-mantic level. In contrast to textual knowledge, the acquisition of rules and models is a complex and time-consuming task. The transition between two representations on the knowledge for-malization continuum is often possible by using established methods, such as natural language processing, text mining, visualization methods, refactorings, and manual elicitation methods.
The core ideas of the knowledge formalization continuum and its application within a Seman-tic Wiki are described in (Baumeister et al., 2009).
3 The eXtensible Semantic Wiki KnowWE
In recent years the advent and success of Web 2.0 application, such as wikis, blogs, and social networks, has changed the way people are using the Internet. When compared to traditional web sites, Web 2.0 applications explicitly involve the users as primary contributors to the system. Thus, the value of the particular systems usually grows with the increasing contribution of the users. Not only private life but also daily business is influenced by the success of Web 2.0 approaches. One prominent example is the wide-spread use of wikis as flexible knowledge management tools, both in personal life and business environments.
Standard wiki systems, however, show limitations when the included content is intended to be used as explicit knowledge. For the retrieval of the content, only a simple full-text search is possible, and knowledge connected across different articles cannot be aggregated in a unified manner. This issue motivated the development of Semantic Wikis that extend standard wikis by an explicit ontological layer defined by semantic annotions of the wiki content. Thanks to
semantic annotations, knowledge reuse is improved by semantic search and semantic naviga-tion (Schaffert et al., 2008). At the same time, Semantic Wikis successfully serve as ontology development tools, that provide a simple, web-based interface to build semantic applications. Typically, the expressiveness of an ontology developed by a Semantic Wiki corresponds to a subset of the web ontology language OWL, which is sufficient for many applications.
The development of (diagnostic) knowledge systems, however, commonly requires the inte-gration of strong problem-solving knowledge, for example (production) rules, decision trees, and fault models. We consequently developed the system KnowWE as an extensible Semantic Wiki, that is able to capture and share strong problem-solving knowledge of various types. KnowWE is the first implementation of a Semantic Wiki, that explicitly integrates strong problem-solving knowledge into the wiki context. The extension by strong problem-solving knowledge motivates the following conceptual changes to the Semantic Wiki architecture:
(a) The representation of problem-solving knowledge and its alignment with the ontology layer. (b) The design of a knowledge base that is distributed over the wiki.
(c) The appropriate interoperability of explicit knowledge with the surrounding tacit knowledge such as text and multimedia.
(d) The tailored interfaces to capture and use the knowledge.
Atask ontologyinterweaves standard OWL ontologies with the problem-solving knowledge. That way, the knowledge of the wiki can be transparently accessed by SPARQL queries. Ad-ditionally, more expressive knowledge—rules, decision trees, and fault models—is used for automated reasoning.
For larger application projects, we introduce the concept ofwiki mastersas a unified knowl-edge engineering metaphor that helps building (large) knowlknowl-edge bases within a wiki. Here, the knowledge is distributed over the wiki system inservantarticles, but is (virtually) joined by a small number of master articles. Alternative variants of masters can be defined declaratively by the wiki users.
In the last years, we gained experience in developing knowledge bases using the sketched Semantic Wiki approach. We discuss the most relevant reflections in the following:
1. Flexible organization of the knowledge:Semantic Wikis free the users from a predefined
organization of the content. As the only requirement, a Semantic Wiki requires thearticle as a logical organization unit, i.e., content is structured by distributing it over wiki articles. In consequence, a project is in no way restricted in terms of dividing the knowledge into logical units, but rather permits any structure as long as it fits into the partitioning of separate articles.
In a medical project, for example, the application knowledge was structured according to their cardinal symptoms, for exampleneurological problems,chest pain, etc. This parti-tioning seemed reasonable with respect to the applied knowledge representation, because for each cardinal symptom one or more heuristic decision trees were defined subsequently. In other projects, the wiki implemented a more solution-oriented organization, i.e., for
each solution a separate article was defined, containing the tacit knowledge as well as the corresponding problem-solving knowledge for the particular solution.
2. Continuous representation of multimodal knowledge:When compared to classic
knowl-edge engineering tools, a Semantic Wiki offers a flexible integration of various types of knowledge. That way, we are able to simply combine tacit knowledge—such as text and multimedia—with more explicit forms of knowledge—such as rules and fault models. Therefore, a Semantic Wiki provides an appropriate technical basis for engineering on the knowledge formalization continuum. We experienced the combined representation of tacit and explicit knowledge to be very beneficial, since tacit knowledge can serve within the project in various ways: (a) as startup documentsat the beginning of a project to informally collect knowledge about the domain, (b) asdocumentation of the knowledge engineering process including the comments on the design decisions taken and notes for future enhancements, (c) as explanationfor the formal counter-part defined in problem-solving markup, and (d) as pursuing information for concepts represented by the article. For example, in a medical project the knowledge was originally formalized in a graph-based notion by MS-Visio documents. These documents were attached as underlying tacit knowledge explaining the decision tree representation, that was used during the for-malization phase. Due to the version control of the wiki, older versions of the attached documents as well as of the wiki articles can be reviewed and compared to the current state at any time. Moreover, the articles incorporated an implicit documentation of the development process.
