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This article reports on a NISO Plus 2022 session that addressed what can be done to safeguard the integrity of the scholarly content being created, disseminated, and used. How much responsibility does the information community have in ensuring that the content we provide is authoritative? Preprints are a great way to make early research results available, but it is not always clear that those results are not yet thoroughly vetted. Peer review – a key element of scholarly publication – can help, but is far from foolproof. Retractions are another important tool, but most retracted research is still all too readily available. What can and should we be doing to safeguard the integrity of the content being created, disseminated, and used? [ABSTRACT FROM AUTHOR]

In this research we aim to demonstrate that an ontology-based system can categorize potential drug-drug interaction (PDDI) evidence items into complex types based on a small set of simple questions. Such a method could increase the transparency and reliability of PDDI evidence evaluation, while also reducing the variations in content and seriousness ratings present in PDDI knowledge bases. We extended the DIDEO ontology with 44 formal evidence type definitions. We then manually annotated the evidence types of 30 evidence items. We tested an RDF/OWL representation of answers to a small number of simple questions about each of these 30 evidence items and showed that automatic inference can determine the detailed evidence types based on this small number of simpler questions. These results show proof-of-concept for a decision support infrastructure that frees the evidence evaluator from mastering relatively complex written evidence type definitions.