COVID -19: A Semantic-Based Pipeline for Recommending Biomedical Entities (2020)

Abstract: With the increasing number of publications about COVID-19, it is a challenge to extract personalized knowledge suitable for each researcher. This work aims to build a new semantic-based pipeline for recommending biomedical entities to scientific researchers. To this end, we developed a pipeline that creates an implicit feedback matrix based on Named Entity Recognition (NER) on a corpus of documents, using multidisciplinary ontologies for recognizing and linking the entities. Our hypothesis is that by using ontologies from different fields in the NER phase, we can improve the results for state-of-the-art collaborative-filtering recommender systems applied to the dataset created. The tests performed using the COVID-19 Open Research Dataset (CORD-19) dataset show that when using four ontologies, the results for precision@k, for example, reach the 80%, whereas when using only one ontology, the results for precision@k drops to 20%, for the same users. Furthermore, the use of multi-fields entities may help in the discovery of new items, even if the researchers do not have items from that field in their set of preferences.

## introduction the research literature is the main form of dissemination for scientific works, growing by the minute. platforms such as pubmed 1 , account for more than 30 million articles related to biomedical literature. the emergence of new topics of investigation with particular interest for modern society, such as @xcite , leads to an even faster increase in the publication rate. the scientific literature contains vast and essential information about biomedical entities engaged in covid-19 processes. however, it is 1 https://pubmed.ncbi.nlm.nih.gov/ difficult for the researchers to read all the papers and keep up with all the new topics suitable for their research. given the importance of covid-19 related topics, the allen institute for ai, in collaboration with the white house office of science and technology policy, the national library of medicine, the chan zuckerburg initiative, microsoft research, and kaggle, collected and released the first version of covid-19 open research dataset (cord-19) foot_0 @xcite . the main goal of this dataset is to help in the development of new tools for the extraction of relevant information in the fight of covid-19 disease. one of the main techniques applied to extract information from the research literature is named entity recognition (ner), followed by relation extraction (re). ner consists of recognizing entities mentioned in the text by identifying the offset of their first and last character. there is an extensive work done on biomedical ner, regarding all type of entities, such as chemicals @xcite and human phenotypes @xcite . re aims to identify a relation between entities mentioned in a given document or text window. regarding biomedical re, the research is focused not only on extracting but also on classifying the relationship between biomedical entities, ranging from phenotype-gene relations @xcite to chemical-chemical interactions @xcite . recommender systems (rs) are tools which allow recommending items of interest to a user, based on the similarity between her/his preferences and the preferences of other users -collaborativefiltering (cf), or based on the similarity of the items this user already liked -content-based (cb). hybrid rs may be created to solve intrinsic challenges of the previous rs approaches, such as the cold start problem for new items in cf and new users in both cf and cb. cold start refers to a new user without any item rated or a new item without any rate by the users. the users' ratings may be explicit, for example, using a star classification system, or implicit, in which case the ratings are inferred from the user's interaction with the items, for example, by buying or seeing them. the implicit rating may be binary, for example, 1 if a user saw a movie, 0 if she/he did not see a movie, or it may be a different measure, for example, the duration that a user watches a video @xcite . rs are most frequently used for recommending items such as movies, books, or e-commerce products. rs approaches have been applied most recently to recommend the most appropriate research items for each researcher @xcite @xcite . these efforts consist mostly of developing recommendation datasets of implicit feedback for various scientific fields, such as chemistry and artificial intelligence, developed by mining the scientific literature. the goal of these datasets is to recommend scientific items for the research, for example, chemical compounds, based on their past interests and their peers' interests. this paper proposes a novel pipeline for extracting, relating and recommending scientific items from cord-19 dataset, using entities from various ontologies: gene ontology (go) 3 , disease ontology (do) 4 , human phenotype ontology (hp) 5 , and chemical entities of biological interest ontology (chebi) 6 . the use of ontologies for the ner phase allows the extraction of the entities from the text and the linking of the entities to a definition, avoiding the ambiguity of the terms. we selected these ontologies for their importance in the covid-19 disease. with these, we may find drugs, genes, phenotypes and diseases related to covid-19, which may guide the researchers in the discovery of new information for stopping the disease. the main contributions of this work are: the source code for this work is fully available at: https://github.com/lasigebiotm/ knowledge-extraction-from-cord-19 . ## related work despite the novelty of cord-19 dataset, the number of works being published using this dataset increases by the day. cord-19 is being used for developing tools in various fields. @xcite ) created a dataset for question and answering about covid-19. @xcite developed a pipeline for creating a dataset based on biomedical ner, for chemicals, diseases, genes and species, using taggerone and gnormplus tools. @xcite focused on a search engine for cord-19 based on neural networks, the neural covidex. @xcite ) created the tool evidenceminer, which allows a user to introduce a sentence in natural language and to retrieve an evidence for that statement. they applied the evidenceminer to cord-19. @xcite ) created the cord-ner dataset, a dataset with entities from 75 fields, including genes, chemicals, diseases, and specific entities related to covid-19, for example, coronaviruses, viral proteins, evolution, materials, substrates and immune responses. (rahdari et al.) is developing a personalized exploratory search system for covid-19, based on cord-19, the covex. according to the authors, the system allows the user to search for keywords, recommending other keywords and research articles. the recommended keywords are extracted only from the title and abstract, using bi-lstm-crf technique. not related to cord-19, other research works created recommendation datasets for scientific fields. @xcite ) created a recommendation dataset of implicit feedback for the field of artificial intelligence with the format @xmath0,topic,cardinality>. their work extracts topics related to artificial intelligence from articles and the cardinality is calculated according to the importance of the topic in the article. then, the dataset is used for recommending topics and articles. the topics are extracted from the research articles using text mining techniques based on the articles' token frequency. they do not use ner techniques. in @xcite , the authors developed a methodology called libretti to create recommendation datasets of implicit feedback for scientific fields, for recommending scientific entities, such as clusters of stars and chemicals compounds. the methodology consists of given a list of scientific entities, finding articles related to these entities, and extracting the authors. the dataset has the format @xmath1,entity,rating>. the ratings are the number of articles a unique author wrote about an entity. this work uses the chebi ontology to extract the list of entities for a dataset of chemical compounds; nevertheless, it is limited to this ontology and performs neither ner nor re. the objective of our pipeline is to create a tool for performing ner of multiple scientific fields in the cord-19 dataset, followed by re, and the creation of a recommendation dataset of implicit feedback based on libretti methodology, to recommend entities of different fields to the users/researchers. we hypothesize that using multiple ontologies in the creation of the recommendation dataset leads to an improvement in the performance of state-of-the-art cf algorithms, in particular when comparing with datasets with only one ontology. ## methodology and experiments the general workflow of the proposed pipeline is represented in figure 1 . the input of the pipeline is a dataset of research articles. first, we apply ner techniques for recognizing relevant entities in the text, using ontologies for linking the entities. second, we extract the relations between the entities (re). and third, we create an implicit feedback matrix and apply rs algorithms for recommending the recognized entities. the dataset used in this work is the covid-19 open research dataset (cord-19) @xcite , more concretely, the 2020-03-13 version. the total number of documents is 29,500. for this work, we used the commercial use subset of 9,000 full-text documents. ## conclusion given the growing number of publications, this work's goal was to develop a pipeline for extracting biomedical entities from scientific literature, finding the relations between them, and recommending entities of interest for a particular researcher. the second goal was to prove that by using ontologies from different science fields, cf rs would achieve better results when recommending ranked lists of entities to the users. we used as a case study the cord-19 dataset, which is a dataset in a field of high relevance for this era. using this dataset, we performed ner using four ontologies, chebi, do, hp, and go, creating an annotated dataset of 9k documents. we also curated 100 documents light-harvesting complex, core complex from this dataset, achieving positives results for precision, recall, and f1-score. the re phase is in the beginning. nevertheless, this is a first step for creating a full dataset of relations between the fields in study, which can then be used for generating a knowledge base for covid-19. we created a dataset with more than 3 million ratings, 45 thousand users, and 13 thousand items from four relevant scientific fields in the recommendation phase. we concluded that using items from several fields for the same users, the cf algorithm reached better results. for future work, we intend to increase the number of research documents, the number of documents manually annotated, and provide a better baseline. furthermore, the next step is to integrate the re dataset in the rs. it is also important to perform online tests for a better understanding of the relevance of recommended items. for such, an online recommendation platform will be developed.