Knut Hinkelmann

Toward a technology for organizational memories

To meet the growing need for enterprise-wide knowledge management, the authors have developed and fielded a three-layered model for processing knowledge. This article shows how their organizational memory serves as an intelligent assistant and deals with both formal and non-formal knowledge elements in a task-oriented fashion.

Context-Aware, Proactive Delivery of Task-Specific Information: The KnowMore Project

From an IT point of view, a key objective of successful knowledge management is to provide relevant and necessary information at the right time to support humans in accomplishing their tasks. This paper presents a prototypical system which meets this objective in an enterprise environment. Based on context information associated with the enterprises business processes, an integration of workflow engine and information assistant enables active presentation of relevant information to the user. We describe the functionality of the system and elaborate (i) on necessary extensions to the business process models, (ii) the ontologies used for information modeling, and (iii) the integration of workflow engine and active information assistant. The prototype system has been developed in the KnowMore project of the DFKI Knowledge Management Group.

A Competence Knowledge Base System as Part of the Organizational Memory

Personal competences of experienced employees are the most important knowledge assets of knowledge-work oriented enterprises. Thus, it makes perfect sense to start IT support for enterprise knowledge management with a system that facilitates finding of appropriate contact persons for business tasks which require specific knowledge, experiences, or skills. We propose such a competence knowledge base system (CKBS) which builds upon an ontology-based model of competence fields, the use of which allows (i) comprehensive multi-criteria organization and queries for personal competences, (ii) complex heuristic inferences for finding knowledgeable persons in spite of vaguely specified information needs, and (iii) easy integration of the CKBS into an overall organizational memory information system.

ARC-TEC : acquisition, representation and compilation of technical knowledge

A global description of an expert system shell for the domain of mechanical engineering is presented. The ARC-TEC project constitutes an AI approach to realize the CIM idea. Along with conceptual solutions, it provides a continuous sequence of software tools for the acquisition, representation and compilation of technical knowledge. The shell combines the KADS knowledge-acquisition methodology, the KL-ONE representation theory and the WAM compilation technology. For its evaluation a prototypical expert system for production planning is developed. A central part of the system is a knowledge base formalizing the relevant aspects of common sense in mechanical engineering. Thus, ARC-TEC is less general than the CYC project but broader than specific expert systems for planning or diagnosis.

Integrationspotenziale für Geschäftsprozesse und Wissensmanagement

Seit Mitte der 90er Jahre gewinnt Wissensmanagement (WM) als ganzheitlicher Ansatz zur Verbesserung der Innovationsfahigkeit, der Prozesseffizienz und der Anpassungsfahigkeit an standig wechselnde Anforderungen zunehmende Bedeutung in Unternehmen und Organisationen (vgl. Nonaka u. Takeuchi 1997; Probst et al. 1997; Davenport u. Prusak 1998). Der vorliegende Sammelband konzentriert sich auf das „Geschaftsprozessorientierte Wissensmanagement“ und bereitet innovative Beratungsmethoden und Softwarelosungen, aktuellste Forschungsergebnisse, sowie Erfahrungen mit forschungsnahen Prototypen auf. Dabei wenden wir uns in Themenauswahl und -darstellung an Praktiker in Beratung, IT-Abteilungen und Management, bzw. an die praxisorientierte Lehre und die anwendungsorientierte Forschung. Das Buch soli diese neue Thematik so umfassend und wohlstrukturiert darstellen, wie dies derzeit mit praxisfahigen Ergebnissen moglich ist. Es soil konkret umsetzbare, gleichwohl methodisch durchdachte und wissenschaft lich abgesicherte Handreichungen geben, wie man durch Beachtung der Synergie von Wissens- und Geschaftsprozessmanagernent innovativere, okonomisch nutzbringendere und insgesamt erfolgversprechendere Projekte in diesem Bereich aufsetzen kann.

Combining Terminological and Rule-based Reasoning for Abstraction Processes

Terminological reasoning systems directly support the abstraction mechanisms generalization and classification. But they do not bother about aggregation and have some problems with reasoning demands such as concrete domains, sequences of finite but unbounded size and derived attributes. The paper demonstrates the relevance of these issues in an analysis of a mechanical engineering application and suggests an integration of a forward-chaining rule system with a terminological logic as a solution to these problems.

