  A hallmark of intelligence is the ability to use a familiar domain to make
inferences about a less familiar domain, known as analogical reasoning. In this
article, we delve into the performance of Large Language Models (LLMs) in
dealing with progressively complex analogies expressed in unstructured text. We
discuss analogies at four distinct levels of complexity: lexical analogies,
syntactic analogies, semantic analogies, and pragmatic analogies. As the
analogies become more complex, they require increasingly extensive, diverse
knowledge beyond the textual content, unlikely to be found in the lexical
co-occurrence statistics that power LLMs. To address this, we discuss the
necessity of employing Neuro-symbolic AI techniques that combine statistical
and symbolic AI, informing the representation of unstructured text to highlight
and augment relevant content, provide abstraction and guide the mapping
process. Our knowledge-informed approach maintains the efficiency of LLMs while
preserving the ability to explain analogies for pedagogical applications.
