The Text-to-SQL task is defined as “given a relational database and a natural language sentence that describes a question on the database, generate an SQL query over the database that expresses the question”. Text-to-SQL strategies based on Large Language Models (LLMs) achieve remarkable performance on well-known benchmarks, but their performance is significantly less for real-world databases. Some of the reasons for this performance decrease lie in the mismatch between the end user’s view of the data and the organization and naming conventions of the database schema, and in the differences between the end user’s data semantics and the encoding of such semantics in the database. This article then proposes an LLM-based strategy to compile natural language questions into SQL queries that uses a knowledge graph and incorporates a dynamic few-shot examples technique. The implementation of the strategy leverages a database keyword search tool and specific naming conventions to expose the knowledge graph to an LLM thereby simplifying the text-to-SQL task. The article includes experiments with a real-world, proprietary relational database and the Mondial database to assess the performance of the proposed strategy. The experiments suggest that the strategy achieves an accuracy on challenging relational databases that surpasses state-of-the-art approaches on the same databases.

Stop-and-move semantic trajectories are segmented trajectories where the stops and moves are semantically enriched with additional data. A query language for semantic trajectory datasets has to include selectors for stops or moves based on their enrichments and sequence expressions that define how to match the results of selectors with the sequence the semantic trajectory defines. This article addresses the problem of searching semantic trajectories, using stop-and-move sequence expressions. The article first proposes a formal framework to define semantic trajectories and introduces stop-and-move sequence expressions, with well-defined syntax and semantics, which act as an expressive query language for semantic trajectories. Then, it describes a concrete semantic trajectory model in RDF, defines SPARQL stop-and-move sequence expressions and discusses strategies to compile such expressions into SPARQL queries. Lastly, the article specifies user-friendly keyword search expressions over semantic trajectories based on the use of keywords to specify stop-and-move queries, and the adoption of terms with predefined semantics to compose sequence expressions. It then shows how to compile such keyword search expressions into SPARQL queries. Finally, it provides a proof-of-concept experiment over a semantic trajectory dataset constructed with user-generated content from Flickr, combined with Wikipedia data.