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

Since the beginning of the 2019–20 coronavirus pandemic, a large number of relevant articles has been published or become available in preprint servers. These articles, along with earlier related literature, compose a valuable knowledge base affecting contemporary research studies, or even government actions to limit the spread of the disease and directing treatment decisions taken by physicians. However, the number of such articles is increasing at an intense rate making the exploration of the relevant literature and the identification of useful knowledge challenging. In this work, we describe BIP4COVID19, an open dataset that offers a variety of impact measures for coronavirus-related scientific articles. These measures can be exploited for the creation or extension of added-value services aiming to facilitate the exploration of the respective literature, alleviating the aforementioned issue. In the same context, as a use case, we provide a publicly accessible keyword-based search interface for COVID-19-related articles, which leverages our data to rank search results according to the calculated impact indicators.

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“…Allen Institute for AI released SciSight (Hope et al, 2020) COVIDSeer (Rohatgi et al, 2020) is another tool that was built on top of a CORD-19…”

Section: Covid-19 Search Servicesmentioning

confidence: 99%

BIP4COVID19: Releasing impact measures for articles relevant to COVID-19

Vergoulis

Kanellos

Chatzopoulos

et al. 2021

Quantitative Science Studies

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“…The SciSight (Hope et al, 2020) The COVID-SEE (Scientific Evidence Explorer for COVID-19) interface (Verspoor et al, 2020) enables the visual exploration of documents from the CORD-19 corpus through three different views: a sankey diagram displays the relationship between PICO concepts and allows to retrieve the documents where these relations occur; a topic view shows the representative topics of the selected documents and their distribution according to certain coherence measures; and a word cloud view displays the representative concepts of a document.…”

Section: Visualization Approachesmentioning

confidence: 99%

“…Furthermore, we propose an unified pipeline (Figure 1) that facilitates the extraction and visualization of information from the CORD-19 corpus by continuously producing and publishing an enriched linked data knowledge graph. Also, our visualization approach differs from previous solutions to explore the COVID-19 scientific literature (e.g., Hope et al, 2020;Verspoor et al, 2020), by supporting the exploration of meaningful subsets of data suitable to users' needs through the definition of custom SPARQL SELECT queries and via multiple, complementary visualization techniques; and by allowing the user to trace back their exploratory path, which help them to understand how they have arrived to a certain outcome.…”

Section: Introductionmentioning

confidence: 99%

Covid-on-the-Web: Exploring the COVID-19 scientific literature through visualization of linked data from entity and argument mining

Menin

Michel

Gandon

et al. 2021

Quantitative Science Studies

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The unprecedented mobilization of scientists, consequent of the COVID-19 pandemics, has generated an enormous number of scholarly articles that is impossible for a human being to keep track and explore without appropriate tool support. In this context, we created the Covid-on-the-Web project, which aims to assist the access, querying, and sense making of COVID-19 related literature by combining efforts from semantic web, natural language processing, and visualization fields. Particularly, in this paper, we present (i) an RDF dataset, a linked version of the “COVID-19 Open Research Dataset” (CORD-19), enriched via entity linking and argument mining, and (ii) the “Linked Data Visualizer” (LDViz), 28 which assists the querying and visual exploration of the referred dataset. The LDViz tool assists the exploration of different views of the data by combining a querying management interface, which enables the definition of meaningful subsets of data through SPARQL queries, and a visualization interface based on a set of six visualization techniques integrated in a chained visualization concept, which also supports the tracking of provenance information. We demonstrate the potential of our approach to assist biomedical researchers in solving domain-related tasks, as well as to perform exploratory analyses through use case scenarios.

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“…Following the beginning of the COVID-19 outbreak, due to the extremely large interest in COVID-19-related scientific articles, various scholarly search engines, which are tailor-made for the COVID-19-related literature have been developed. First of all, the teams that developed and maintain the major literature datasets provide their own search engines: Allen Institute for AI released SciSight (Hope et al, 2020), a tool for exploring the CORD-19 data, while LitCovid (Q. Chen et al, 2020a,b) provides a search engine featuring basic functionalities (e.g., keyword search, facets).…”

Section: Covid-19 Search Servicesmentioning

confidence: 99%

BIP4COVID19: Releasing impact measures for articles relevant to COVID-19

Vergoulis

Kanellos

Chatzopoulos

et al. 2020

Preprint

View full text Add to dashboard Cite

Since the beginning of the 2019-20 coronavirus pandemic, a large number of relevant articles has been published or become available in preprint servers. These articles, along with earlier related literature, compose a valuable knowledge base affecting contemporary research studies or, even, government actions to limit the spread of the disease and treatment decisions taken by physicians. However, the number of such articles is increasing at an intense rate making the exploration of the relevant literature and the identification of useful knowledge in it challenging. In this work, we describe BIP4COVID19, an open dataset compiled to facilitate the coronavirus-related literature exploration, by providing various indicators of scientific impact for the relevant articles. Finally, we provide a publicly accessible web interface on top of our data, allowing the exploration of the publications based on the computed indicators.

show abstract

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

Cited by 22 publications

References 39 publications

BIP4COVID19: Releasing impact measures for articles relevant to COVID-19

BIP4COVID19: Releasing impact measures for articles relevant to COVID-19

Covid-on-the-Web: Exploring the COVID-19 scientific literature through visualization of linked data from entity and argument mining

BIP4COVID19: Releasing impact measures for articles relevant to COVID-19

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