Context-aware recommender systems have long recognized that factors such as location, time, and weather shape where and what people choose to eat. Existing weather-aware food and point-of-interest recommenders, however, typically treat weather generically -- mapping conditions to preferences through hand-crafted rules or specially trained context models -- and do not capture that the culturally appropriate response to weather is itself region-specific: a rainy evening calls for hot tea and fried snacks in one culinary culture and for very different comfort food in another. Encoding such weather-by-region-by-cuisine interactions as explicit rules or training data is brittle and does not scale. We present a weather- and location-aware agentic dining-recommendation system that takes a different approach: a large language model (LLM) orchestrates tools for location and weather retrieval and then reasons in natural language over the combined context, drawing on the cultural and culinary world knowledge already latent in the model to produce region-sensitive, weather-appropriate recommendations without per-region rule tables or specialized training. We describe the agent architecture, the tool-orchestration flow (Google location services and a weather service feeding an OpenAI LLM), and the reasoning mechanism, and we report on a working prototype that was implemented and briefly deployed end-to-end. We discuss design trade-offs -- cost, latency, ambiguity handling, and fallbacks -- and we are explicit about limitations, including the absence of a formal user study and the risk of cultural stereotyping in locality-based inference. The contribution is architectural: a simple, extensible pattern for incorporating environmental and cultural context into agentic recommendation through LLM reasoning rather than engineered rules.
We present an extension of Triangolazioni (a Cultural Heritage webapp) to enrich curated content with context-dependent, external information provided by Large Language Models (LLMs) within a loosely-coupled architecture agnostic to the LLM. The system supports context-dependent information search and presentation within an architecture agnostic to the exploited LLM.
Hussain Hussain, Stefan Schöberl, Angelika Schneider +1cs.AI
Organizations increasingly define operational metrics in structured, machine-readable formats to monitor systems, processes, and compliance. These metric definitions implicitly encode domain knowledge, such as referencing concepts, properties, and relationships, that often extends what is captured in formal ontologies. Yet the connection between operational metric catalogues and ontological knowledge remains manual, ad-hoc, and labor-intensive. We present COntExt, a framework for context-aware ontology extension that takes structured metric definitions as input and suggests how referenced concepts and properties should be integrated into an existing ontology, utilizing the context of these metrics. The framework defines the extension problem as three sub-tasks: parent class prediction, relation type prediction, and data property assignment. Across four cybersecurity ontologies, we evaluate different algorithms for each task. Our results show that metric-derived context improves the suggestions over ontology-context baselines for relation type prediction and data property assignment. Our work demonstrates that operational metric catalogues are a practical and underexploited source for ontology extension. This work enables organizations to maintain their ontologies at a significantly lower cost than manual engineering.
Accurate vessel trajectory forecasting is essential for maritime situational awareness, navigation safety, traffic management, and autonomous navigation. Public Automatic Identification System (AIS) archives have enabled extensive research in this area, yet results remain difficult to compare because existing studies use incompatible preprocessing pipelines, forecasting horizons, data splits, coordinate systems, contextual inputs, and evaluation settings. We present EnvShip, a unified multi-region framework for context-aware and cross-region vessel trajectory forecasting. EnvShip applies a fixed and reproducible pipeline to public AIS data from Denmark, the United States, Greece, and Norway, and defines two standardized forecasting tracks spanning short- and long-horizon settings. From large-scale processed data, we curate 330{,}000 short-term and 106{,}857 long-horizon samples through strict motion screening, vessel-category and difficulty stratification, redundancy control, and vessel-disjoint splits. Each sample is aligned with environmental and neighboring-vessel context, together with weather and sea-state variables where available. We evaluate representative methods under in-domain and cross-region protocols, with analyses across prediction difficulty, scene type, and random seeds. Results show that environmental context provides the largest gains in coastline-constrained scenes, whereas neighboring-vessel context primarily benefits interaction-rich cases. Multi-region training improves generalization in most settings but introduces negative transfer for some source combinations. EnvShip provides a common and reproducible testbed for vessel trajectory forecasting. Code and data are publicly available at https://huggingface.co/datasets/mark000071/envship_v2_datasets.