A big size of bridge data is being constantly generated - from a variety of distributed sources and in heterogeneous formats - in the form of national bridge inventory (NBI) data, national bridge elements (NBE) data, bridge inspection and maintenance reports, traffic data, etc. Big bridge data analytics open unprecedented opportunities to leverage this large amount of data for improved bridge deterioration prediction and understanding, and enhanced maintenance decision making. In this paper, the authors propose a big bridge data analytics framework that consists of: (1) semantic data/information extraction and integration, (2) semantic data/information analysis and mining, and (3) semantic data/information retrieval and visualization. The big size of the data, in addition to it being scattered and heterogeneity of meaning and format, adds to the challenge of data integration and analysis. As such, at the cornerstone of this framework is an ontology of bridge deterioration knowledge, which aims to facilitate the semantic integration and analysis of the data based on content and domain-specific meaning, and to bridge the terminology gap across different sources. This paper focuses on providing an overview of the framework for big bridge data analytics and presenting the proposed bridge deterioration ontology as the first step towards implementing this framework.