Bücher von Rudi Studer

Intelligent information processing has become one of the key issues on our way to a knowledge society. Recent research has led to exciting advances in technologies that will shape our future. Knowledge-based systems have matured to provide services in a well-founded ontological framework, and intelligent knowledge management can be supported by tools that are ready for practical applications. Intelligent Information Processing presents new research with special emphasis on knowledge-based system architecture and intelligent information management. The following topics are addressed: -Agent-based Computing; -Semantic Web and Learning; -Ontology Management; -Semantic Web Architecture; -Knowledge-engineering Frameworks; -Knowledge-system Structure; -Data Mining; -Methods and Tools for Identifying Communities of Practice; and -Implementing Problem Solvers. This volume comprises the proceedings of the Conference on Intelligent Information Processing (IIP 2002) held in conjunction with the 17th World Computer Congress, which was sponsored by the International Federation of Information Processing (IFIP) and which convened in Montral, Qubec, Canada, in August 2002. The conference represented a major activity of IFIP Technical Committee 12 (Artificial Intelligence), Working Group 12.5, which is dedicated to `knowledge-oriented development of applications'. This Working Group brings together an international community of scientists concerned with the use of knowledge-based techniques in the engineering of real-world software systems.

Past, Present, and Future of Knowledge Acquisition This book contains the proceedings of the 11th European Workshop on Kno- edge Acquisition, Modeling, and Management (EKAW '99), held at Dagstuhl Castle (Germany) in May of 1999. This continuity and the high number of s- missions re?ect the mature status of the knowledge acquisition community. Knowledge Acquisition started as an attempt to solve the main bottleneck in developing expert systems (now called knowledge-based systems): Acquiring knowledgefromahumanexpert. Variousmethodsandtoolshavebeendeveloped to improve this process. These approaches signi?cantly reduced the cost of - veloping knowledge-based systems. However, these systems often only partially ful?lled the taskthey weredevelopedfor andmaintenanceremainedanunsolved problem. This required a paradigm shift that views the development process of knowledge-based systems as a modeling activity. Instead of simply transf- ring human knowledge into machine-readable code, building a knowledge-based system is now viewed as a modeling activity. A so-called knowledge model is constructed in interaction with users and experts. This model need not nec- sarily re?ect the already available human expertise. Instead it should provide a knowledgelevelcharacterizationof the knowledgethat is requiredby the system to solve the application task. Economy and quality in system development and maintainability are achieved by reusable problem-solving methods and onto- gies. The former describe the reasoning process of the knowledge-based system (i. e. , the algorithms it uses) and the latter describe the knowledge structures it uses (i. e. , the data structures). Both abstract from speci?c application and domain speci?c circumstances to enable knowledge reuse.