3. Simple administration and rights management: In the past, development tools
usu-ally required the installation of proprietary software on the client-side. Web-based soft-ware, such as wikis, only require the availability of a standard web-browser and an inter-net/intranet connection on the client side. As an additional benefit of web-based software, knowledge engineers are not limited to the particular computer that has the software in-stalled, but are able to start and continue the development process on any computer with a browser and an (internet) connection to the wiki server.
Due to the built-in rights management, wikis allow for a fine-grained setting of the read/write access of the articles. In this manner, some parts of the wiki can be closed for public ac-cess, for example, when the development process is not finished for these parts or for articles, that document administrative content. Finally, any content stored in the wiki— knowledge as well as data—is held under version control, and changes and revisions can be safely performed.
The conceptual model of Semantic Wikis combining ontologies and problem-solving knowl-edge is described in (Baumeister et al., 2010). Appropriate markups for the effective and ex-tensible knowledge acquisition are introduced in (Baumeister and Puppe, 2008; Reutelshoefer et al., 2009b).
4 Quality in Intelligent Systems
Application-driven domain ontologies build the knowledge backbone of advanced intelligent systems. In the previous section, we briefly introduced the Semantic Wiki KnowWE as an example of an advanced knowledge management system. Here, the fundamental concepts are also linked by an underlying ontology. Additionally, we see that ontologies are combined with further types of knowledge. The most prominent example is the combination of ontologies with logic-based rules, as proposed by recent developments of the Semantic Web Stack.
When putting developed systems into practice, it is essential to evaluate theirquality. Knowl-edge engineering research provides verification and validation methods as objective measures for the quality evaluation. We investigated advanced verification and validation methods that take heterogenous knowledge and practical requirements into account. Byverification, we de-note the detection of anomalies that disagree with a (logical) specification.
4.1 Verification of Ontologies with Rules
In general, when OWL ontologies and rules are combined, the detection of all anomalies is an undecidable task. Therefore, we investigate methods that rely on a pattern-based approach, thus trying to find occurrences of known anomalies in the knowledge. By verification, we under-stand the syntactic analysis of ontologies at the symbolic level for detecting anomalies. The work is based on prior research on the evaluation of ontologies introduced by (Gómez-Pérez, 2001) and research on the verification of rules, for instance described by (Preece et al., 1992). However, the combination of taxonomic and other ontological knowledge with a rule exten-sion induces new evaluation issues, that can cause redundant or even inconsistent behavior. For example, an obvious redundancy may be due to the coexistence of the taxonomic relation A sub-class-ofB and the ruleA →B. We contribute to this work by extending classic measures by novel anomalies that result from the combination of rule-based and ontological knowledge. It is important to notice that there exists no final enumeration of anomalies, but new anomalies arise due to application-specific requirements. Therefore, we introduce the declarative spec-ification of anomalies by the new language Datalog*, that allows for flexibly including new and application-relevant anomalies. In detail, we investigate the implications and problems that emerge from rule definitions in combination with some of the following ontological descriptions: (a) class relations likesubclass,complement,disjointnessand (b) basic property characteristics liketransitivity,symmetry,rangesanddomains, andcardinality restrictions.
The detection of anomalies in ontologies with rules is discussed in (Baumeister and Seipel, 2010) and (Baumeister and Seipel, 2006), whereas (Baumeister et al., 2007) also considers rea-sonable refactoring methods to eliminate found anomalies.
4.2 Advanced Validation and Visualization
Critical application domains require the elaborate and thoughtful validation of the knowledge bases before deployment. Empirical testing denotes the most popular validation technique, where predefined test cases are used to simulate and review the correct behavior of the
sys-tem. We motivate that the classic notions of a test case and corresponding measures are not sufficient in many (industrial) application scenarios. Today, typical knowledge systems are
• interactive, i.e., providing an adaptive interview with the user to allow for effective problem– solving with a minimal amount of user input, and
• anytime, i.e., from an (early) point of the problem–solving process the systems are able to provide (preliminary) solutions to the user’s problem; the quality/detail of the solutions usually improves with further inputs.
We contribute enhanced notions of a test case, that generalize the standard test case to arated test case, where competing solutions can be ordered by their rating state. We also introduce the notion of a sequential test case as a further generalization of the rated test case, where distinct episodes of a particular test case can explicitly be represented. For those extensions we introduce appropriate adaptations of the measures precision and recall. Whenever a test case fails, the effectiveness of inspecting the problem becomes an important issue. The novel visualization technique DDTree (dialog/derivation tree) is introduced, that combines strategic and derivation knowledge in a graphical manner, and that is successfully applied in a number of knowledge engineering projects. DDTrees allow for an intuitive and compact depiction of test cases and they support the manual inspection of erroneous cases by coloring metaphors. The visualization demonstrated its usefulness during debugging sessions of knowledge bases and test cases, respectively. Moreover, it provides an intuitive overview of the validity of the entire test suite. Advanced empirical testing methods are described in (Baumeister, 2009).
4.3 Grey-Box Robustness Testing
As described in the previous section, empirical testing checks the correct behavior of a knowl-edge base by running test cases. Only little research is available, however, that considers the va-lidity of the knowledge in noisy environments. The validation of knowledge bases with respect to noisy or incomplete knowledge is calledrobustness testing(Groot et al., 2000). Robustness testing performs a series of empirical test runs having a varying input or knowledge base quality. In the context of intelligent systems on the web, e.g., Semantic Web applications, the robustness is a very important issue, since these applications are intended to be used by random users.
