About: SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search

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About: SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search
Creator	Borchardt, Jonathan Hearst, Marti Hope, Tom Portenoy, Jason Vasan, Kishore Weld, Daniel West, Jevin
Source	BioRxiv
abstract	The COVID-19 pandemic has sparked unprecedented mobilization of scientists, already generating thousands of new papers that join a litany of previous biomedical work in related areas. This deluge of information makes it hard for researchers to keep track of their own field, let alone explore new directions. Standard search engines are designed primarily for targeted search and are not geared for discovery or making connections that are not obvious from reading individual papers. In this paper, we present our ongoing work on SciSight, a novel framework for exploratory search of COVID-19 research. Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring interactions between biomedical facets (e.g., proteins, genes, drugs, diseases, patient characteristics); and second, discovering groups of researchers and how they are connected. We extract entities using a language model pre-trained on several biomedical information extraction tasks, and enrich them with data from the Microsoft Academic Graph (MAG). To find research groups automatically, we use hierarchical clustering with overlap to allow authors, as they do, to belong to multiple groups. Finally, we introduce a novel presentation of these groups based on both topical and social affinities, allowing users to drill down from groups to papers to associations between entities, and update query suggestions on the fly with the goal of facilitating exploratory navigation. SciSight1 has thus far served over 10K users with over 30K page views and 13% returning users. Preliminary user interviews with biomedical researchers suggest that SciSight complements current approaches and helps find new and relevant knowledge.
has issue date	2020-05-26(xsd:dateTime)
bibo:doi	10.1101/2020.05.23.112284
has license	biorxiv
sha1sum (hex)	01adacf695d33973f7b7989e2e0d90da18ea7f73
schema:url	https://doi.org/10.1101/2020.05.23.112284
resource representing a document's title	SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search
schema:publication	bioRxiv
resource representing a document's body	covid:01adacf695d33973f7b7989e2e0d90da18ea7f73#body_text
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