Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Information Retrieval

arXiv:2009.02526 (cs)
COVID-19 e-print

Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field.

[Submitted on 5 Sep 2020 (v1), last revised 13 Oct 2020 (this version, v3)]

Title:Vapur: A Search Engine to Find Related Protein-Compound Pairs in COVID-19 Literature

Authors:Abdullatif Köksal, Hilal Dönmez, Rıza Özçelik, Elif Ozkirimli, Arzucan Özgür
View PDF
Abstract:Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains. The resulting publications created a huge text collection in which finding the studies related to a biomolecule of interest is challenging for general purpose search engines because the publications are rich in domain specific terminology. Here, we present Vapur: an online COVID-19 search engine specifically designed to find related protein - chemical pairs. Vapur is empowered with a relation-oriented inverted index that is able to retrieve and group studies for a query biomolecule with respect to its related entities. The inverted index of Vapur is automatically created with a BioNLP pipeline and integrated with an online user interface. The online interface is designed for the smooth traversal of the current literature by domain researchers and is publicly available at this https URL .
Comments: EMNLP 2020 - COVID-19 Workshop
Subjects: Information Retrieval (cs.IR); Machine Learning (cs.LG); Molecular Networks (q-bio.MN)
Cite as: arXiv:2009.02526 [cs.IR]
  (or arXiv:2009.02526v3 [cs.IR] for this version)
  https://doi.org/10.48550/arXiv.2009.02526

Submission history

From: Riza Ozcelik [view email]
[v1] Sat, 5 Sep 2020 12:50:54 UTC (8,394 KB)
[v2] Sat, 10 Oct 2020 21:45:27 UTC (8,716 KB)
[v3] Tue, 13 Oct 2020 06:34:24 UTC (8,716 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
license icon view license
Current browse context:
cs.IR
< prev   |   next >
new | recent | 2020-09
Change to browse by:
cs
cs.LG
q-bio
q-bio.MN

References & Citations

DBLP - CS Bibliography

listing | bibtex
Arzucan Özgür
export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)