Bisociative Literature-Based Discovery: Lessons Learned and New Prospects

The field of bisociative literature-based discovery aims at exploring scientific literature to reveal new discoveries based on yet uncovered relations between knowledge from different, relatively isolated fields of specialization. This paper outlines selected outlier-based literature mining approaches, which focus on finding outlier documents as means for finding unexpected links crossing different contexts. Selected approaches to bridging term detection through outlier document exploration are briefly outlined, together with the lessons learned from recent applications in medical and biological literature-based knowledge discovery. Finally, the paper addresses new prospects in bisociative literaturebased discovery, emphasizing the use of advanced embeddings technology for cross-domain literature min-

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