Although there has been considerable work in this topic, it has been distributed across several distinct research communities. In machine learning, learning apprentices acquire knowledge by non-intrusively watching a user perform a task. In the human-computer interfaces community, programming-by-demonstration systems learn to perform a task by watching a user demonstrate how to accomplish it. In knowledge engineering, modeling techniques and design principles have been proposed for knowledge-based systems, often exploiting commonly occurring domain-independent inference structures and ontologies. In planning and process management, mixed-initiative systems acquire knowledge about a user¹s goals by taking commands or advice regarding a task. In natural language processing, tools can process text and create representations of its knowledge content. 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.
The aim of this conference is to provide a forum to bring together these disparate research communities interested in efficiently capturing knowledge from a variety of sources into representations that can be (or can eventually be) useful for reasoning. This conference will foster synergistic work in knowledge-processing research that could result in a new generation of knowledge capture tools and techniques.
Topics of interest include but are not limited to: