Description
Recent advances in AI promise new possibilities for knowledge integration, synthesis, and dissemination. However, novel AI systems must produce reliable answers if researchers and stakeholders want to make trustworthy claims, predictions, and decisions. Carefully curated Knowledge Graphs can be the key for this reliability, as they represent research data in a machine-readable, -interpretable, and -actionable fashion. In this talk, we present an LLM (Large Language Model)-based Question-Answering Interface that leverages facts from a Knowledge Graph to produce grounded answers in plain language. This both bridges the gap between users and Knowledge Graphs – as the technical query language SPARQL is notoriously difficult to use – and allows users to trace how the answer to their question was formulated. The application allows users to converse with PhenObs, a global network of botanical gardens that observes plant phenology – the timing of biological events, such as flowering, fruiting, and senescence. Users can ask questions, generate plots and visualizations, and download the data relevant to their question for further analysis. The chatbot’s flexibility makes research data available to users, no matter their prior experience, and thus supports other researchers in their synthesis efforts while giving the general public and enthusiasts a tangible entry point into the research. Additionally, as the system follows a modular design, the Question-Answering Interface can easily be configured to answer questions over different Knowledge Graphs. We invite attendees to try the provided demo and give their suggestions on further functionalities.
| Topic | Topic 4: Can knowledge graphs and AI-based synthesis improve ecological synthesis? |
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