(Raw Data Set) From Context to Forecast: Ontology-Based Data Integration and AI for Events Prediction

Abstract

This study focuses on event detection and prediction using long short-term memory (LSTM) algorithms implemented in ontology-based sensor data integration. By integrating data from various sources, we facilitate the creation of semantically enriched and contextually integrated data, thereby enhancing the capability for more holistic predictions. The study introduces a framework that leverages ontology models for data integration, fostering the development of semantic context within a sensor network. A feasibility study, conducted with actual sensor data obtained from hydrometric and hydrological stations, underscores the framework's proficiency in abstracting the data integration process, constructing context, correlating events, and predicting their occurrences. The feasibility study results demonstrate the framework's effectiveness and highlight the potential of combining ontologies and artificial intelligence to enhance data interpretation.