Techniques for organizational memory information systems

The KnowMore project aims at providing active support to humans working on knowledge-intensive tasks. To this end the knowledge available in the modeled business processes or their incarnations in specific workfiows shall be used to improve information handling. We present a representation formalism for knowledge-intensive tasks and the specification of its object-oriented realization. An operational semantics is sketched by specifying the basic functionality of the Knowledge Agent which works on the knowledge intensive task representation. The Knowledge Agent uses a meta-level description of all information sources available in the Organizational Memory. We discuss the main dimensions that such a description scheme must be designed along, namely information content, structure, and context. On top of relational database management systems, we basically realize deductive object-oriented modeling with a comfortable annotation facility. The concrete knowledge descriptions are obtained by configuring the generic formalism with ontologies which describe the required modeling dimensions. To support the access to documents, data, and formal knowledge in an Organizational Memory an integrated domain ontology and thesaurus is proposed which can be constructed semi-automatically by combining document-analysis and knowledge engineering methods. Thereby the costs for up-front knowledge engineering and the need to consult domain experts can be considerably reduced. We present an automatie thesaurus generation tool and show how it can be applied to build and enhance an integrated ontology /thesaurus. A first evaluation shows that the proposed method does indeed facilitate knowledge acquisition and maintenance of an organizational memory.

Knowledge-Base Evolution for Product and Production Planning

Knowledge-base evolution techniques are shown to be of critical importance for the successful application of knowledge-based systems in complex domains. By conceptualizing knowledge-base evolution as theory revision, we can take advantage of the basic findings from different research communities. Results from Inductive Logic Programming ILP and Explanation-Based Learning EBL provide a set of techniques that can be used as a foundation for obtaining new knowledge knowledge-base exploration. Techniques from deductive database research might be used for testing the correctness of a knowledge base knowledge base verification. By an interactive application of these exploration and verification techniques, domain experts and other users may similarly improve the effectiveness of the knowledge base knowledge validation. The application of such selected techniques is then discussed with respect to the specific problem of improving production parameters.

CoLab: A hybrid knowledge representation and compilation laboratory

Knowledge bases for real-world domains such as mechanical engineering require expressive and efficient representation and processing tools. We pursue a declarative-compilative approach to knowledge engineering.While Horn logic (as implemented in PROLOG) is well-suited for representing relational clauses, other kinds of declarative knowledge call for hybrid extensions: functional dependencies and higher-order knowledge should be modeled directly. Forward (bottom-up) reasoning should be integrated with backward (top-down) reasoning. Constraint propagation should be used wherever possible instead of search-intensive resolution. Taxonomic knowledge should be classified into an intuitive subsumption hierarchy.Our LISP-based tools provide directtranslators of these declarative representations into abstract machines such as an extended Warren Abstract Machine (WAM) and specialized inference engines that are interfaced to each other. More importantly, we provide source-to-sourcetransformers between various knowledge types, both for user convenience and machine efficiency.These formalisms with their translators and transformers have been developed as part of CoLab, acompilationlaboratory for studying what we call, respectively, ‘vertical’ and ‘horizontal’ compilation of knowledge, as well as for exploring the synergeticcolaboration of the knowledge representation formalisms.A case study in the realm of mechanical engineering has been an important driving force behind the development of CoLab. It will be used as the source of examples throughout the paper when discussing the enhanced formalisms, the hybrid representation architecture, and the compilers.

Computing Cost Estimates for Proof Strategies

In this paper we extend work of Treitel and Genesereth for calculating cost estimates for alternative proof methods of logic programs. We consider four methods: (1) forward chaining by semi-naive bottom-up evaluation, (2) goal-directed forward chaining by semi-naive bottom-up evaluation after Generalized Magic-Sets rewriting, (3) backward chaining by OLD resolution, and (4) memoing backward chaining by OLDT resolution. The methods can interact during a proof. After motivating the advantages of each of the proof methods, we show how the effort for the proof can be estimated. The calculation is based on indirect domain knowledge like the number of initial facts and the number of possible values for variables. From this information we can estimate the probability that facts are derived multiple times. An important valuation factor for a proof strategy is whether these duplicates are eliminated. For systematic analysis we distinguish between in costs and out costs of a rule. The out costs correspond to the number of calls of a rule. In costs are the costs for proving the premises of a clause. Then we show how the selection of a proof method for one rule influences the effort of other rules. Finally we discuss problems of estimating costs for recursive rules and propose a solution for a restricted case.