Intelligent processing and data management of sports content in media and communications

Abstract

This work presents a framework for intelligent processing and management automation of sports content utilizing algorithmic techniques and Artificial Intelligence (AI) methods, such as Machine/ Deep Learning (ML/DL). In the modern digital media landscape, sports data is among the most popular news/informing categories, favoring mediated communication and audience interaction. The main subject of interest may concern any sports event (e.g., a sports match) and all accompanying data related to player statements, athlete profiles, historical records of similar sports competitions, broader events, audience reactions, and more. The proposed approach introduces a series of techniques for semantic processing, annotating, and data linking mechanisms, providing a broader framework for indexing, and retrieving interconnected information. These data may include audiovisual material from event recordings, textual streams of unstructured or standardized descriptions, contributed content, comments, and reactions from ordinary users, before, during and after the main event. A modular ontology for organizing and describing events allows for the structured management of these informatory streams, making them useful for coaches, sports analysts, editors, journalists, and the broader audience. The basic functional capabilities and pilot results of the initial techniques applied in basketball are presented.