activity
20122023
most citedSupervised Contrastive Learning for Product Matching

37 citations · 77 across the 9 of their papers we have counts for

collaborators
Showing 2023Show all

6 papers · 1 filter

cs.CL2023★ 9 cited

Entity Matching using Large Language Models

Ralph Peeters, Aaron Steiner, Christian Bizer

Entity matching is the task of deciding whether two entity descriptions refer to the same real-world entity. Entity matching is a central step in most data integration pipelines. M…

cs.CL2023★ 7 cited

Column Type Annotation using ChatGPT

Keti Korini, Christian Bizer

Column type annotation is the task of annotating the columns of a relational table with the semantic type of the values contained in each column. Column type annotation is an impor…

cs.CL2023★ 10 cited

Product Information Extraction using ChatGPT

Alexander Brinkmann, Roee Shraga, Reng Chiz Der +1

Structured product data in the form of attribute/value pairs is the foundation of many e-commerce applications such as faceted product search, product comparison, and product recom…

cs.CL2023★ 3 cited

Using ChatGPT for Entity Matching

Ralph Peeters, Christian Bizer

Entity Matching is the task of deciding if two entity descriptions refer to the same real-world entity. State-of-the-art entity matching methods often rely on fine-tuning Transform…

cs.DB2023

SC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines

Alexander Brinkmann, Roee Shraga, Christian Bizer

The goal of entity resolution is to identify records in multiple datasets that represent the same real-world entity. However, comparing all records across datasets can be computati…

cs.LG2023★ 6 cited

WDC Products: A Multi-Dimensional Entity Matching Benchmark

Ralph Peeters, Reng Chiz Der, Christian Bizer

The difficulty of an entity matching task depends on a combination of multiple factors such as the amount of corner-case pairs, the fraction of entities in the test set that have n…