  The success of research institutions heavily relies upon identifying the
right researchers "for the job": researchers may need to identify appropriate
collaborators, often from across disciplines; students may need to identify
suitable supervisors for projects of their interest; administrators may need to
match funding opportunities with relevant researchers, and so on. Usually,
finding potential collaborators in institutions is a time-consuming manual
search task prone to bias. In this paper, we propose a novel query-based
framework for searching, scoring, and exploring research expertise
automatically, based upon processing abstracts of academic publications. Given
user queries in natural language, our framework finds researchers with relevant
expertise, making use of domain-specific knowledge bases and word embeddings.
It also generates explanations for its recommendations. We evaluate our
framework with an institutional repository of papers from a leading university,
using, as baselines, artificial neural networks and transformer-based models
for a multilabel classification task to identify authors of publication
abstracts. We also assess the cross-domain effectiveness of our framework with
a (separate) research funding repository for the same institution. We show that
our simple method is effective in identifying matches, while satisfying
desirable properties and being efficient.
