This paper proposes an information system, which aims to bridge the semantic gap in web search. The system uses multiple domain ontological structures expanding the user’s query with semantically related concepts, enhancing in parallel the quality of retrieval to a large extend. Query analyzers broaden the user’s information needs from classical term-based to conceptually representations, using knowledge from relevant ontologies and theirs’ properties. Besides the use of semantics, the system employs machine learning techniques from the field of swarm intelligence through the Ant Colony algorithm, where ants are considered as web agents capable of collecting and processing relevant information. Furthermore, the effectiveness of the approach is verified experimentally, by observing that the retrieval precision for the enhanced queries is in higher levels, in comparison with the results derived from the classical term-based retrieval procedure.