Information in all forms is increasingly available, but using it effectively requires a range of technologies for representing, manipulating, and reasoning with information. These technologies comprise knowledge capture, the extraction of useful knowledge from vast and diverse sources of information and raw data. Driven by the demands for knowledge-based applications, and the unprecedented availability of information on the Internet, the study of knowledge capture has a renewed importance.
Although there has been considerable work in the area of knowledge capture, activities have been distributed across several distinct research communities, principally knowledge engineering, machine learning, and natural-language processing. However, other fields study knowledge capture, too. For example, in planning and process management, mixed-initiative systems acquire knowledge about a user's goals by taking commands or accepting advice regarding a task. In addition, recent research with the Semantic Web includes work that tries to capture the knowledge associated with appropriately annotated web pages. All of these approaches are related in that they acquire information and organize it in knowledge structures that can be used for reasoning. They are complementary in that they use different techniques and approaches to capture different forms of knowledge.
K-CAP 2003 will provide a forum in which to bring together disparate research communities whose members are interested in efficiently capturing knowledge from a variety of sources and in creating representations that can be useful for reasoning. We solicit high-quality research papers for publication and presentation at our conference. Our aim is to promote multidisciplinary research that could lead to a new generation of tools and methodologies for knowledge capture.
Topics of interest include, but are not limited to: