Assessing plausibility of scientific claims to support high-quality content in digital collections

This paper presents a formalization and extension of a novel approach to support high-quality content in digital libraries. Building on the concept of plausibility used in cognitive sciences, we aim at judging the plausibility of new scientific papers in light of prior knowledge. In particular, our work proposes a novel assessment of scientific papers to qualitatively support the work of reviewers. To do this, our approach focuses on the key aspect of scientific papers: claims. Claims are sentences found in empirical scientific papers that state statistical associations between entities and correspond to the core contributions of the papers. We can find these types of claims, for instance, in medicine, chemistry, and biology, where the consumption of a drug, a substance, or a product causes an effect on some other type of entity such as a disease, or another drug or substance. To operationalize the notion of plausibility, we promote claims as first-class citizens for scientific digital libraries and exploit state-of-the-art neural embedding representations of text and topic models. As a proof of concept of the potential usefulness of this notion of plausibility, we study and report extensive experiments on documents with scientific papers from the PubMed digital library.