Monitoring semantic relatedness and revealing fairness and biases through trend tests

An emerging application domain concerning content-based recommender systems provides a better consideration of the semantics behind textual descriptions. Traditional approaches often miss relevant information due to their sole focus on syntax. However, the Semantic Web community has enriched resources with cultural and linguistic background knowledge, offering new standards for word categorization. This paper proposes a framework that combines the information extractor ReVerb with the WordNet taxonomy to monitor global semantic relatedness scores. Additionally, an experimental validation confronts human-based semantic relatedness scores with theoretical ones, employing Mann–Kendall trend tests to reveal fairness and biases. Overall, our framework introduces a novel approach to semantic relatedness monitoring by providing valuable insights into fairness and biases. • We combined ReVerb and WordNet to perform global semantic relatedness scores. • Some historical similarity measures were redefined using a logical formalism. • Our framework monitors the relatedness scores through a segmental views-based interface. • We confronted humans-based semantic similarity scores with the theoretical ones. • We performed a set of Mann–Kendall trend tests in order to reveal fairness and biases.