C ov R elex- SE : Adding Semantic Information for Relation Search via Sequence Embedding (2023)

Abstract: In recent years, COVID-19 has impacted all aspects of human life. As a result, numerous publications relating to this disease have been issued. Due to the massive volume of publications, some retrieval systems have been developed to provide researchers with useful information. In these systems, lexical searching methods are widely used, which raises many issues related to acronyms, synonyms, and rare keywords. In this paper, we present a hybrid relation retrieval system, CovRelex-SE, based on embeddings to provide high-quality search results. Our system can be accessed through the following URL: https://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex-se/

## introduction scientific information related to the coronavirus disease has received a lot of attention in recent years. the number of covid-19-relevant publications is increasing daily. in the record of cord-19 dataset @xcite , there are more than 900k papers introduced by march 31 st , 2022. the huge number of documents demonstrates the importance of retrieval systems for providing researchers with informative knowledge. a relation is an object that consists of three components (arg1, rel, arg2), where arg1, and arg2 are noun phrases that may contain biomedical entities and rel is an expression describing the re-lation between arg1 and arg2. a query is made up of partial information on a relation, which includes keywords regarding these components. ideally, a relation retrieval system should return all relevant relations with the corresponding papers, which can be used to answer two different types of questions: single-hop and multi-hop. regarding single-hop questions, such as "covid-19 disables which things?", we can input the query ("covid-19", "disable", any-arg2), and then extract the answer by using the returned results of arg2. on the other hand, we can combine two queries: ("covid-19", "cause", disease), and (chemical, "treat", disease) to answer the multi-hop question "what are chemical that can treat some diseases caused by covid-19?". by that, the answer can be extracted by using the returned results at the position of chemical. in this paper, we propose covrelex-se, a hybrid retrieval system to search the relations that effectively tackles the issues raised by the lexical approach in the covrelex system @xcite . instead of searching relations using lexical methods, covrelex-se ranks their scores by utilizing the combination of lexical scores (based on the elasticsearch foot_0 engine) and semantic scores (based on cord19-bert embeddings). in summary, our contributions in this paper are as follows: (i) a novel approach to ranking covid-19relevant relations, which combines the effectiveness of lexical approach and vector representation approach; ## related work due to the covid-19 outbreak, it is vital to collect crucial information from a huge number of covid-19-related publications. zhang et al. (2020 ) created covidex, a search engine that allows users to query the covid-19 open research dataset and access inside information. esteva et al. (2020) introduced co-search, a semantic search engine composed of a retriever and a ranker that was built to handle complex queries throughout the covid-19 papers. additionally, wang et al. ( 2020b ) created the ev-idenceminer web-based solution. given a query as a natural language statement, evidenceminer retrieves textual evidence at the sentence level from the cord-19 corpus for life sciences. more recently, @xcite present an information retrieval system that uses latent information to select relevant works related to specific concepts. @xcite develop a question answering system that receives a set of questions asked by experts about the disease covid-19 and sars-cov-2 virus, and provides a ranked list of expert-level answers to each question. conceptually, the most similar to our work, cov-relex @xcite , is a retrieval system for scientific publications that target entities and relations via relation extraction from covid-19 scientific papers. however, there is still a lack of systems that automatically extract the diverse relations through papers and obtain the results using semantic information, especially given the rapid publication of covid-19 papers. this issue motivates us to create the covrelex-se system. from the raw text of document abstracts, we extract relations and recognize biomedical entities inside the extracted relations. for each relation, arg1, arg2, and rel are converted into vectors by using cord19-bert. three faiss @xcite indices are then trained using all of the embedding vectors. at the query time, the user input a query, which will be converted to embedding vectors. following that, the faiss indices will be looked up for the most similar relations according to the query and return semantic scores. the elasticsearch engine will also look up the query and utilize the bm25 algorithm @xcite to calculate lexical scores. the system then combines the lexical scores and semantic scores as final scores for relations. finally, covrelex-se returns the top-ranked triplets after filtering query entities using the elasticsearch engine. ## threats to validity there are two main threats to validity in this study, which are described as follows. ## conclusions in this paper, we present covrelex-se, a novel covid-19 retrieval system for ranking relations in the cord-19 corpus. the score of a relation is calculated based on semantic and lexical scores. the semantic score is determined using the embeddings from cord19-bert, whereas the lexical score is computed using the elasticsearch engine. in order to evaluate the effectiveness of covrelex-se, we conducted an evaluation task. the experimental results show that our system outperforms the