We contributed to the research on robustness testing by introducing grey-box testing tech-niques (Baumeister et al., 2006). Grey-box testing incorporates background knowledge to make the results of the tests more realistic with respect to the expected application environment. With these extensions, we are able to process knowledge concerning the ambivalence of user inputs and the dependency between different inputs. In consequence, more realistic degradation studies are conducted even for knowledge bases with an interactive interview structure.
5 Applications and Outlook
The introduction of the knowledge formalization continuum and extensible Semantic Wikis de-notes a significant improvement when compared to previous knowledge engineering approaches. Due to the flexible approach of the knowledge formalization and the availability of an appro-priate tool, we are now able to model and combine problem-solving knowledge at different
formalization levels. The conceptual approach of the knowledge formalization continuum and its implementation by the Semantic Wiki KnowWE is currently used in a number of (partly industrial, partly academic) projects, ranging from simple recommender systems to complex decision-support systems for technical and medical devices. For example, KnowWE provides a technical platform to support a biological community within the BIOLOG Wissen4 project
(formerly LaDy). BIOLOG Wissen serves as a web-based application for the collaborative con-struction and use of a decision-support system for landscape diversity. It aims to integrate knowl-edge on causal dependencies of stakeholders, relevant statistical data, and multimedia content. We refer the interested reader to (Nadrowski et al., 2008) for more details. In another recent project, KnowWE is extended by diagnostic workflow knowledge in the context of the CliWE project5. By this extension, the wiki is used to collaboratively develop clinical guidelines, that
are integrated as compiled knowledge bases into next-generation medical devices. A first pro-totype of this extension is reported in Hatko et al. (Hatko et al., 2009). Further applications of the presented approach have been developed in the technical domain for the diagnosis of spe-cial purpose vehicles. In the historical domain, KnowWE serves as a e-Learning platform for representing and teaching knowledge about ancient greek history (Reutelshoefer et al., 2010).
In the future, current methods for the evolutionof ontologies and problem-solving knowl-edge need to be re-considered in the light of the different faces of knowlknowl-edge. Especially in a distributed environment like a Semantic Wiki, the included knowledge is likely to undergo continuous modifications. Adapted evaluation methods and corresponding refactorings of the knowledge across the entire repository will help to understand and manage even complex tasks. First steps into this direction (Baumeister and Nalepa, 2009; Reutelshoefer et al., 2009a) show promising results.
References
Jürgen Angele, Dieter Fensel, Dieter Landes, and Rudi Studer. Developing knowledge-based systems with MIKE. Automated Software Engineering: An International Journal, 5(4):389– 418, October 1998.
Marc Ayel and Jean-Pierre Laurent. Validation, Verification and Test of Knowledge-Based Sys-tems. Wiley, 1991.
Joachim Baumeister. Advanced measures for empirical testing. In FLAIRS’09: Proceedings of the 22th International Florida Artificial Intelligence Research Society Conference, pages 378–383. AAAI Press, 2009.
Joachim Baumeister and Grzegorz J. Nalepa. Verification of distributed knowledge in semantic knowledge wikis. InFLAIRS’09: Proceedings of the 22th International Florida Artificial Intelligence Research Society Conference, pages 384–389. AAAI Press, 2009.
4BIOLOG is funded by the German Federal Ministry of Education and Research from 2007-2009 (final funding phase).
Joachim Baumeister and Frank Puppe. Web-based knowledge engineering with knowledge wikis. In Proceedings of Symbiotic Relationships between Semantic Web and Knowledge Engineering (AAAI 2008 Spring Symposium), 2008.
Joachim Baumeister and Dietmar Seipel. Verification and refactoring of ontologies with rules. InEKAW’06: Proceedings of the 15th International Conference on Knowledge Engineering and Knowledge Management, pages 82–95, Berlin, 2006. Springer.
Joachim Baumeister and Dietmar Seipel. Anomalies in ontologies with rules. Web Semantics: Science, Services and Agents on the World Wide Web, 8(1):55–68, 2010.
Joachim Baumeister, Jürgen Bregenzer, and Frank Puppe. Gray box robustness testing of rule systems. InKI’06: Proceedings of the 29th Annual German Conference on Artificial Intelli-gence, LNAI 4314, pages 346–360. Springer, 2006.
Joachim Baumeister, Thomas Kleemann, and Dietmar Seipel. Towards the verification of on-tologies with rules. InFLAIRS’07: Proceedings of the 20th International Florida Artificial Intelligence Research Society Conference, pages 524–529, 2007.
Joachim Baumeister, Martina Menge, and Frank Puppe. Visualization techniques for the eval-uation of knowledge systems. InFLAIRS’08: Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference, pages 329–334. AAAI Press, 2008. Joachim Baumeister, Jochen Reutelshoefer, and Frank Puppe. Continuous knowledge
engineer-ing with semantic wikis. InCMS’09: Proceedings of 7th Conference on Computer Meth-ods and Systems (Knowledge Engineering and Intelligent Systems), pages 163–168. Opro-gramowanie Naukowo-Techniczne, 2009.
Joachim Baumeister, Jochen Reutelshoefer, and Frank Puppe. KnowWE: A semantic wiki for knowledge engineering. Applied Intelligence, to appear, 2010.
