Disambiguate Entity Matching through Relation Discovery with Large
  Language Models
Zezhou Huang
Entity matching is a critical challenge in data integration and cleaning,
central to tasks like fuzzy joins and deduplication. Traditional approaches
have focused on overcoming fuzzy term representations through methods such as
edit distance, Jaccard similarity, and more recently, embeddings and deep
neural networks, including advancements from large language models (LLMs) like
GPT. However, the core challenge in entity matching extends beyond term
fuzziness to the ambiguity in defining what constitutes a "match," especially
when integrating with external databases. This ambiguity arises due to varying
levels of detail and granularity among entities, complicating exact matches. We
propose a novel approach that shifts focus from purely identifying semantic
similarities to understanding and defining the "relations" between entities as
crucial for resolving ambiguities in matching. By predefining a set of
relations relevant to the task at hand, our method allows analysts to navigate
the spectrum of similarity more effectively, from exact matches to conceptually
related entities.