In the paper an important scientific and practical problem of building intelligent decision support systems (IDSS) which knowledge base core is an ontology has been solved. Classification of those systems according to its functioning has been done. For each class appropriate mathematical software has been developed. IDSS models which functioning is based on the ontology had been investigated. The concept of adaptive ontology has been introduced. The model of adaptive ontology is considered as development of the classic model by adding importance weights of the concepts and relations that are stored in the ontology. Entering such scalar values (importance weights) allows adopting the ontology to the specific subject area and tasks, which are solved in the IDSS by configuring the entered weights. Further those weights are used to build metrics for search IDSS solutions. Tasks of machine learning of upper ontology and its optimization had been investigated. Recursive method of the automatic development of ontology has been developed.