The rise of big data and multimodal content—from text and images to videos and audio—demands sophisticated data management and analysis techniques, particularly in intelligent environments. This paper addresses the challenges of managing and analyzing multimodal data within complex Entity-Relationship (ER) models, often structured in normalized forms to enhance efficiency and scalability. We delve into relational learning, emphasizing the integration and manipulation of data tables in third normal form, and explore how these are processed through feature extraction techniques into Machine Learning (ML) models.

Central to our study is the use of the propositionalization algorithm, Wordification, a method that facilitates the straightforward application of general ML algorithms and supports specialized algorithms like PropDRM and PropStar, designed for multi-relational data mining. This research aims to showcase the efficacy of our automated Wordification method for feature extraction, contrasting it with traditional manual approaches.

Our findings indicate that propositionalization significantly simplifies the application of ML algorithms to relational learning, enhancing the processing of multimodal data in intelligent environments. This method significantly improves efficiency and scalability, proving beneficial for rapid deployment and the enhancement of social intelligence through more effective data analysis.