T-KAER: Transparency-enhanced Knowledge-Augmented Entity Resolution Framework

TL;DR

T-KAER enhances transparency in entity resolution by documenting the process and semantic information augmented, enabling better error analysis and understanding of how external knowledge influences model predictions.

Contribution

This paper introduces T-KAER, a framework that improves transparency in knowledge-augmented entity resolution by systematically documenting the process and semantic information involved.

Findings

T-KAER effectively documents entity resolution processes.

It enables detailed error analysis through transparency logs.

Demonstrates how augmented knowledge impacts predictions.

Abstract

Entity resolution (ER) is the process of determining whether two representations refer to the same real-world entity and plays a crucial role in data curation and data cleaning. Recent studies have introduced the KAER framework, aiming to improve pre-trained language models by augmenting external knowledge. However, identifying and documenting the external knowledge that is being augmented and understanding its contribution to the model's predictions have received little to no attention in the research community. This paper addresses this gap by introducing T-KAER, the Transparency-enhanced Knowledge-Augmented Entity Resolution framework. To enhance transparency, three Transparency-related Questions (T-Qs) have been proposed: T-Q(1): What is the experimental process for matching results based on data inputs? T-Q(2): Which semantic information does KAER augment in the raw data inputs? T-Q(3): Which semantic information of the augmented data inputs influences the predictions? To address the T-Qs, T-KAER is designed to improve transparency by documenting the entity resolution processes in log files. In experiments, a citation dataset is used to demonstrate the transparency components of T-KAER. This demonstration showcases how T-KAER facilitates error analysis from both quantitative and qualitative perspectives, providing evidence on "what" semantic information is augmented and "why" the augmented knowledge influences predictions differently.