Bisociative Literature-Based Discovery: Lessons Learned and New Word Embedding Approach-Reference-Cited by-同舟云学术

Bisociative Literature-Based Discovery: Lessons Learned and New Word Embedding Approach

Published:2020-10-06 Issue:4 Volume:38 Page:773-800
ISSN:0288-3635
Container-title:New Generation Computing
language:en
Short-container-title:New Gener. Comput.

Author:

Lavrač Nada,Martinc Matej,Pollak Senja,Pompe Novak Maruša,Cestnik Bojan^ORCID

Abstract

AbstractThe field of bisociative literature-based discovery aims at mining scientific literature to reveal yet uncovered connections between different fields of specialization. This paper outlines several outlier-based literature mining approaches to bridging term detection and the lessons learned from selected biomedical literature-based discovery applications. The paper addresses also new prospects in bisociative literature-based discovery, proposing an advanced embeddings-based technology for cross-domain literature mining.

Funder

Horizon 2020

Javna Agencija za Raziskovalno Dejavnost RS

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00354-020-00108-w.pdf

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