  As the amount and complexity of data grows, retrieving it has become a more
difficult task that requires greater knowledge and resources. This is
especially true for the logistics industry, where new technologies for data
collection provide tremendous amounts of interconnected real-time data. The
Intelligent Query and Learning System (IQLS) simplifies the process by allowing
natural language use to simplify data retrieval . It maps structured data into
a framework based on the available metadata and available data models. This
framework creates an environment for an agent powered by a Large Language
Model. The agent utilizes the hierarchical nature of the data to filter
iteratively by making multiple small context-aware decisions instead of
one-shot data retrieval. After the Data filtering, the IQLS enables the agent
to fulfill tasks given by the user query through interfaces. These interfaces
range from multimodal transportation information retrieval to route planning
under multiple constraints. The latter lets the agent define a dynamic object,
which is determined based on the query parameters. This object represents a
driver capable of navigating a road network. The road network is depicted as a
graph with attributes based on the data. Using a modified version of the
Dijkstra algorithm, the optimal route under the given constraints can be
determined. Throughout the entire process, the user maintains the ability to
interact and guide the system. The IQLS is showcased in a case study on the
Canadian logistics sector, allowing geospatial, visual, tabular and text data
to be easily queried semantically in natural language.
