Disambiguate Entity Matching through Relation Discovery with Large Language Models

arXiv:2403.17344v1 Announce Type: cross
Abstract: 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 …

abstract arxiv challenge cleaning cs.cl cs.db data data integration deduplication discovery edit embeddings integration jaccard joins language language models large language large language models networks neural networks tasks through type