CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata

TL;DR

CHOLAN is a modular, transformer-based entity linking system that leverages external context from Wikipedia and Wikidata, outperforming existing methods on multiple benchmark datasets.

Contribution

It introduces a novel modular pipeline with external context integration for end-to-end entity linking, improving accuracy over state-of-the-art approaches.

Findings

Outperforms state-of-the-art on CoNLL-AIDA, MSNBC, AQUAINT, ACE2004, T-REx datasets.

Utilizes external Wikipedia and Wikidata contexts for improved disambiguation.

Employs a two-transformer pipeline for mention detection and entity classification.

Abstract

In this paper, we propose CHOLAN, a modular approach to target end-to-end entity linking (EL) over knowledge bases. CHOLAN consists of a pipeline of two transformer-based models integrated sequentially to accomplish the EL task. The first transformer model identifies surface forms (entity mentions) in a given text. For each mention, a second transformer model is employed to classify the target entity among a predefined candidates list. The latter transformer is fed by an enriched context captured from the sentence (i.e. local context), and entity description gained from Wikipedia. Such external contexts have not been used in the state of the art EL approaches. Our empirical study was conducted on two well-known knowledge bases (i.e., Wikidata and Wikipedia). The empirical results suggest that CHOLAN outperforms state-of-the-art approaches on standard datasets such as CoNLL-AIDA, MSNBC, AQUAINT, ACE2004, and T-REx.