Knowledge discovery from data in environmental sciences is becoming more and more important nowadays because of the deluge of information found in databases of digital ecosystems, coming altogether from institutions and amateurs. For example in biodiversity science, all these data need to be validated by specialists with the help of Intelligent Environmental Decision Support Systems (IEDSSs), then enhanced and certified into qualitative knowledge before reaching their audience. Data mining through classification or clustering is the dedicated inductive process of grouping descriptions based on similarity measures, then building classes and naming them. Later, the formed concepts can be reused for identification purpose with new observations. The problem is that when using such knowledge-based systems, we tend to forget the fundamental role of subjects (end-users) in the definition, observation and description of objects. In order to get good identification results, a consensus must be found between these experts and amateurs for interpreting correctly the observed objects. Thus, a new method of Knowledge discovery is necessary by switching from Data mining to Sign management.The method focuses on the process of building knowledge by sharing signs and significations (Semiotic Web), more than on knowledge transmission with intelligent object representations (Semantic Web). Sign management is the shift of paradigm for Biodiversity Informatics that we have investigated in such domains as enhancing natural heritage with ICT. In this paper, we will present Sign management and illustrate this concept with two knowledge bases built inLa Reunion Island for corals’classification with IKBS (Iterative Knowledge Base System) and plants identification with X per2 software platform.