Data-driven classification of the certainty of scholarly assertions

PeerJ

Beatriz García-Jiménez¹  Mark D. Wilkinson²  Mario Prieto²  Helena Deus³  Anita de Waard⁴  Erik Schultes⁵ 

[1] Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Pozuelo de Alarcon, Madrid, Spain;Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Pozuelo de Alarcon, Madrid, Spain;Elsevier Inc., Cambridge, MA, United States of America;Elsevier Research Collaborations Unit, Jericho, VT, United States of America;GO FAIR International Support and Coordination Office, Leiden, The Netherlands;

关键词: Text mining;    Scholarly communication;    Certainty;    FAIR Data;    Machine learning;   

DOI  :  10.7717/peerj.8871

来源: DOAJ

【 摘 要 】

The grammatical structures scholars use to express their assertions are intended to convey various degrees of certainty or speculation. Prior studies have suggested a variety of categorization systems for scholarly certainty; however, these have not been objectively tested for their validity, particularly with respect to representing the interpretation by the reader, rather than the intention of the author. In this study, we use a series of questionnaires to determine how researchers classify various scholarly assertions, using three distinct certainty classification systems. We find that there are three distinct categories of certainty along a spectrum from high to low. We show that these categories can be detected in an automated manner, using a machine learning model, with a cross-validation accuracy of 89.2% relative to an author-annotated corpus, and 82.2% accuracy against a publicly-annotated corpus. This finding provides an opportunity for contextual metadata related to certainty to be captured as a part of text-mining pipelines, which currently miss these subtle linguistic cues. We provide an exemplar machine-accessible representation—a Nanopublication—where certainty category is embedded as metadata in a formal, ontology-based manner within text-mined scholarly assertions.

【 授权许可】

Unknown