Asunción Gómez-Pérez. Evaluation of ontologies. International Journal of Intelligent Systems, 16(3):391–409, 2001.
Perry Groot, Frank van Harmelen, and Annette ten Teije. Torture tests: A quantitative analysis for the robustness of knowledge-based systems. InKnowledge Acquisition, Modeling and Management, LNAI 1319, pages 403–418, Berlin, 2000. Springer.
Perry Groot, Annette ten Teije, and Frank van Harmelen. A quantitative analysis of the robust-ness of knowledge-based systems through degradation studies. Knowledge and Information Systems, 7(2):224–245, 2003.
Reinhard Hatko, Joachim Baumeister, and Frank Puppe. Diaflux: Diagnostic flows in wikis. In FGWM’09: Proceedings of German Workshop of Knowledge and Experience Management (at LWA’09), 2009.
Ian Horrocks, Bijan Parsia, Peter Patel-Schneider, and James Hendler. Semantic web archi-tecture: Stack or two towers? In Francois Fages and Sylvain Soliman, editors, Principles
and Practice of Semantic Web Reasoning (PPSWR), number 3703 in LNCS, pages 37–41. Springer, 2005.
Matthias Hüttig, Georg Buscher, Thomas Menzel, Wolfgang Scheppach, Frank Puppe, and Hans-Peter Buscher. A diagnostic expert system for structured reports, quality assessment, and training of residents in sonography.Medizinische Klinik, 3:117–22, 2004.
Mikio Kimura, Mitsuo Sakamoto, Takuya Adachi, and Hiroko Sagara. Diagnosis of febrile illnesses in returned travelers using the PC software GIDEON.Travel Medicine and Infectious Disease, 3(3):157–160, 2005.
William Lidwell, Kritina Holden, and Jill Butler. Universal Principles of Design. Rockport Publishers, October 2003. ISBN 1592530079.
Stefan Mersmann and Michel Dojat. SmartCaretm - automated clinical guidelines in critical care. InECAI’04/PAIS’04: Proceedings of the 16th European Conference on Artificial Intel-ligence, including Prestigious Applications of Intelligent Systems, pages 745–749, Valencia, Spain, 2004. IOS Press.
Robert Milne and Charlie Nicol. TIGER: Continuous diagnosis of gas turbines. In ECAI’00: Proceedings of the 14th European Conference on Artificial Intelligence, Berlin, Germany, 2000.
Karin Nadrowski, Joachim Baumeister, and Volkmar Wolters. LaDy: Knowledge Wiki zur kollaborativen und wissensbasierten Entscheidungshilfe zu Umweltveränderung und Biodi-versität. Naturschutz und Biologische Vielfalt, 60:171–176, 2008.
Dang Duc Nghia and Frank Puppe. Hybrides, skalierbares Diagnosesystem für freie Kfz-Werkstätten.KI, 23(2):31–37, 2009.
Alun Preece, Rajjan Shinghal, and Aida Batarekh. Principles and practice in verifying rule-based systems.The Knowledge Engineering Review, 7 (2):115–141, 1992.
Jochen Reutelshoefer, Joachim Baumeister, and Frank Puppe. A data structure for the refactor-ing of multimodal knowledge. InKESE’09: 5th Workshop on Knowledge Engineering and Software Engineering (CEUR proceedings 486), Paderborn, 2009a.
Jochen Reutelshoefer, Florian Lemmerich, Fabian Haupt, and Joachim Baumeister. An exten-sible semantic wiki architecture. InSemWiki’09: Fourth Workshop on Semantic Wikis – The Semantic Wiki Web (CEUR proceedings 464), 2009b.
Jochen Reutelshoefer, Florian Lemmerich, Joachim Baumeister, Jorit Wintjes, and Lorenz Haas. Taking OWL to athens – semantic web technology takes ancient greek history to students. to appear, 2010.
Sebastian Schaffert, François Bry, Joachim Baumeister, and Malte Kiesel. Semantic wikis.IEEE Software, 25(4):8–11, 2008.
Guus Schreiber, Hans Akkermans, Anjo Anjewierden, Robert de Hoog, Nigel Shadbolt, Wal-ter Van de Velde, and Bob Wielinga. Knowledge Engineering and Management - The Com-monKADS Methodology. MIT Press, 2 edition, 2001.
Wikipedia. Continuum (theory) — wikipedia, the free encyclopedia, 2010. [Online; accessed 10-February-2010].
Publications
Monographs & Special Issues
[1] J. Baumeister. Agile Development of Diagnostic Knowledge Systems. IOS Press, AKA, DISKI 284, 2004.
[2] J. Baumeister and G. J. Nalepa, editors. Special Issue on Knowledge and Software Engi-neering for Intelligent Systems, to appear. International Journal of Knowledge EngiEngi-neering and Data Mining (IJKEDM), 2010.
Proceedings
[1] J. Baumeister and D. Seipel, editors. KESE: 1st Workshop on Knowledge Engineering and Software Engineering. Workshop notes of 28th Annual German Conference on Artificial Intelligence (KI-2005), Koblenz, Germany, 2005.
[2] J. Baumeister and D. Seipel, editors. KESE: 2nd Workshop on Knowledge Engineering and Software Engineering. Workshop notes of 29th Annual German Conference on Artificial Intelligence (KI-2006), Bremen, Germany, 2006.
