  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.
