Populating the Cyc Knowledge Base (KB) has been a manual process until very recently. However, there is currently enough knowledge in Cyc for it to be feasible to attempt to acquire additional knowledge autonomously. This paper describes a system that can collect and validate formally represented, fully-integrated knowledge from the Web or any other electronically available text corpus, about various entities of interest (e.g.
The Cyc project is predicated on the idea that effective machine learning depends on having a core of knowledge that provides a context for novel learned information - what is known informally as "common sense." Over the last twenty years, a sufficient core of common sense knowledge has been entered into Cyc to allow it to begin effectively and flexibly supporting its most important task: increasing its own store of world knowledge. In this paper, we present initial work on a method of using a combination of Cyc and the World Wide Web, accessed via Google, to assist in entering knowledge into Cyc. The long-term goal is automating the process of building a consistent, formalized representation of the world in the Cyc knowledge base via machine learning. We present preliminary results of this work and describe how we expect the knowledge acquisition process to become more accurate, faster, and more automated in the future.
This paper describes the TextLearner prototype, a knowledgeacquisition program that represents the culmination of the DARPA-IPTO-sponsored Reading Learning Comprehension seedling program, an effort to determine the feasibility of autonomous knowledge acquisition through the analysis of text. Built atop the Cyc Knowledge Base and implemented almost entirely in the formal representation language of CycL, TextLearner is an anomaly in the way of Natural Language Understanding programs. The system operates by generating an information-rich model of its target document, and uses that model to explore learning opportunities. In particular, TextLearner generates and evaluates hypotheses, not only about the content of the target document, but about how to interpret unfamiliar natural language constructions. This paper focuses on this second capability and describes four algorithms TextLearner uses to acquire rules for interpreting text.