[3] J. Baumeister and M. Schaaf, editors. Proceedings of the Workshop: ’Knowledge and Ex-perience Management’ (German SIG meeting, FGWM). 2007.
[4] J. Baumeister and D. Seipel, editors. KESE: 3rd Workshop on Knowledge Engineering and Software Engineering. Workshop notes of 30th Annual German Conference on Artificial Intelligence (KI-2007), CEUR Proceedings 282, Osnabrück, Germany, 2007.
[5] J. Baumeister and M. Atzmueller, editors. Lernen, Wissen und Adaptivität. University Würzburg, Computer Science, TR 448, 2008.
[6] J. Baumeister and N. Müller, editors. Proceedings of the Workshop: ’Knowledge and Expe-rience Management’ (German SIG meeting, FGWM). 2008.
[7] G. J. Nalepa and J. Baumeister, editors. KESE: 4th Workshop on Knowledge Engineering and Software Engineering. Workshop notes of 31th Annual German Conference on Artificial Intelligence (KI-2008), CEUR Proceedings 425, Kaiserslautern, Germany, 2008.
[8] J. Baumeister and G. J. Nalepa, editors. KESE: 5th Workshop on Knowledge Engineering and Software Engineering. Workshop notes of 32nd Annual German Conference on Artifi-cial Intelligence (KI-2009), CEUR Proceedings 486, Paderborn, Germany, 2009.
Book Chapters
[1] M. Neumann and J. Baumeister. A Rule–Based vs. a Set-Covering Implementation of the Knowledge System LIMPACT and its Significance for Maintenance and Discovery of Eco-logical Knowledge. InModelling Community Structure in Freshwater Ecosystems, pages 401–410. Springer, Berlin, 2005.
[2] J. Baumeister, D. Seipel, and F. Puppe. Agile Development of Rule Systems. In Giurca, Gasevic, and Taveter, editors,Handbook of Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches. IGI Publishing, 2009.
[3] S. Schaffert, F. Bry, J. Baumeister, and M. Kiesel. Semantische Wikis. In A. Blumauer and T. Pellegrini, editors,Social Semantic Web. Springer, 2009.
Journal Articles
[1] M. Neumann, J. Baumeister, M. Liess, and R. Schulz. An Expert System to Estimate the Pesticide Contamination of Small Streams using Benthic Macroinvertebrates as Bioindi-cators, Part 2: The Knowledge Base of LIMPACT. Ecological Indicators, 2(4):391–401, 2002.
[2] M. Neumann, J. Baumeister, M. Liess, and R. Schulz. LIMPACT: Ein Expertensys-tem zur Abschätzung der Pflanzenschutzmittel-Belastung kleiner Fließgewässer mittels der Makroinvertebraten-Fauna.Umweltwissenschaften und Schadstoff-Forschung (USWF), 3:152–156, 2002.
[3] J. Baumeister, D. Seipel, and F. Puppe. Incremental Development of Diagnostic Set-Covering Models with Therapy Effects. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(Suppl. Issue 2):25–49, 2003.
[4] J. Baumeister, F. Puppe, and D. Seipel. An Agile Process Model for Developing Diagnostic Knowledge Systems. Künstliche Intelligenz, 3/04:12–16, 2004.
[5] M. Neumann, J. Baumeister, and F. Puppe. ILMAX: A System for Managing Experience Knowledge in a long–term Study of Stream Ecosystem Regeneration – An Application of Ecological Informatics.Management of Environmental Quality: An International Journal, 15(3):306–317, 2004.
[6] D. Seipel and J. Baumeister. Declarative Methods for the Evaluation of Ontologies. Kün-stliche Intelligenz, 4/04:51–57, 2004.
[7] J. Baumeister and D. Seipel. Anfragesprachen für das Semantic Web.Informatik Spektrum, 28(1):40–44, 2005.
[8] M. Atzmueller, J. Baumeister, M. Goller, and F. Puppe. A Datagenerator for Evaluating Machine Learning Methods. Künstliche Intelligenz, 3/06:57–63, 2006.
[9] S. Schaffert, F. Bry, J. Baumeister, and M. Kiesel. Semantic Wiki. Informatik Spektrum, 30(6):434–439, 2007.
[10] K. Nadrowski, J. Baumeister, and V. Wolters. LaDy: Knowledge Wiki zur kollabora-tiven und wissensbasierten Entscheidungshilfe zu Umweltveränderung und Biodiversität. Naturschutz und Biologische Vielfalt, 60:171–176, 2008.
[11] S. Schaffert, F. Bry, J. Baumeister, and M. Kiesel. Semantic Wikis. IEEE Software, 25(4):8–11, 2008.
[12] J. Baumeister, J. Reutelshoefer, and F. Puppe. KnowWE: A Semantic Wiki for Knowledge Engineering. Applied Intelligence, to appear, 2010.
[13] J. Baumeister and D. Seipel. Anomalies in Ontologies with Rules.Web Semantics: Science, Services and Agents on the World Wide Web, 8(1):55–68, 2010.
Submitted Journal Articles
[1] J. Baumeister. Advanced Empirical Testing. Knowledge-Based Systems, submitted Mar 26, 2009.
[2] J. Baumeister and M. Freiberg. Knowledge Visualization for Evaluation Tasks. Knowledge and Information Systems, submitted Jan 15, 2010.
