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

被引：2

作者：

Lavrac, Nada ^{[1
,2
]}

Martinc, Matej ^{[1
,3
]}

Pollak, Senja ^{[1
]}

Novak, Marusa Pompe ^{[4
]}

Cestnik, Bojan ^{[1
,5
]}

机构：

[1] Jozef Stefan Inst, Jamova 39, Ljubljana 1000, Slovenia

[2] Univ Nova Gorica, Vipayska 13, Nova Gorica 5000, Slovenia

[3] Jozef Stefan Int, Postgrad Sch, Jamova 39, Ljubljana 1000, Slovenia

[4] Natl Inst Biol, Vecna Pot 111, Ljubljana 1000, Slovenia

[5] Temida Doo, Dunajska Cesta 51, Ljubljana 1000, Slovenia

来源：

NEW GENERATION COMPUTING | 2020年 / 38卷 / 04期

基金：

欧盟地平线“2020”;

关键词：

Literature-based discovery; Cross-domain bisociations; Computational creativity; Embeddings technology; MAGNESIUM; MIGRAINE;

D O I：

10.1007/s00354-020-00108-w

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：773 / 800

页数：28

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