MapperGPT: Large Language Models for Linking and Mapping Entities
Nicolas Matentzoglu J. Harry Caufield Harshad B. Hegde Justin T. Reese Sierra Moxon Hyeongsik Kim Nomi L. Harris Melissa A Haendel Christopher J. Mungall
Aligning terminological resources, including ontologies, controlled
vocabularies, taxonomies, and value sets is a critical part of data integration
in many domains such as healthcare, chemistry, and biomedical research. Entity
mapping is the process of determining correspondences between entities across
these resources, such as gene identifiers, disease concepts, or chemical entity
identifiers. Many tools have been developed to compute such mappings based on
common structural features and lexical information such as labels and synonyms.
Lexical approaches in particular often provide very high recall, but low
precision, due to lexical ambiguity. As a consequence of this, mapping efforts
often resort to a labor intensive manual mapping refinement through a human
curator.
  Large Language Models (LLMs), such as the ones employed by ChatGPT, have
generalizable abilities to perform a wide range of tasks, including
question-answering and information extraction. Here we present MapperGPT, an
approach that uses LLMs to review and refine mapping relationships as a
post-processing step, in concert with existing high-recall methods that are
based on lexical and structural heuristics.
  We evaluated MapperGPT on a series of alignment tasks from different domains,
including anatomy, developmental biology, and renal diseases. We devised a
collection of tasks that are designed to be particularly challenging for
lexical methods. We show that when used in combination with high-recall
methods, MapperGPT can provide a substantial improvement in accuracy, beating
state-of-the-art (SOTA) methods such as LogMap.