Buildings tend to not operate as intended, and a pronounced gap often exists between measured and predicted environmental and energy performance. Although the causes of this 'performance gap' are multi-faceted, issues surrounding data integration are key contributory factors. The distributed nature of the Architecture, Engineering and Construction (AEC) industry presents many challenges to the effective capture, integration and assessment of building performance data. Not all building data can be described semantically, nor is it feasible to create adapters between many different software tools. Similarly, not all building contextual data can easily be captured in a single product-centric model. This paper presents a new solution to the problem based upon a hybrid architecture that links data which is retained in its original format. The architecture links existing and efficient relational databases storing time series data and semantically-described building contextual data. The main contribution of this work is an original RDF syntax structure and ontology to represent existing database schema information, and a new mechanism that automatically prepares data streams for processing by rule-based performance definitions. Two test cases evaluate the concept by 1) applying the hybrid architecture to building performance data from an actual building, and 2) evaluating the efficiency of the architecture against a purely RDF-based solution that also stores all of the time-series data in RDF for a virtual building. The hybrid architecture also avoids the duplication of time-series data and overcomes some of the differences found in database schemas and database platforms. (C) 2016 Published by Elsevier B.V.