This presentation will discuss how linguistic relevance parameters improve the quality of knowledge discovery and use, thus bringing a substantial added value to the quality of the accessed information and enabling efficient and optimal response. It shows linguistic relevance pertinence as a natural extension of other well-spread pertinence parameters, such as popularity, authority, user profile analytics, etc. It will discuss several different dimensions of natural language relevance parameters: formal linguistic, and subjective perceptual, and how they interrelate to bring best results in different kinds of knowledge use paradigms. We will give some comparative examples of results.