Searching COVID-19 Clinical Research Using Graph Queries: Algorithm Development and Validation

TL;DR

This paper presents a graph-based search engine for COVID-19 literature that uses concept co-occurrence networks and sophisticated query matching to improve literature exploration and evidence retrieval.

Contribution

It introduces a novel graph query engine leveraging concept co-occurrence networks and partial matching for efficient COVID-19 literature search.

Findings

Built a large co-occurrence network with over 128,000 entities

Developed a user-friendly interface for graph query formulation

Enabled detailed publication explanations based on query matches

Abstract

Objective: This study aims to consider small graphs of concepts and exploit them for expressing graph searches over existing COVID-19-related literature, leveraging the increasing use of graphs to represent and query scientific knowledge and providing a user-friendly search and exploration experience. Methods: We considered the COVID-19 Open Research Dataset corpus and summarized its content by annotating the publications' abstracts using terms selected from the UMLS and the Ontology of Coronavirus Infectious Disease. Then, we built a co-occurrence network that includes all relevant concepts mentioned in the corpus, establishing connections when their mutual information is relevant. A sophisticated graph query engine was built to allow the identification of the best matches of graph queries on the network. It also supports partial matches and suggests potential query completions using shortest paths. Results: We built a large co-occurrence network, consisting of 128,249 entities and 47,198,965 relationships; the GRAPH-SEARCH interface allows users to explore the network by formulating or adapting graph queries; it produces a bibliography of publications, which are globally ranked; and each publication is further associated with the specific parts of the query that it explains, thereby allowing the user to understand each aspect of the matching. Conclusions: Our approach supports the process of query formulation and evidence search upon a large text corpus; it can be reapplied to any scientific domain where documents corpora and curated ontologies are made available.