Exploration and Discovery of the COVID -19 Literature through Semantic Visualization (2021)

Abstract: We propose semantic visualization as a linguistic visual analytic method. It can enable exploration and discovery over large datasets of complex networks by exploiting the semantics of the relations in them. This involves extracting information, applying parameter reduction operations, building hierarchical data representation and designing visualization. We also present the accompanying COVID-SemViz a searchable and interactive visualization system for knowledge exploration of COVID-19 data to demonstrate the application of our proposed method. In the user studies, users found that semantic visualization-powered COVID-SemViz is helpful in terms of finding relevant information and discovering unknown associations.

## introduction covid-19 is the first global pandemic within a century. to facilitate the scientific and medical effort to stop this pandemic, most publishers are making full text of covid-19 related manuscripts freely available. 2 however, every year, the number of published papers is growing at a rate that makes full use of these resources a daunting task @xcite , and it is getting severer especially during the covid-19 pandemic when new information is rapidly emerging. to facilitate the research over these articles, many researchers also publish corpora of preprocessed and curated covid-19 articles such as lidcovid @xcite and @xcite . however, for most users and researchers, it is still challenging to fully explore such a corpus due to the complexity of scientific content it contains (for example, complicated pathways in biomedical field @xcite ). finding connections among multiple corpora is another challenge. even for corpora that are targeting a specific topic like covid-19, they may contain information at different scale for different purposes. for example, one dataset provides parsed text and meta information of articles @xcite , and another provides detailed proteinprotein interactions extracted from sentences @xcite . it is difficult to gain full insight by looking either one of those individually. although search engine is supported for some corpora and portals, this query-based and targeted search is limited in finding connections and patterns that are not obvious from individual articles or sentences @xcite . to enhance the scientific discovery over complex corpora, we propose semantic visualization, a set of text processing and visualization techniques and accompanying tool covid-semviz for enhanced knowledge exploration of covid-19 data (figure 1 ). semantic visualization transforms large datasets of complex networks into rich semantic-aware text data; processes text data in a hierarchical manner; and provides visualizations for the indexed data. the tool covid-semviz allows for searchable and interactive visualization of data through word clouds, heat maps, graphs, etc. unlike other work (see section 4), we focus on constructing and navigating information from biomedical datasets in a unified hierarchical structure. for example, the activation relations between proteins and covid-19 can be constructed as the functional type "covid-19 activators". by reducing relations to a single functional type, it enables the visualization of higher order relations (e.g. relations between covid-19 activators and other protein inhibitors) through a simple 2-dimensional heat map. other types of visualizations will also appear on the side such as a word cloud of proteins that activate covid-19, and a tabular form of evidencing sentences. all these visualizations compose a habitat of information about covid-19. through this, we aim to provide researchers with a global view of selected relationship subtypes drawn from hundreds or thousands of papers at a single glance. this enables the ready identification of novel relationships that would typically be missed by directed keyword searches. we summarize our main contributions as the follows: (1) proposed semantic visualization, a linguistic visual analytic method that enhances the exploration and visualization of scientific text datasets; (2) implemented covid-semviz, a working prototype for enhanced knowledge exploration of covid-19 datasets; (3) user studies that evaluate the effectiveness of our system to the biomedical research community as well as future improvements. ## semantic visualization we propose semantic visualization as a general linguistic visual analytic method for enabling exploration and discovery over large text datasets by exploiting the semantics of the relations in them. this involves (i) collecting data and applying nlp to extract named entities, relations and knowledge graphs from the original text; (ii) indexing the output and creating hierarchical representations for all relevant entities, relations and text that can be visualized in many different ways such as tag clouds, heat maps, graphs, etc.; (iii) applying parameter reduction operations to the extracted relations, creating functional types that can also be visualized using the same methods, allowing the visualization of multiple relations, partial graphs, and exploration across multiple dimensions. ## user studies and evaluation we present user studies from five researchers (t1-t5) by letting them interacting with covid-semviz in their own research on coronaviruses. 9 finding supporting evidence and articles. based on the search of anti-sars cov-2 antibodies, t1 found most of the relevant literature and "allowed me to quickly zero in on the papers and evidencing sentences i would highlight." t2 is interested in hes activities in sars cov-2 and found "many of the common and well known players were revealed in the word cloud". ## related work with the emerging of various covid-19 data resources, many tools have been developed to enable the visualization and exploration of the large amount of articles that are growing everyday. hope et al. (2020) developed scisight foot_9 , a tool that can be used to visualize co-mentions of biomedical concepts such as genes, proteins and cells that are found in the articles related to covid-19. it focuses more on displaying purely the association between entities that are mined from articles. ibm covid-19 navigator foot_10 supports the semantic search by building queries with the combination of general terms, umls (unified medical language system) concepts, authors and boolean operators. it only provides term-level search and no visualization functionality. covid-see foot_11 , proposed by @xcite , supports the search from cord-19 dataset and visualization of article topics and relational concepts. most other visualizations, however, relate to epidemiological statistics and the effects of covid-19 on social and health factors 13 . recent work has been mining useful data from biomedical text. @xcite explored the text structure of biomedical data and used information from different levels of the structure as the features to automatically extract bacteria names. @xcite proposed a text mining system for identifying relationships between biomedical entities. it supports template-based queries for structured search and also provides key sentences as the provenance of identified relations. linguistic visualization research in general is an emerging field of visual analytics for linguistics @xcite . previous research in this field covers thematic text cluster analysis @xcite , ner-based document content analysis @xcite , multi-party discourse analysis @xcite and topic modeling visualization @xcite . @xcite propose a web framework that consists of various linguistic visualization techniques. however, existing work in this field focuses on the analysis of corpora of conversational text and transcripts, and does not include approaches for analyzing and visualizing semantics of relations. ## conclusion we have proposed semantic visualization, a linguistic visual analytic method of multiple steps involving data extraction, parameter reduction, hierarchical structure building and visualization design. it can facilitate the exploration over large and complex datasets by exploiting the semantics of the relations in them. we have also presented covid-semviz, a working prototype for the visualization and exploration of three covid-19-related datasets. our user studies indicate that covid-semviz is helpful to the biomedical community and the utility of semantic visualization techniques. although we only demonstrated how to apply semantic visualization to covid-related articles, our proposed method is generalizable enough to be applied to other text corpora. future work includes addressing current limitations, applying to data from other domains and incorporating more and useful information extraction models in the pipeline. it is our hope that this semantic visualization environment will enable the discovery of novel inferences over relations in complex data that otherwise would go unnoticed.