ERLKG : Entity Representation Learning and Knowledge Graph based association analysis of COVID -19 through mining of unstructured biomedical corpora (2020)

Abstract: We introduce a generic, human-out-of-the-loop pipeline, ERLKG, to perform rapid association analysis of any biomedical entity with other existing entities from a corpora of the same domain. Our pipeline consists of a Knowledge Graph (KG) created from the Open Source CORD-19 dataset by fully automating the procedure of information extraction using SciBERT. The best latent entity representations are then found by benchnmarking different KG embedding techniques on the task of link prediction using a Graph Convolution Network Auto Encoder (GCN-AE). We demonstrate the utility of ERLKG with respect to COVID-19 through multiple qualitative evaluations. Due to the lack of a gold standard, we propose a relatively large intrinsic evaluation dataset for COVID-19 and use it for validating the top two performing KG embedding techniques. We find TransD to be the best performing KG embedding technique with Pearson and Spearman correlation scores of 0.4348 and 0.4570 respectively. We demonstrate that a considerable number of ERLKG’s top protein, chemical and disease predictions are currently in consideration for COVID-19 related research.

## introduction covid-19 is a global epidemic with a considerable fatality rate and a high transmission rate, affecting millions of people world-wide since its outbreak. 1 the search for treatments and possible cures for the novel coronavirus @xcite has led to an exponential increase in scientific publications, but the challenge lies in effectively processing, integrating and leveraging related sources of information. rapid and effective utilization of literature during times of pandemic such as covid-19 is of utmost importance in combating the disease. in this paper, we introduce a fully automated generic pipeline consisting of an information extraction (ie) system followed by knowledge graph construction. the ie module uses scibert @xcite for performing named entity recognition (ner) and relationship extraction (re). the entire entity extraction procedure is fully automated and no human expertise is used. the major goal is to ensure rapid access of relevant data through a structured representation of free text articles. following this, we focus on the task of association analysis of essential biomedical entities, namely, proteins, diseases and, chemicals. such entities are well explored in existing literature and an analysis of their relatedness to covid-19 is provided by leveraging the cord-19 open research dataset @xcite . this can assist the physicians to accelerate knowledge discovery and provide support for clinical decision making. the dataset and related resources of this paper are made public 2 2 . due to a lack of gold standard information, we perform extensive qualitative evaluations in order to show that our system does not suffer from redundancy or bias. these evaluations include performance on a link prediction task and intrinsic evaluation. for the former, kg embeddings along with graph adjacency matrix are fed to a gcn-ae @xcite model to perform link prediction. average precision (ap) and roc scores were used to benchmark different kg embeddings on the generated knowledge graph. for the intrinsic evaluation, we propose a new dataset that has been developed with the help of three physicians and benchmark our embeddings against it. finally, based on cosine similarity score, the best representation was used to predict top chemicals, proteins and diseases related to covid-19. the contributions of our approach are as follows : ## related work we mostly focus on recent works centered around the cord-19 dataset by discussing about the techniques used for ie and kg generation. ## erlkg in this section we discuss about the entire pipeline and its various components. figure 1 depicts the pipeline which consists of the following modules : preprocessing, named entity recognition (ner), relation extraction (re) and knowledge graph (kg) construction. the rest part of the figure 1 depicts the evaluation strategies adopted for a reliable association analysis of various chemical, protein and drug entities from cord-19 corpus with re-spect to covid-19. ## discussion we exploit the contextual evidence from cord-19 corpus in finding entities and relations. this is followed by kg construction for determining the relatedness between any biomedical entity with respect to covid-19. a simple co-occurrence ma- we face the challenge of an absence of ground truth data for cord-19 corpus. thus, we conduct extensive qualitative evaluations and in the process, introduce two gold-standard, annotated entity lists, cov19 25, and cov19 729. cov19 25 consists of 25 entities predicted by the top two embedding techniques, transd and rotate while cov19 729 consists of 729 entities sampled from the processed cord-19 dataset. the ratings were based on an entity's relatedness to covid-19. from the correlation scores 3 of our intrinsic evaluation, we observe that our model could provide considerable insight in predicting important associations with respect to covid-19. transd has the highest pearson and spearman score on the cov19 729 and cov19 25 datasets. hence, we use transd as the final embedding generation method for erlkg. figures 2 , 3 and 4 show the top related proteins, chemicals and diseases that erlkg, using transd embedding, predicted with respect to covid-19. without using any external knowledge resources, our pipeline predicts various chemicals, proteins and diseases that are highly related with covid-19. these predicted entities could help the biomedical community to get a better understanding of covid-19. a few top chemicals like mitoxantrone @xcite , carfilzomib @xcite , flutamide @xcite , lopinavir and ritonavir @xcite are being considered as a potential cure for the virus. from the predicted proteins list, entities like parp1 @xcite , spike protein @xcite , lactate dehydrogenase @xcite and nsp1 @xcite have direct relevance with covid-19. entities like ventricular tachycardia (vt) @xcite , myasthenic @xcite ) crisis, acute respiratory syndrome @xcite , ards (respiratory distress syndrome) (marini and gattinoni, 2020) and thrombocytopenia @xcite ) are a few diseases that are very likely to occur in patients suffering from covid-19. ## conclusion and future work we propose erlkg, a generic pipeline, for association analysis with respect to a given entity from an unstructured dataset. the part of the pipeline integrating ie and kg construction keeps humanout-of-the-loop. in order to learn the latent repre-sentation of the formed kg, we first benchmark various types of kg embedding techniques on the task of link prediction. according to our experiments we find transd and rotate producing a comparable performance. in this work our approach is evaluated only on cord-19 dataset and no additional resources have been employed. however, due to the lack of gold standard data we introduce cov19 729, which is a list of extracted named entities from our pipeline selected randomly and given to physicians for assigning association scores with respect to covid-19. owing to random selection most of the entities listed with greater association scores by transd and rotate were found to be missing in cov19 729 hence another set was given to physicians from the top entites which we call cov19 25. finally transd is used as our best kg embedding technique to predict top entities that are closely associated to covid-19 from cord-19 corpus. as a future scope, we plan to implement a normalization and abbreviation expansion module after the detection of entities. the study of these top predicted entities, by the domain experts, can help them understand the different types of associations and relationships they exhibit with respect to covid-19.