This paper presents an experimental pipeline and an annotation scheme for enriching text with semantic information by leveraging implicit information extracted from Abstract Meaning Representation (AMR). AMR, as an unanchored semantic representation language, encodes a wide range of information that can be useful for downstream NLP tasks. This implicit knowledge encompasses frames, semantic roles, numeric value labels, concept grounding to Wikipedia entries, and fine-grained relationship types. The extracted information can improve the performance of existing logic-based end-to-end question-answering pipelines that rely on deterministic rule-driven semantic parsers for text-to-logic conversion based on Universal Dependencies (UD).