Bridge inspection reports provide a large amount of technically-detailed bridge condition and maintenance history data/information that could be utilized for advancing the understanding of bridge deterioration for supporting improved maintenance of bridges. However, the valuable information buried in these inspection reports remains unexploited. There is, thus, a need for information extraction (IE) methods to automatically extract information about existing bridge deficiencies and performed maintenance actions from these reports for further analysis. However, existing IE methods are limited in their ability to extract information from such highly-technical text, with varying levels of technical detail, text patterns, and quality. To address this gap, this paper proposes an ontology-based IE framework that extracts entities and relations (i.e., dependency relations) about bridge deficiencies and maintenance actions, and represents the extracted information in a structured way. The proposed IE framework is composed of two primary components: (1) a name entity recognizer for term identification, and (2) a relation extractor for relationship association. This paper focuses on presenting the proposed similarity-based dependency parsing (DP) methodology for automated relation extraction. The proposed DP methodology utilizes a transition-based DP model to capture sentence-level dependency configurations, and represents each configuration with a distributed representation. Each unlabeled configuration is compared to labeled configurations in the distributed representation space by a cosine-similarity measure, and is then labeled with its most similar labeled configuration's transition. The proposed DP methodology achieved a configuration-based accuracy of 78.0%.