Abstract:
Many AI applications require a base of world knowledge to support reasoning. However, construction of such inference-capable knowledge bases, even if constrained in scope, remains one of the major challenges of AI. Authoring knowledge 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. However, there is an intermediate position, which we are pursuing, namely authoring knowledge in a restricted subset of natural 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 developed such a language (called CPL, Computer-Processable Language), an interpreter, and a reasoner, and have used them to encode approximately 1000 "commonsense" rules (a mixture 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 reasoning, and discuss the strengths and weaknesses of restricted natural language as a the basis for knowledge representation.