International Conferences
[1] J. Baumeister, M. Atzmueller, and F. Puppe. Inductive Learning for Case-Based Diagnosis with Multiple Faults. In ECCBR’02: Proceedings of the 6th European Conference on Case-Based Reasoning, LNAI 2416, pages 28–42. Springer, Berlin, 2002.
[2] M. Neumann and J. Baumeister. A Rule-Based vs. a Model-Based Implementation of the Knowledge System LIMPACT and its Significance for Maintenance and Discovery of Ecological Knowledge. InISEI’02: Proceedings of the 3rd Conference of the International Society for Ecological Informatics. 2002.
[3] M. Atzmueller, J. Baumeister, and F. Puppe. Inductive Learning of Simple Diagnostic Scores. InISMDA’03: Proceedings of the International Symposium of Medical Data Anal-ysis, LNCS 2868, pages 23–30. Springer, Berlin, 2003.
[4] M. Neumann, J. Baumeister, and F. Puppe. ILMAX: A System for Managing Experience Knowledge in a long–term Study of Stream Ecosystem Regeneration – an Application of Ecological Informatics. In ITEE’2003: Proceedings of the 1st International NAISO Symposium on Information Technologies in Environmental Engineering. 2003.
[5] M. Atzmueller, W. Shi, J. Baumeister, F. Puppe, and J. A. Barnden. Case-Based Ap-proaches for Diagnosing Multiple Disorders. InFLAIRS’04: Proceedings of the 17th In-ternational Florida Artificial Intelligence Research Society Conference, pages 154–159. 2004.
[6] J. Baumeister, D. Seipel, and F. Puppe. Refactoring Methods for Knowledge Bases. In EKAW’04: Engineering Knowledge in the Age of the Semantic Web: 14th International Conference, LNAI 3257, pages 157–171. Springer, Berlin, 2004.
[7] J. Baumeister, D. Seipel, and F. Puppe. Using Automated Tests and Restructuring Methods for an Agile Development of Diagnostic Knowledge Systems. InFLAIRS’04: Proceedings of the 17th International Florida Artificial Intelligence Research Society Conference, pages 319–324. 2004.
[8] W. Shi, J. A. Barnden, J. Baumeister, and M. Atzmueller. An Intelligent Diagnosis System Handling Multiple Disorders. InICIIP’04: Proceedings of the International Conference on Intelligent Information Processing. 2004.
[9] M. Atzmueller, J. Baumeister, A. Hemsing, E.-J. Richter, and F. Puppe. Subgroup Mining for Interactive Knowledge Refinement. InAIME’05: Proceedings of the 10th Conference on Artificial Intelligence in Medicine, LNAI 3581, pages 453–462. Springer, Berlin, 2005. [10] M. Atzmueller, J. Baumeister, and F. Puppe. Quality Measures and Semi-Automatic
Min-ing of Diagnostic Rule Bases. InINAP/WLP’04: Applications of Declarative Programming and Knowledge Management (selected papers), LNAI 3392, pages 65–78. Springer, Berlin, 2005.
[11] J. Baumeister, R. Knauf, and F. Puppe. Semi-Automatic Generation of Test Cases by Case Morphing. InFLAIRS’05: Proceedings of the 18th International Florida Artificial Intelligence Research Society Conference, pages 814–815. AAAI Press, 2005.
[12] J. Baumeister and D. Seipel. Smelly Owls – Design Anomalies in Ontologies. In FLAIRS’05: Proceedings of the 18th International Florida Artificial Intelligence Research Society Conference, pages 215–220. AAAI Press, 2005.
[13] G. Buscher, J. Baumeister, and F. P. D. Seipel. User–Centered Consultation by a Society of Agents. InK-CAP ’05: Proceedings of the 3rd International Conference on Knowledge Capture, pages 27–34. ACM, New York, NY, USA, 2005.
[14] M. Hopfner, D. Seipel, and J. Baumeister. A PROLOG Tool for Slicing Source Code. In WLP’05: Proceedings of the 19th Workshop on (Constraint) Logic Programming. 2005. [15] D. Seipel, M. Hopfner, and J. Baumeister. Declarative Querying and Visualizing
Knowl-edge Bases in XML. In INAP/WLP’04: Applications of Declarative Programming and Knowledge Management (selected papers), LNAI 3392, pages 16–31. Springer, Berlin, 2005.
[16] W. Shi, J. A. Barnden, M. Atzmueller, and J. Baumeister. An Intelligent Diagnosis System Handling Multiple Disorders. IFIP International Federation for Information Processing, 163:421–430, 2005.
[17] M. Atzmueller, J. Baumeister, and F. Puppe. Semi-Automatic Learning of Simple Diagnos-tic Scores utilizing Complexity Measures.Artificial Intelligence in Medicine, 37(1):19–30, 2006.
[18] J. Baumeister, M. Atzmueller, P. Kluegl, and F. Puppe. Conservative and Creative Strate-gies for the Refinement of Scoring Rules. InFLAIRS’06: Proceedings of the 19th Interna-tional Florida Artificial Intelligence Research Society Conference, pages 408–413. 2006. [19] J. Baumeister, M. Atzmueller, and F. Puppe. Introspective Subgroup Analysis for
Interac-tive Knowledge Refinement. InFLAIRS’06: Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference, pages 402–407. 2006.
