A Multilingual Entity Linking System for Wikipedia with a Machine-in-the-Loop Approach

TL;DR

This paper presents a multilingual entity linking system with a machine-in-the-loop approach to enhance Wikipedia's hyperlink coverage, especially in low-resource languages, by combining data-driven models with interactive editor feedback.

Contribution

It introduces a language-agnostic entity linking model integrated with an interactive interface for editors, improving link coverage and editor experience in Wikipedia.

Findings

Achieves over 80% precision in link recommendations.

Ensures at least 50% recall across six diverse languages.

Facilitates continuous evaluation through active editor feedback.

Abstract

Hyperlinks constitute the backbone of the Web; they enable user navigation, information discovery, content ranking, and many other crucial services on the Internet. In particular, hyperlinks found within Wikipedia allow the readers to navigate from one page to another to expand their knowledge on a given subject of interest or to discover a new one. However, despite Wikipedia editors' efforts to add and maintain its content, the distribution of links remains sparse in many language editions. This paper introduces a machine-in-the-loop entity linking system that can comply with community guidelines for adding a link and aims at increasing link coverage in new pages and wiki-projects with low-resources. To tackle these challenges, we build a context and language agnostic entity linking model that combines data collected from millions of anchors found across wiki-projects, as well as billions of users' reading sessions. We develop an interactive recommendation interface that proposes candidate links to editors who can confirm, reject, or adapt the recommendation with the overall aim of providing a more accessible editing experience for newcomers through structured tasks. Our system's design choices were made in collaboration with members of several language communities. When the system is implemented as part of Wikipedia, its usage by volunteer editors will help us build a continuous evaluation dataset with active feedback. Our experimental results show that our link recommender can achieve a precision above 80% while ensuring a recall of at least 50% across 6 languages covering different sizes, continents, and families.