Syntax-Semantic Mapping for General Intelligence: Language Comprehension as Hypergraph Homomorphism, Language Generation as Constraint Satisfaction

A new approach to translating between natural language expressions and hypergraph-based semantic knowledge representations is proposed. Language comprehension is formulated in terms of homomorphisms mapping syntactic parse trees into semantic hypergraphs, and language generation as constraint satisfaction based on constraints derived via applying the inverse relations of these homomorphisms. This provides an elegant approach to implementing semantically savvy NLP systems, and also to thinking about the feedbacks between syntactic and semantic processing that are the crux of generally intelligent NLP. A prototype of the approach created using the link parser and the OpenCog Atom semantic representation is described, and initial results presented. Routes to extending this prototype into something useful for aiding generally intelligent dialogue systems are discussed.