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

Hope, Tom; Portenoy, Jason; Vasan, Kishore; Borchardt, Jonathan; Horvitz, Eric; Weld, Daniel S.; Hearst, Marti A.; West, Jevin D.

doi:10.48550/arxiv.2005.12668

Cited by 7 publications

(10 citation statements)

References 18 publications

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“…The vastness of COVID-19 literature also spurned the need for having systems that could allow researchers and base users alike to get their COVID-19 queries answered. Systems like CKG (Wise et al) [41] and SciSight (Hope et al) [14] currently provide this functionality. While we do aim to provide an easy to use webframework for medical researchers, the scope of the aforementioned systems is beyond the scope of our work.…”

Section: Related Workmentioning

confidence: 99%

Accelerating COVID-19 research with graph mining and transformer-based learning

Tyagin¹,

Kulshrestha²,

Sybrandt³

et al. 2021

Preprint

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In 2020, the White House released the, "Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset," wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. The Allen Institute for AI and collaborators announced the availability of a rapidly growing open dataset of publications, the COVID-19 Open Research Dataset (CORD-19). As the pace of research accelerates, biomedical scientists struggle to stay current. To expedite their investigations, scientists leverage hypothesis generation systems, which can automatically inspect published papers to discover novel implicit connections. We present an automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. The systems are based on graph-mining and the transformer model. The systems are massively validated using retrospective information rediscovery and proactive analysis involving human-in-the-loop expert analysis. Both systems achieve high-quality predictions across domains (in some domains up to 0.97% ROC AUC) in fast computational time and are released to the broad scientific community to accelerate biomedical research. In addition, by performing the domain expert curated study, we show that the systems are able to discover on-going research findings such as the relationship between COVID-19 and oxytocin hormone.

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Section: Related Workmentioning

confidence: 99%

Accelerating COVID-19 research with graph mining and transformer-based learning

Tyagin¹,

Kulshrestha²,

Sybrandt³

et al. 2021

Preprint

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“…However, few literature search and recommendation tools address the issue that documents often contain multiple aspects [35]. Further, prior work has also demonstrated that researchers often desire finer-grained control in literature search and navigation tools [12,18,34]. Context-dependent faceted search tools have the potential to fill this gap.…”

Section: Query Titlementioning

confidence: 99%

“…In similar vein Neves et al [34], extensively evaluate a range of methods intended to extract rhetorical structure elements for faceted scientific paper search and evaluate it with a small scale dataset of biomedical publications labelled for fine grained facet similarity. Hope et al [18] allows exploratory search using multi-facet characterizations of scientific articles for the COVID19 research literature. Faceted document similarity for articles has been explored most recently in weakly supervised settings in Ostendorff [35] and Kobayashi et al [22], with both works evaluating using the task of predicting papers cited in a particular section of a research article.…”

Section: Related Workmentioning

confidence: 99%

CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example

Mysore¹,

O'Gorman²,

McCallum³

et al. 2021

Preprint

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Query by Example is a well-known information retrieval task in which a document is chosen by the user as the search query and the goal is to retrieve relevant documents from a large collection. However, a document often covers multiple aspects of a topic. To address this scenario we introduce the task of faceted Query by Example in which users can also specify a finer grained aspect in addition to the input query document. We focus on the application of this task in scientific literature search. We envision models which are able to retrieve scientific papers analogous to a query scientific paper along specifically chosen rhetorical structure elements as one solution to this problem. In this work, the rhetorical structure elements, which we refer to as facets, indicate "background", "method", or "result" aspects of a scientific paper. We introduce and describe an expert annotated test collection to evaluate models trained to perform this task. Our test collection consists of a diverse set of 50 query documents, drawn from computational linguistics and machine learning venues. We carefully followed the annotation guideline used by TREC for depth-k pooling (k = 100 or 250) and the resulting data collection consists of graded relevance scores with high annotation agreement. The data is freely available for research purposes.

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“…A lot of previous work extracts biomedical entities (Habibi et al, 2017;Crichton et al, 2017;Wang et al, 2018;Beltagy et al, 2019;Alsentzer et al, 2019;Wang et al, 2020c), relations (Uzuner et al, 2011;Krallinger et al, 2011;Manandhar and Yuret, 2013;Bui et al, 2014;Peng et al, 2016;Wei et al, 2015;Peng et al, 2017;Luo et al, 2017;Peng et al, 2019Peng et al, , 2020, and events (Ananiadou et al, 2010;Van Landeghem et al, 2013;Nédellec et al, 2013;Deléger et al, 2016;Li et al, 2019;ShafieiBavani et al, 2020) from biomedical literature, and more recent work focuses on COVID-19 literature (Hope et al, 2020;Ilievski et al, 2020;Wolinski, 2020;Ahamed and Samad, 2020).…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

Wang¹,

Li²,

Wang³

et al. 2020

Preprint

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To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract finegrained multimedia knowledge elements (entities, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures and knowledge subgraphs as evidence. All of the data, KGs, reports 1 , resources and shared services are publicly available 2 .

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

Cited by 7 publications

References 18 publications

Accelerating COVID-19 research with graph mining and transformer-based learning

Accelerating COVID-19 research with graph mining and transformer-based learning

CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

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