Distributed Knowledge Networks (DKN) provide some of the key enabling technologies for translating recent advances in automated data acquisition, digital storage, computers and communications into fundamental advances in organizational decision support, data analysis, and related applications. DKN include computational tools for accessing, organizing, transforming, and analyzing the contents of heterogeneous, distributed data and knowledge sources and for distributed problem solving and decision making under tight time, resource, and performance constraints. This paper presents an overview of the DKN project in the Iowa State University Artificial Intelligence Laboratory. I. Introduction Advanced scientific research (e.g., the genome project), military applications (e.g., intelligence data handling, situation assessment, command and control) , law enforcement (e.g., terrorism prevention), crisis management, design and manufacturing systems, and medical information infrastructure, pow...

Scientific discovery in data rich domains (e.g., biological sciences, atmospheric sciences) presents several challenges in information extraction and knowledge acquisition from heterogeneous, distributed, autonomously operated, dynamic data sources. This paper describes these problems and outlines the key elements of algorithmic and systems solutions for computer assisted scientific discovery in such domains. These include: ontology-assisted approaches to customizable data integration and information extraction from heterogeneous, distributed data sources; distributed data mining algorithms for knowledge acquisition from large, distributed data sets which obviate the need for transmitting large volumes of data across the network; ontology-driven approaches to exploratory data analysis from alternative ontological perspectives; and modular and extensible agent-based implementations of the algorithms within a platform-independent agent infrastructure. Prototype implementations of ...