[20] J. Baumeister, J. Bregenzer, and F. Puppe. Gray Box Robustness Testing of Rule Systems. InKI’06: Proceedings of the 29th Annual German Conference on Artificial Intelligence, LNAI 4314, pages 346–360. Springer, 2006.
[21] J. Baumeister and D. Seipel. Verification and Refactoring of Ontologies With Rules. In EKAW’06: Proceedings of the 15th International Conference on Knowledge Engineering and Knowledge Management, pages 82–95. Springer, Berlin, 2006.
[22] M. Atzmueller, J. Baumeister, P. Klügl, and F. Puppe. Rapid Knowledge Capture Using Subgroup Discovery with Incremental Refinement. In K-CAP ’07: Proceedings of the 4th International Conference on Knowledge Capture, pages 31–38. ACM, New York, NY, USA, 2007.
[23] M. Atzmueller, J. Baumeister, and F. Puppe. Pattern–Constrained Test Case Generation. In FLAIRS’07: Proceedings of the 20th International Florida Artificial Intelligence Research Society Conference, pages 518–523. 2007.
[24] J. Baumeister, T. Kleemann, and D. Seipel. Towards the Verification of Ontologies with Rules. InFLAIRS’07: Proceedings of the 20th International Florida Artificial Intelligence Research Society Conference, pages 524–529. 2007.
[25] J. Baumeister, J. Reutelshoefer, and F. Puppe. KnowWE – Community–based Knowledge Capture with Knowledge Wikis. In K-CAP ’07: Proceedings of the 4th International Conference on Knowledge Capture, pages 189–190. ACM, New York, NY, USA, 2007. [26] J. Baumeister, M. Menge, and F. Puppe. Visualization Techniques for the Evaluation of
Knowledge Systems. InFLAIRS’08: Proceedings of the 21th International Florida Artifi-cial Intelligence Research Society Conference, pages 329–334. AAAI Press, 2008. [27] D. Seipel and J. Baumeister. Declarative Specification and Interpretation of Rule-Based
Systems. InFLAIRS’08: Proceedings of the 21th International Florida Artificial Intelli-gence Research Society Conference, pages 359–364. AAAI Press, 2008.
[28] J. Baumeister. Advanced Measures for Empirical Testing. InFLAIRS’09: Proceedings of the 22th International Florida Artificial Intelligence Research Society Conference, pages 378–383. AAAI Press, 2009.
[29] J. Baumeister and G. J. Nalepa. Verification of Distributed Knowledge in Semantic Knowl-edge Wikis. InFLAIRS’09: Proceedings of the 22th International Florida Artificial Intel-ligence Research Society Conference, pages 384–389. AAAI Press, 2009.
[30] J. Baumeister, J. Reutelshoefer, and F. Puppe. Continuous Knowledge Engineering with Semantic Wikis. In CMS’09: Proceedings of 7th Conference on Computer Methods and Systems (Knowledge Engineering and Intelligent Systems), pages 163–168. Opro-gramowanie Naukowo-Techniczne, 2009.
National Conferences
[1] M. Neumann, J. Baumeister, M. Liess, and R. Schulz. LIMPACT: Ein Expertensys-tem zur Abschätzung der Pflanzenschutzmittel-Belastung kleiner Fließgewässer mittels der Makroinvertebraten-Fauna. In SETAC-GLB’01: Proceedings der SETAC-GLB Tagung. 2001.
[2] M. Atzmueller, J. Baumeister, and F. Puppe. Evaluation of two Strategies for Case-Based Diagnosis handling Multiple Faults. InWM’03: Proceedings of the 2nd Conference "Pro-fessionelles Wissensmanagement". 2003.
[3] M. Atzmueller, J. Baumeister, A. Hemsing, E.-J. Richter, and F. Puppe. Using Subgroup Mining for the Refinement of Knowledge Systems. In GfKl’05: Proceedings of the 29th Annual Conference of the German Classification Society. 2005.
[4] M. Atzmueller, J. Baumeister, and F. Puppe. Exemplifying Subgroup Mining Results for Interactive Knowledge Refinement. InLIT’05: Proceedings of the 13th Leipziger Informatik Tage, LNI P-72, pages 101–106. 2005.
[5] J. Baumeister, J. Bregenzer, and F. Puppe. A Methodological View on Robustness Testing of Rule-Based Knowledge Systems. InLIT’05: Proceedings of the 13th Leipziger Informatik Tage, LNI P-72, pages 131–138. 2005.
[6] S. Schulz, J. Baumeister, A. Crössmann, F. Puppe, and P. Pauli. www.icdforum.de -Ein internetbasiertes Programm für Patienten mit implantiertem Cardioverter Defibrillator. Beiträge zur Gesundheitspsychologie, Gmünder Hochschulreihe Nr. 29, 2007.
[7] S. M. Schulz, J. Baumeister, G. W. Alpers, A. Crössmann, H. Neuser, F. Puppe, and P. Pauli. An Internet-based Intervention to Reduce Cardiac Fear in Patients with Implantable Car-dioverter Defibrillator. Abstract in Conference Proceedings of the 9. Jahrestagung der Gesellschaft für Angstforschung, 2007.
