  In this work, we are interested in automated methods for knowledge graph
creation (KGC) from input text. Progress on large language models (LLMs) has
prompted a series of recent works applying them to KGC, e.g., via zero/few-shot
prompting. Despite successes on small domain-specific datasets, these models
face difficulties scaling up to text common in many real-world applications. A
principal issue is that in prior methods, the KG schema has to be included in
the LLM prompt to generate valid triplets; larger and more complex schema
easily exceed the LLMs' context window length. To address this problem, we
propose a three-phase framework named Extract-Define-Canonicalize (EDC): open
information extraction followed by schema definition and post-hoc
canonicalization. EDC is flexible in that it can be applied to settings where a
pre-defined target schema is available and when it is not; in the latter case,
it constructs a schema automatically and applies self-canonicalization. To
further improve performance, we introduce a trained component that retrieves
schema elements relevant to the input text; this improves the LLMs' extraction
performance in a retrieval-augmented generation-like manner. We demonstrate on
three KGC benchmarks that EDC is able to extract high-quality triplets without
any parameter tuning and with significantly larger schemas compared to prior
works.
