C ov R elex: A COVID -19 Retrieval System with Relation Extraction (2021)

Abstract: This paper presents CovRelex, a scientific paper retrieval system targeting entities and relations via relation extraction on COVID-19 scientific papers. This work aims at building a system supporting users efficiently in acquiring knowledge across a huge number of COVID-19 scientific papers published rapidly. Our system can be accessed via https://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex/ .

## introduction this work aims at facilitating knowledge acquisition from a huge number of covid-19 scientific papers. due to the covid-19 outbreak, researchers have been focusing on studying the virus and publishing a huge number of papers rapidly. according to the estimation of @xcite , 23,634 unique documents were published in just 6 months between january 1 st and june 30 th , 2020. in the records of the covid-19 open research dataset (cord-19) challenge foot_0 , the number of collected papers about covid-19, sars-cov-2 and related coronaviruses is more than 400k by january 9 th , 2021. the rapid speed of new publication and the huge number of related papers challenges specialists to seek knowledge by connecting findings across papers efficiently and timely. when focusing on knowledge acquisition of biomedical entities, several questions can be asked regarding the entities and their relations: such questions can be answered by our system. relation extraction entity recognition relation index relation clustering relation scoring graph construction cord-19 reverb, ollie, clausie, relink, openie scispacy disease, protein, chemical, dna, … pmi-derived informativeness cluster hierarchy w/ finch & bert linking entities across papers relations graph figure 1: system overview. arg 1 rel arg 2 [mers-cov] ggp include [fever] disease , [chills/rigors] disease , [headache] disease , non-productive [cough] disease [mers-cov] ggp is responsible for causing lower [respiratory infections] disease with [fever] disease and [cough] disease figure 2: an example of relations extracted from covid-19 papers. variants and other associated entities such as diseases and chemicals/drugs. wei et al. (2019) presented a web service pubtator central (ptc) that provides automated bioconcept annotations in full text biomedical articles, in which bioconcepts are extracted from state-of-the-art text mining systems. due to the covid-19 outbreak, it is essential to grasp valuable knowledge from a huge number of covid-19-related papers for dealing with the pandemic effectively. sohrab et al. (2020) introduced the bennerd system that detects named entities in biomedical text and links them to the unified medical language system (umls) to facilitate the covid-19 research. hope et al. (2020) created a dataset annotated for mechanism relations and trained an information extraction model on this data. then, they used the model to extract a knowledge base (kb) of mechanism and effect relations from papers relating to covid-19. zhang et al. (2020) built covidex, a search infrastructure that provides information access to the covid-19 open research dataset such as answering questions. esteva et al. (2020) also presented co-search, a retriever-ranker semantic search engine designed to handle complex queries over the covid-19 literature. wang et al. (2020) created the evidenceminer web-based system. given a query as a natural language statement, evidenceminer automatically retrieves sentence-level textual evidence from the cord-19 corpus. clearly, previous works made a great effort to acquire useful knowledge from the covid-19 literature, such as recognizing biomedical entities @xcite , extracting mechanism relations between entities @xcite , or retrieving relevant text segments based on the user query @xcite . however, there is still a lack of a system that has the ability to automatically detect both entities with various types and their diverse relations through papers, especially when covid-19 papers are published rapidly. this motivates us to build the covrelex system, which aims to exploit such information. ## conclusion we have presented our covid-19 scientific paper retrieval system which focuses on analysing entities and their relations. the system is empowered with several relation extraction and entity recognition methods. the system supports users in acquiring knowledge efficiently across a huge number of covid-19 scientific papers published rapidly. there, however, exist extremely challenging problems to tackle for making the system more practical: dealing with the newly created and unknown data, solving the performance gap when utilizing present methods, and do these in the nick of time of fighting with pandemics.