An Explanatory Query-Based Framework for Exploring Academic Expertise
Oana Cocarascu, Andrew McLean, Paul French, Francesca Toni

TL;DR
This paper introduces a query-based system that automatically identifies academic researchers' expertise from publication abstracts, aiding collaboration and resource matching with explanations, evaluated on university data.
Contribution
It presents a novel, efficient framework that uses natural language queries and domain knowledge to find relevant researchers, outperforming neural network baselines.
Findings
Effective in matching researchers to queries
Outperforms neural network baselines
Works across different academic domains
Abstract
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…
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Taxonomy
TopicsExpert finding and Q&A systems · Topic Modeling · Information Retrieval and Search Behavior
