S ci S ight: Combining faceted navigation and research group detection for COVID -19 exploratory scientific search (2020)

Abstract: The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track and explore new directions. Search engines are designed for targeted queries, not for discovery of connections across a corpus. In this paper, we present SciSight, a system for exploratory search of COVID-19 research integrating two key capabilities: first, exploring associations between biomedical facets automatically extracted from papers (e.g., genes, drugs, diseases, patient outcomes); second, combining textual and network information to search and visualize groups of researchers and their ties. SciSight has so far served over 15K users with over 42K page views and 13% returns.

## introduction scientists worldwide are racing against the growing number of covid-19 infections, to understand and treat the disease @xcite . however, a very different kind of exponential growth has been plaguing researchers -the flurry of papers published every year, at a rate that continues to increase @xcite . at the time of this writing, the covid-19 open research dataset (cord-19) @xcite includes over 130,000 publications of potential relevance, both historical and cutting-edge. to boost scientific discovery over this corpus, we propose scisight, a working prototype system for exploratory search of the covid-19 literature. unlike many tools (see section 2), we shift the focus from searching over lists of papers or authors, to navigating networks of biomedical con-cepts and research groups -for example, exploring links between covid-19 and other diseases, or labs working on treatments. while search engines are a powerful tool for finding documents, they are mostly geared toward targeted search, when researchers know what they are looking for -less useful for exploring connections that are not obvious from reading individual papers @xcite . building exploratory interfaces in science is difficult not only due to the complexities of scientific content, but also because of social undercurrents that have tremendous effects on the construction of knowledge @xcite @xcite (as reflected, for example, in biased citation patterns @xcite ). silos of knowledge throughout the literature @xcite foot_0 can hinder research advancement and cross-fertilization across groups and fields that is crucial for driving innovation @xcite , ultimately impacting human lives @xcite . these problems are acute when it comes to the covid-19 pandemic, with new information rapidly emerging and urgently needed. we aim to incorporate the social structure into an intuitive design interface, to help researchers make connections to other groups and ideas in the literature by traversing across networks of concepts and groups of scientists -helping users discover who is working on what, and where? we identify groups by clustering the coauthorship graph, and extract topics and entities from the group's papers. each group is represented using textual and network information: the group's salient authors (who), the topics they work on (what) and their affiliations (where). we build meta-edges capturing topical affinity between groups using a language model fine-tuned for semantic similarity, and present approaches for searching for groups with queries consisting of authors, topics and affiliations. each selected query automatically suggests new queries to try, to support exploration @xcite . in summary, our main contributions: ## related work the field of bibliometric visualization goes back decades @xcite , with a large body of work. visualizations of the scientific literature can take many shapes and forms, with the aim of depicting the connections between fields, topics, authors, and, most commonly, papers @xcite . while much research has been done in this field over the years, actual tools that are readily available primarily focus on visualization of citation-based graphs between individual authors, papers or topics @xcite @xcite . while this rich information could in theory be useful, in practice it often renders the visualization inscrutable, especially for real-world networks comprising many authors. this problem is especially acute when the goal is to enable discovery of new areas with unfamiliar authors. recently, such tools have been applied to covid-19 papers, such as journal networks and heat maps of frequently occurring terms @xcite . however, many tools require training before being able to be used, and state of the art bibliometric mapping is currently considered "complex and unwieldy" (bales et al., 2020), potentially because the typical user "does not immediately comprehend a map and (as a result) is not enticed into using it" @xcite . covid-19 tools. in response to covid-19, many tools for exploring the relevant literature have been released. the great majority featured paper search interfaces, with lists of titles and abstracts being the main focus. many of the covid-19 tools we reviewed included standard faceted search functionality @xcite @xcite , enabling users to filter papers according to various facets. in a search tool from microsoft azure (microsoft, 2020), for example, users can filter search results by various facets (such as by authors or gene mentions extracted automatically from texts). similar services were made available by ibm watson (ibm, 2020), elsevier (elsevier, 2020) and the national institutes of health (nih, 2020). currently a small number of tools focus on concept associations. one tool @xcite ) feeds a covid-19 knowledge graph (kg) from @xcite into kibana foot_1 , an external product for creating dashboards with complex heat maps of term frequencies in documents, including a specialized query language for users with sufficient familiarity with kibana. a tool from @xcite shows clusters of high-level topics extracted with latent dirichlet allocation (lda) @xcite (visualized with word clouds). in this paper, we integrate textual information from papers and the network of author collaborations, allowing users to drill down from research groups to papers to associations between entities in one system, with a custom interface aimed to help users "comprehend the map" @xcite intuitively. ## scisight: system overview in this section we present an overview of our prototype and its distinct components. we motivate each by discussing researcher needs. we illustrate scisight's features and potential with the following illustrative example: marc is a researcher interested in exploring chloroquine, an anti-malarial drug that has been surrounded with controversies in the context of covid-19 (touret and de lamballerie, 2020). in particular, marc wants to find connections between chloroquine and other drugs and diseases, and to understand how these entities are interconnected in order to explore other candidate drugs and potential side-effects. marc is familiar with the field and its main papers, but the amount of re-lated work is overwhelming with a litany of drugs and diseases. complicating things further, knowing that chloroquine is not a new medication, marc wants to examine connections across years of research, not just recent work. ## informal user studies and findings we conduct preliminary user studies with four researchers and one practitioner. p1 is a research scientist in virology, whose work also studies the zika virus; p2 is a postdoctoral fellow in the area of virology, working on viral infections and human antibody responses, and p3 is a postdoctoral fellow and md working primarily in oncology. p4 is a medical professional and pharmd. p5 is a researcher working on viral diseases and proteins. ## conclusion in this paper we presented scisight, an evolving system for scientific literature search and exploration. we demonstrate scisight's use on a large corpus of papers related to the covid-19 pandemic and previous coronaviruses. we use stateof-the-art scientific language models to extract entities such as proteins, drugs and diseases, and an overlapping community detection approach for identifying groups of researchers. to visualize groups we display group "cards" with a novel link scheme capturing topical and social affinities between communities, designed to identify socially disjoint groups working on similar topics. preliminary user interviews suggest that scisight can help complement standard search and may pave new research directions. in future work, we plan to conduct extensive studies to validate scisight and better understand its potential and limitations.