Report: Workshop on connecting Knowledge Graphs with BioChatter

The workshop on connecting Knowledge Graphs (KGs) with BioChatter convened experts from biology, computer science, and bioinformatics to tackle challenges in integrating and accessing dispersed datasets in plant sciences. The goal was to create user-friendly interfaces for querying these datasets using natural language, bypassing the need for expertise in semantic technologies or query languages like SPARQL or Cypher.Key use cases included the BrAPI project, which aimed to simplify data retrieval from plant research datasets. While BioChatter effectively generated simple API queries, complex multi-step queries posed challenges, suggesting that programmatic approaches are better suited for such tasks. Integrating BrAPI with BioCypher enabled successful querying of KGs for questions like identifying studies involving specific plant varieties.The RDF adapter use case focused on enhancing the Plant Phenotyping Experiment Ontology (PPEO) by converting it into a BioCypher-compatible KG, thereby improving data interoperability and enabling LLMs to generate context-aware responses. The Mobile Element Knowledge Graph use case explored the relationship between transposable elements and gene regulation networks, utilizing BioChatter to assist users unfamiliar with Cypher.The Stress Knowledge Map (SKM) use case integrated a highly curated model of plant stress signaling with BioCypher and BioChatter, allowing natural language queries and improving access to complex biological data. The Chem and Plant KG use case aimed to integrate diverse scientific resources into a unified KG, enhancing data interoperability and accessibility.Challenges included the need for human-readable concepts within KGs to improve LLM interaction and aligning LLMs with user demands. Future work will focus on refining KG schemas, improving LLM integration, and expanding documentation to support broader adoption and utility in scientific research. The workshop highlighted the potential of combining KGs with LLMs to enhance data accessibility and drive new insights in biological and agricultural sciences.