R.H.: Acquiring and Using World Knowledge Using a Restricted Subset of English

Many AI applications require a base of world knowledge to support reasoning. However, construction of such inference-capable knowledge bases, even if constrained in coverage, re-mains one of the major challenges of AI. Authoring knowl-edge in formal logic is too complex a task for many users, while knowledge authored in unconstrained natural language is generally too difficult for computers to understand. How-ever, there is an intermediate position, which we are pursuing, namely authoring knowledge in a restricted subset of natu-ral language. Our claim is that this approach hits a “sweet spot ” between the former two extremes, being both usable by humans and understandable by machines. We have de-veloped such a language (called CPL, Computer-Processable Language), an interpreter, and a reasoner, and have used them to encode approximately 1000 “commonsense ” rules (a mix-ture of general and domain-specific). The knowledge base is being used experimentally for semantic retrieval of video clips based on their captions, also expressed in CPL. In this paper, we describe CPL, its interpretation, and its use for rea-soning, and discuss the strengths and weaknesses of restricted natural language as a the basis for knowledge representation