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

The 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 sele...

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Bibliographic Details
Published inNew generation computing Vol. 38; no. 4; pp. 773 - 800
Main Authors Lavrač, Nada, Martinc, Matej, Pollak, Senja, Pompe Novak, Maruša, Cestnik, Bojan
Format Journal Article
LanguageEnglish
Published Tokyo Ohmsha 01.11.2020
Subjects
Artificial Intelligence
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Software Engineering/Programming and Operating Systems
Cross-domain bisociations
Literature-based discovery
Computational creativity
Embeddings technology
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Summary:The 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.
ISSN:0288-3635
1882-7055
DOI:10.1007/s00354-020-00108-w