How do the semantic aspects of information be measured?

Abstract

This study examines methodologies for quantifying the semantic aspects of the concept of information, with a focus on measuring “semantic content” as distinct from “data,” which is commonly addressed in traditional information theory. Conventional measures such as entropy and self-information measure the likelihood of events occurring, and provide valuable insights into the quantity of information. However, they fail to capture the nuances of semantic content, which involves meaning and interpretation, thereby necessitating alternative approaches. Recognizing the multidimensional nature of the information concept, this study aims to bridge the theoretical gaps in current models of semantic information. One influential framework, Floridi's Theory of Strongly Semantic Information (TSSI), quantifies information by considering truth values and semantic discrepancies. This approach successfully aligns with the notion of modeling reality, offering a rigorous foundation for understanding semantic information. Nevertheless, TSSI faces significant limitations. It often fails to calculate instances involving false propositions and lacks consistency in its treatment of true and false cases. These shortcomings restrict its applicability in broader contexts. To address these challenges, this research incorporates Niiniluoto's distance approach, which evaluates the truthlikeness of propositions by measuring their similarity to reality. Unlike TSSI, this method handles both true and false propositions effectively, providing a more comprehensive view of semantic content. This study introduces a novel extension of the distance approach by incorporating agent-specific weighting. This modification allows for adjustments based on individual intentions, interests, or contextual priorities, thus bridging the gap between the mind-independent and mind-dependent properties of information. By integrating these perspectives, the research proposes a more flexible and comprehensive framework for quantifying semantic content. This enriched model not only advances theoretical understanding but also has practical implications for fields where the interaction between data, meaning, and interpretation plays a critical role. Ultimately, this study aims to solidify the foundations for a nuanced theory that captures the multifaceted nature of semantic information in both abstract and applied contexts