# FlexER: Flexible Entity Resolution for Multiple Intents

**Authors:** Bar Genossar, Roee Shraga, Avigdor Gal  
**arXiv:** [2209.07569](https://arxiv.org/abs/2209.07569)  
**Date:** 2022-08-23  
**Summary page:** https://www.summarizepaper.com/en/paper/2209.07569v1/

## Key points

- Entity resolution is crucial for data cleaning and integration, identifying data records corresponding to the same real-world entity.
- Traditional approaches treat entity resolution as universal, aiming to distinguish between matching and non-matching records based on a single interpretation of entities.
- In practical scenarios, downstream applications may require different interpretations of entities, leading to the concept of multiple intents entity resolution (MIER).
- FlexER is a solution that addresses MIER by utilizing intent-based representations in a multiplex graph input into a graph neural network (GNN).
- FlexER treats the problem as a multi-label classification issue and has shown effectiveness in solving MIER through large-scale empirical evaluations.

## Summary

Entity resolution is a critical aspect of data cleaning and integration, focused on identifying data records that correspond to the same real-world entity. Traditional approaches to entity resolution typically treat the task as universal, assuming a single interpretation of a real-world entity. They aim to distinguish between matching and non-matching records based on this singular interpretation. However, in practical scenarios where entity resolution is part of broader data projects, downstream applications may require varying interpretations of real-world entities to cater to different user needs. This complexity led to the introduction of the concept of multiple intents entity resolution (MIER), which extends the traditional single intent entity resolution task. In response to this challenge, a solution called FlexER has been proposed. FlexER leverages contemporary techniques used in universal entity resolution tasks to effectively address multiple intents entity resolution. It approaches the problem as a multi-label classification issue and utilizes intent-based representations of tuple pairs through a multiplex graph representation input into a graph neural network (GNN). FlexER not only learns intent representations but also enhances outcomes for multiple resolution problems. Through large-scale empirical evaluations that introduce a new benchmark alongside two established benchmarks, FlexER has demonstrated its effectiveness in solving the MIER problem while surpassing existing state-of-the-art methods for universal entity resolution. Authored by Bar Genossar from Technion - Israel Institute of Technology, Roee Shraga from Northeastern University, and Avigdor Gal from Technion - Israel Institute of Technology, the research paper titled "FlexER: Flexible Entity Resolution for Multiple Intents" presents an innovative approach to addressing the challenges posed by varying interpretations in real-world entity resolution tasks.