The Dimensions of Context-Space

Contexts have historically been either ignored completely or else treated as black boxes, as indivisible atoms. About a decade ago, as part of our work on building the large Cyc ® knowledge base of human common sense and common knowledge, our group began to study and harness the internal structure of that “atom”. Each context was said to have assumptions and content; there was a theory of importing assertions across contexts; contexts were fully reified first-class terms in the CycL representation language; they were partially ordered by specialization to control visibility and access to content; and so on. That 1989-91 work turned out to be inadequate: it was too expensive to do nontrivial lifting (importing); to explicate the assumptions of each context; and to place each assertion/query into the proper context. Over the last few years, as the number of Cyc contexts grew into the thousands, we gained a better understanding of the problem – and a possible solution has emerged. There is a finer internal structure to a context than just those two parts, assumptions and content. There are a dozen mostly-independent dimensions along which contexts vary; conversely, each region of that 12-dimensional space implicitly defines a context. In effect that space is the space of assumptions, and each assertion can be thought to hold true in some region of that space.

on “Whatever Happened to AI?”

and occasionally bittersweet presentatio

Searching for common sense: Populating Cyc from the Web

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.

The Comprehensive Terrorism Knowledge Base in Cyc

This paper describes the comprehensive Terrorism Knowledge Base TM (TKB TM) which will ultimately contain all relevant knowledge about terrorist groups, their members, leaders, affiliations, etc., and full descriptions of specific terrorist events. Led by world-class experts in terrorism, knowledge enterers have, with simple tools, been building the TKB at the rate of up to 100 assertions per person-hour. The knowledge is stored in a manner suitable for computer understanding and reasoning. The TKB also utilizes its reasoning modules to integrate data and correlate observations, generate scenarios, answe