MOTIVATION: Integrated analysis across biological databases is becoming increasingly important in life science research, leading many public databases to adopt Semantic Web technologies, also known as knowledge graphs. However, biological data possesses inherently complex and diverse structures, which makes the resulting Resource Description Framework (RDF) schemas intricate and difficult for non-expert users to master, preventing them from translating natural language questions into correct SPARQL queries. Although recent large language model (LLM)-based approaches show potential for automatic SPARQL query generation, they often suffer from structural hallucinations and require large-scale training data to capture schema-specific structures. In this study, we propose a novel framework that avoids hallucinations and requires no training data by combining LLM-based word extraction with a schema-based SPARQL query builder. RESULTS: The LLM extracts variables and parameters from the user's question based on a predefined schema, and the query builder generates a syntactically correct SPARQL query accordingly. By providing a predefined schema in prompts, our method eliminates the need for training data. Experimental results on UniProt, Rhea, and Bgee demonstrate that our method outperforms baseline LLM-based methods using fine-tuning and prompt-tuning in terms of the similarity between search results obtained from generated and expert-written queries. Furthermore, we developed a proof-of-concept chatbot system that enables users to query RDF databases via natural language input, demonstrating the practical utility of our approach in improving access to biological data resources. AVAILABILITY AND IMPLEMENTATION: Experimental environment: https://github.com/scott2121/sparql_query_generator (DOI: https://doi.org/10.5281/zenodo.18539213). Chatbot: https://github.com/scott2121/sparql_query_chatbot (DOI: https://doi.org/10.5281/zenodo.18539225).

MOTIVATION: Understanding life cannot be accomplished without making full use of biological data, which are scattered across databases of diverse categories in life sciences. To connect such data seamlessly, identifier (ID) conversion plays a key role. However, existing ID conversion services have disadvantages, such as covering only a limited range of biological categories of databases, not keeping up with the updates of the original databases and outputs being hard to interpret in the context of biological relations, especially when converting IDs in multiple steps. RESULTS: TogoID is an ID conversion service implementing unique features with an intuitive web interface and an application programming interface (API) for programmatic access. TogoID currently supports 65 datasets covering various biological categories. TogoID users can perform exploratory multistep conversions to find a path among IDs. To guide the interpretation of biological meanings in the conversions, we crafted an ontology that defines the semantics of the dataset relations. AVAILABILITY AND IMPLEMENTATION: The TogoID service is freely available on the TogoID website (https://togoid.dbcls.jp/) and the API is also provided to allow programmatic access. To encourage developers to add new dataset pairs, the system stores the configurations of pairs at the GitHub repository (https://github.com/togoid/togoid-config) and accepts the request of additional pairs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.