Linked open data-based explanations for transparent recommender systems.

•We design the main components of an algorithm-agnostic framework to generate natural language explanations;•We propose a methodology to extract descriptive (direct and indirect) properties about the items, and we use these properties to feed a graph-based explanation model;•We define a scoring function to rank these explanation patterns and we use the most relevant ones to generate a template-based natural language explanation;•We validate our methodology by carrying out a large user study (N = 680) in three different domains, as movies, books and music;•We integrate our methodology in a conversational recommender system implemented as a Telegram Bot.