Workshops
[1] J. Baumeister, D. Seipel, and F. Puppe. Incremental Development of Diagnostic Set– Covering Models with Therapy Effects. InProceedings of the KI-2001 Workshop on Un-certainty in Artificial Intelligence. 2001.
[2] J. Baumeister and D. Seipel. Diagnostic Reasoning with Multilevel Set-Covering Models. InDX’02: Proceedings of the 13th International Workshop on Principles of Diagnosis. 2002.
[3] A. Hörnlein, J. Baumeister, and F. Puppe. Modelle für die Generierung von Folgesitzungen zur Therapieüberwachung in fallbasierten Trainingssystemen. InCBT’03: Proceedings zum 7. Workshop der GMDS AG Computergestützte Lehr- und Lernsysteme in der Medizin. Shaker, 2003.
[4] K.-W. Lorenz, J. Baumeister, C. Greim, N. Roewer, and F. Puppe. QualiTEE - An Intelli-gent Guidance and Diagnosis System for the Documentation of Transesophageal Echocar-diography Examinations. ESCTAIC’03: Proceedings of the 14th Annual Meeting of the European Society for Computing and Technology in Anaesthesia and Intensive Care, 2003. [5] N. Bruemmer, J. Baumeister, D. Riewenherm, F. Puppe, and J. Broscheit. Visual Devel-opment of Temporal Patterns for Medical Data Abstraction. InIDAMAP’06: Proceedings of the Workshop on Intelligent Data Analysis in Biomedicine and Pharmacology, pages 37–38. 2006.
[6] J. Baumeister, J. Reutelshoefer, K. Nadrowski, and A. Misok. Using Knowledge Wikis to Support Scientific Communities. InSCOOP’07: Proceedings of 1st Workshop on Scientific Communities of Practice. Bremen, Germany, 2007.
[7] J. Baumeister, J. Reutelshoefer, and F. Puppe. Markups for Knowledge Wikis. In SAAKM’07: Proceedings of the Semantic Authoring, Annotation and Knowledge Markup Workshop, pages 7–14. Whistler, Canada, 2007.
[8] J. Baumeister and F. Puppe. Web-based Knowledge Engineering with Knowledge Wikis. InProceedings of Symbiotic Relationships between Semantic Web and Knowledge Engi-neering (AAAI 2008 Spring Symposium). 2008.
[9] J. Baumeister, J. Reutelshoefer, F. Haupt, and K. Nadrowski. Capture and Refactoring in Knowledge Wikis – Coping with the Knowledge Soup. InSCOOP’08: Proceedings of 2nd Workshop on Scientific Communities of Practice. Bremen, Germany, 2008.
[10] J. Reutelshoefer, J. Baumeister, and F. Puppe. Ad-Hoc Knowledge Engineering with Se-mantic Knowledge Wikis. InSemWiki’08: Proceedings of 3rd Semantic Wiki workshop -The Wiki Way of Semantics (CEUR Proceedings 360). 2008.
[11] J. Baumeister, J. Reutelshoefer, and F. Puppe. Engineering on the Knowledge Formaliza-tion Continuum. InSemWiki’09: Proceedings of 4th Semantic Wiki workshop. 2009.
[12] M. Freiberg, J. Baumeister, and F. Puppe. The Usability Stack: Reconsidering Usability Criteria regarding Knowledge-Based Systems. InLWA-2009 (Special Track on Knowledge Management). 2009.
[13] R. Hatko, J. Baumeister, and F. Puppe. DiaFlux: Diagnostic Flows in Wikis. In FGWM’09: Proceedings of German Workshop of Knowledge and Experience Manage-ment (at LWA’09). 2009.
[14] J. Reutelshoefer, F. Lemmerich, F. Haupt, and J. Baumeister. An Extensible Semantic Wiki Architecture. InSemWiki’09: Fourth Workshop on Semantic Wikis – The Semantic Wiki Web (CEUR proceedings 464). 2009.
Miscellaneous Publications
[1] J. Broscheit, K.-W. Lorenz, J. Baumeister, P. Kranke, and C. Greim. Determinants of Alarm-Rate and the Potential of Intelligent Monitoring Systems in Routine Anaesthesiological Use. ESCTAIC’02: Proceedings of the 13th Annual Meeting of the European Society for Com-puting and Technology in Anaesthesia and Intensive Care, 2002.
[2] K.-W. Lorenz, C. Greim, J. Baumeister, F. Puppe, and N. Roewer. EchoDOC - intelligentes Diagnose- und Dokumentationssystem für die TEE-Untersuchung. DGAI’04: Proceedings of the 51st Annual Meeting of the German Association of Anaesthesiologists, 2004. [3] N. Bruemmer, J. Baumeister, and F. Puppe. Relations between Visual and Textual
Represen-tations of Temporal Patterns for Medical Data Abstraction. Technical Report 394, Computer Science, University of Würzburg, Germany, 2006.
[4] G. Buscher, J. Baumeister, F. Puppe, and D. Seipel. Semi-Distributed Development of Agent-Based Consultation Systems. InEKAW’06: Poster–Proceedings of the 15th Inter-national Conference on Knowledge Engineering and Knowledge Management. 2006. [5] M. Freiberg and J. Baumeister. A Survey on Usability Evaluation Techniques and an
Anal-ysis of their actual Application. Technical Report 450, Computer Science, University of Würzburg, Germany, 2008.
