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Leonore Reiser

Publications and source records attributed to Leonore Reiser.

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Engaging the scientific community in high-quality biocuration: a report on the International Society for Biocuration workshop, 'Maximizing community curation for the benefit of all'

Biological knowledgebases traditionally rely on expert, professional curation of the research literature to maintain up-to-date collections of data organized in machine-readable form. However, despite the increasing amount of curatable biomedical knowledge, support for knowledgebases is declining, leaving these resources no alternative but to explore additional ways of updating and maintaining content. One way in which knowledgebases have addressed this problem is by engaging researchers to help curate their published papers, a process generally known as 'community curation'. As helpful as community curation can be, though, it is not universally adopted and, for groups that do have it, there is a wide range of approaches. To learn about existing community curation pipelines and explore possibilities for working towards a common approach, we organized a workshop, Maximizing Community Curation for the Benefit of All, at the 18th International Biocuration Conference, hosted by the Stowers Institute for Medical Research. Our aim was to examine the different strategies that groups use, share successes, failures, and ongoing challenges, and produce suggested deliverables for broader adoption of common best practices and tools for effective community curation. Representatives from 18 different resources, ranging from model organism and specialty knowledgebases to journals and literature resources, presented their work. The result was a comprehensive assessment of the state-of-the-art for community curation and an in-depth discussion on how community curation can become standard practice for maintaining timely, highquality biological resources that will continue to provide scientists with the essential information they need for their research.

cs.DB

Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)

Background: Ontologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources. Results: We assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues. Conclusions: These findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.

cs.AI