Acquiring and Using World Knowledge using a Restricted Subset of English

TLDR

AI applications need world knowledge, yet building inference‑capable knowledge bases is a major challenge because formal logic is too complex for users and unconstrained natural language is too hard for machines. This paper pursues an intermediate approach of authoring knowledge in a restricted subset of natural language, describing CPL and its use for reasoning while discussing its strengths and weaknesses. The authors developed CPL, an interpreter and reasoner, encoded roughly 1,000 commonsense rules, and experimentally used the knowledge base for semantic retrieval of video clips from CPL captions. They claim this restricted natural language approach strikes a sweet spot, being both human‑usable and machine‑understandable.

Abstract

Many AI applications require a base of world knowledge to support reasoning. However, construction of such inferencecapable knowledge bases, even if constrained in coverage, 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.