Harmonising the metadata format alone does not solve the issue of efficient access to relevant information in heterogeneous environments, when different systems use different content, contextual and semantic concepts for certain entities. One such type of heterogeneous systems are also Current Research Information Systems (CRIS), which store their data primarily in local relational databases, using different formats and various local concepts. In this article, we study the possibilities and propose a new ontologically supported semantic search engine (OSSSE) which, in addition to the harmonisation of the metadata format among local CRIS systems, also ensures that the meaning of data and/or concepts that belong to various metadata entities are also harmonised. A special model of ontological infrastructure was designed, and dedicated test ontology was created alongside with a new simplified algorithm for creating ontology, the basis of which is the distinction between new and already existing classes in terms of content. Finally, we evaluated the proposed OSSSE model using a simulation of the search process on the base of 41,113 real searches within SICRIS. The obtained results show that regardless of the search situation, the proposed OSSSE is always at least as efficient as a search without ontological support in terms of precision, while recall remains the same; the improvement has been shown to be statistically significant with a high confidence interval (p<0.005). The proposed OSSSE model is able to solve the issue of harmonizing the data where different heterogeneous systems use different content, contextual and semantic concepts, which is the case in many advanced expert systems. In this manner, the more the search is carried out based on the properties described by the supporting ontology, the more the infrastructure can help a searcher. The proposed concepts, ontological infrastructure and the designed semantic search engine may well help to improve search precision in several information retrieval systems. (C) 2016 Elsevier Ltd. All rights reserved.