Joachim Baumeister

KnowWE: a Semantic Wiki for knowledge engineering

Recently, Semantic Wikis showed reasonable success as collaboration platforms in the context of social semantic applications. In this paper, we present a novel approach, that interprets the concept of Semantic Wikis as a knowledge engineering environment, that effectively help to build decision-support systems. We introduce the Semantic Wiki KnowWE, that provides the possibility to define and maintain ontologies together with strong problem-solving knowledge. Thus, the wiki can be used to collaboratively build decision-support systems. These enhancements require extensions of the standard Semantic Wiki architecture by a task ontology for problem-solving and an adapted reasoning process. We discuss these extensions in detail, and we describe a case study in the field of medical emergency systems.

Inductive Learning for Case-Based Diagnosis with Multiple Faults

We present adapted inductive methods for learningsimilarities, parameter weights and diagnostic profiles for case-based reasoning. All of these methods can be refined incrementally by applyingdif ferent types of background knowledge. Diagnostic profiles are used for extending the conventional CBR to solve cases with multiple faults. The context of our work is to supplement a medical documentation and consultation system by CBR techniques, and we present an evaluation with a real-world case base.

An expert system to estimate the pesticide contamination of small streams using benthic macroinvertebrates as bioindicators: II. The knowledge base of LIMPACT

We developed an expert system (LIMPACT) to estimate the pesticide contamination of streams using macroinvertebrate indicators. Here, we present the database consisting of 157 data sets obtained from 1992 to 2000 through investigation of 104 small headwater streams with an agricultural catchment area. The contamination by pesticides (insecticides, fungicides and herbicides) during rainfall events varied greatly in both water and suspended-particle samples, occasionally reaching ecotoxicologically relevant levels. On the basis of standardised toxicities, the data sets were grouped into Not Detected (n = 55), Low (34), Moderate (42) and High (26) contamination with pesticides. Additionally, nine water-quality and morphological parameters were evaluated with regard to their influence on the fauna and are used to exclude unsuitable streams from LIMPACT. The benthic macroinvertebrate fauna data were divided into four time frames (March/April; May/June; July/August; September/October) and analysed regarding the abundance and the abundance dynamics of the 39 most common taxa. In contaminated streams, lower abundance for negative and higher for positive indicator taxa were observed. The number of taxa was significantly lower (unpaired t-test P< 0.015) in the most severely contaminated streams. Information abstracted from this empirical approach was used to create rules indicating or not indicating contamination and to build up the heuristic knowledge base of LIMPACT as shown in the Part 2 paper (M. Neumann, J. Baumeister, M. Liess, R. Schulz, An expert system to estimate the pesticide contamination of small streams using benthic macroinvertebrate as bioindicators. Part 2. The knowledge base of LIMPACT, Ecological Indicators, this issue).

Anomalies in ontologies with rules

For the development of practical semantic applications, ontologies are commonly used with rule extensions. Prominent examples of semantic applications not only are Semantic Wikis, Semantic Desktops, but also advanced Web Services and agents. The application of rules increases the expressiveness of the underlying knowledge in many ways. Likewise, the integration not only creates new challenges for the design process of such ontologies, but also existing evaluation methods have to cope with the extension of ontologies by rules. Since the verification of Owl ontologies with rule extensions is not tractable in general, we propose to verify ontologies at the symbolic level by using a declarative approach: With the new language Datalog^@?, known anomalies can be easily specified and tested in a compact manner. We introduce supplements to existing verification techniques to support the design of ontologies with rule enhancements, and we focus on the detection of anomalies that especially occur due to the combined use of rules and ontological definitions.

KnowWE: community-based knowledge capture with knowledge wikis

This paper presents a collaborative knowledge engineering approach based on the widespread wiki technique.The interface of a standard wiki system is extended to allow for the capture, the maintenance and the use of knowledge systems.

Subgroup mining for interactive knowledge refinement

When knowledge systems are deployed into a real-world application, then the maintenance of the knowledge is a crucial success factor. In the past, some approaches for the automatic refinement of knowledge bases have been proposed. Many only provide limited control during the modification and refinement process, and often assumptions about the correctness of the knowledge base and case base are made. However, such assumptions do not necessarily hold for real-world applications. In this paper, we present a novel interactive approach for the user-guided refinement of knowledge bases. Subgroup mining methods are used to discover local patterns that describe factors potentially causing incorrect behavior of the knowledge system. We provide a case study of the presented approach with a fielded system in the medical domain.

Incremental development of diagnostic set-covering models with therapy effects

Although a lot of work in the field of knowledge acquisition has been done, the manual development of diagnostic knowledge systems by domain experts still is a very complex task. In this paper we will present an incremental approach for building diagnostic systems based on set-covering models. We start with a simple model describing the coarse structure between diagnoses and findings. Subsequently, this simple model can be enhanced by similarities, weights and probabilities to increase the accuracy of the knowledge and the resulting system. We will also show how these static set-covering models can be combined with dynamic set-covering models including higher level knowledge about causation effects. We will motivate how dynamic set-covering models can be used for implementing diagnostic systems including therapy effects. Finally, we report on two practical applications dealing with set-covering models from the geoecological and from the medical domain, respectively, that we have implemented.

Verification and refactoring of ontologies with rules

Currently, the introduction of an appropriate rule representation layer for the semantic web stack is discussed. However, with the inclusion of rule-based knowledge new verification issues for rule-augmented ontologies arise. In this paper we investigate the detection of anomalies as an important subtask of verification. We extend and revise existing approaches for the syntactic verification of ontologies with respect to the existence of rules, and we introduce new anomalies considering the understandability and maintainability of such ontologies.

Engineering intelligent systems on the knowledge formalization continuum

Engineering intelligent systems on the knowledge formalization continuum In spite of their industrial success, the development of intelligent systems is still a complex and risky task. When building intelligent systems, we see that domain knowledge is often present at different levels of formalization—ranging from text documents to explicit rules. In this paper, we describe the knowledge formalization continuum as a metaphor to help domain specialists during the knowledge acquisition phase. To make use of the knowledge formalization continuum, the agile use of knowledge representations within a knowledge engineering project is proposed, as well as transitions between the different representations, when required. We show that a semantic wiki is a flexible tool for engineering knowledge on the knowledge formalization continuum. Case studies are taken from one industrial and one academic project, and they illustrate the applicability and benefits of semantic wikis in combination with the knowledge formalization continuum.

Knowledge visualization for evaluation tasks

Although various methods for the evaluation of intelligent systems have been proposed in the past, almost no techniques are present that support the manual inspection of knowledge bases by the domain specialist. Manual knowledge base inspection is an important and frequently applied method in knowledge engineering. Since it can hardly be performed in an automated manner, it is a time-consuming and costly task. In this paper, we discuss a collection of appropriate visualization techniques that help developers to interactively browse and analyze the knowledge base in order to find deficiencies and semantic errors in their implementation. We describe standard visualization methods adapted to specifically support the analysis of the static knowledge base structure, but also of the usage of knowledge base objects such as questions or solutions. Additionally, we introduce a novel visualization technique that supports the validation of the derivation and interview behavior of a knowledge system in a semi-automatic manner. The application of the presented methods was motivated by the daily practice of knowledge base development.