Rapid Development of Large Knowledge Bases *

Abstract- This paper presents the Disciple-RKF methodology for rapid development of large knowledge bases which relies on importing ontological knowledge from existing knowledge repositories, on parallel development of separate knowledge bases by subject matter experts, and on the merging of these knowledge bases into a high performance integrated knowledge base. The paper discusses several issues related to ontology import and merging, and presents the results of a successful knowledge base development and integration experiment performed at the US Army War College

Ontologies for Learning Agents: Problems, Solutions and Directions

We are developing a general end-to-end approach, called Disciple, for building and using personal problem solving and learning agents. This approach raises complex challenges related to ontology specification, import, elicitation, learning, and merging, that we have explored to various degrees, as we are developing successive versions of Disciple. This paper presents some of these challenges, our current solutions and the future directions, that are relevant for building agents in general

Parallel Knowledge Base Development by Subject Matter Experts

Abstract. This paper presents an experiment of parallel knowledge base development by subject matter experts, performed as part of the DARPA’s Rapid Knowledge Formation Program. It introduces the Disciple-RKF development environment used in this experiment and proposes design guidelines for systems that support authoring of problem solving knowledge by subject matter experts. Finally, it compares Disciple-RKF with the other development environments from the same DARPA program, providing further support for the proposed guidelines.

1. DISCIPLE-RKF LEARNING AGENT

This paper presents Disciple-RKF, a learning agent shell that can be used by subject matter experts, with limited assistance from knowledge engineers, to develop knowledge-based agents incorporating their expertise