Exploring the knowledge certainty shift: Metaknowledge analysis on drugs via assertion uncertainty burstiness

• Published in: Journal of informetrics 2023-05, Vol.17 (2), p.101412, Article 101412
• Main Authors: Yoon, Jeeyoung, Syafiandini, Arida Ferti, Song, Min
• Format: Article
• Language: English
• Subjects: Metaknowledge analysis; Pharmacology; Uncertainty
• ISSN: 1751-1577; 1875-5879
• DOI: 10.1016/j.joi.2023.101412

•This paper suggests a novel Assertion Uncertainty Burstiness Analysis Framework (AUBAF) for capturing and quantifying uncertainty embedded in pharmacological literature. The suggested approach categorizes drugs based on uncertainty burstiness, enabling a diachronic analysis of assertion uncertainty shift.•We conducted experiments using literature on drugs commonly used in neurodegenerative disease treatment and concluded that AUBAF could determine and interpret uncertainty patterns of different drugs.•Our experiment results show that AUBAF can successfully categorize drugs based on their uncertainty burst. Also, we observed that there exist drugs showing high uncertainty levels, even if they have been approved and commercialized for a particular time.•We believe AUBAF and our experiment results can stimulate more comprehensive research on neurodegenerative disease treatments and contribute to clinical practice in the long run. Pharmacology remains at the center of attention in our effort to ensure everyone has the opportunity for a better life. Previous works underline several contributions of pharmaceutical innovation to economic and social aspects. Based on such importance, numerous studies have conducted thorough metaknowledge analyses of current findings related to drugs using various information. Although there are a few notable works in mapping current findings and generating hypotheses of drugs, the certainty level of findings is lightly explored. Certainties are usually expressed in the literature using hedge cues and indicating speculations. Qualifying certainty expressed in published literature is essential in facilitating the precision of its findings and projecting research directions. This paper suggests a novel Assertion Uncertainty Burstiness Analysis Framework (AUBAF) for capturing and quantifying uncertainty embedded in pharmacological literature. The suggested approach categorizes drugs based on uncertainty burstiness, enabling a diachronic analysis of assertion uncertainty shift. We conducted experiments using literature on drugs commonly used in neurodegenerative disease treatment and concluded that AUBAF could determine and interpret uncertainty patterns. Our experiment results also highlight several drugs with high uncertainty levels, even if they have been approved and commercialized for a particular time. We believe AUBAF and our experiment results can stimulate more comprehensive research on neurodegenerative disease treatments an