Semantic annotation for context-aware information retrieval for supporting the environmental review of transportation projects

Although transportation practitioners nowadays have an unprecedented level of access to massive information, there still exist substantial gaps in their ability to efficiently and reliably find the right information, at the right time, and for the task/decision at hand. To address this gap, this paper proposes a context-aware information retrieval (IR) approach, which can capture and exploit the conceptualization of user needs, decision context, and content meanings to support the retrieval of information that is more relevant to decision making. The proposed IR approach includes three primary components: semantic annotation (SA), semantic query processing (SQP), and semantic document ranking (SDR). This paper focuses on SA for IR for supporting the Transportation Project Environmental Review (TPER) decision-making process. The paper proposes an epistemology-based SA algorithm for automatically annotating webpages in the TPER domain with contextual concepts from an epistemological model. The TPER epistemology is a semantic model for representing and reasoning about information and information retrieval in the TPER domain. In developing the proposed algorithm, a number of syntactic-based and semantic-based annotation methods/algorithms were developed and tested. For the syntactic-based algorithms, the effect of syntactic expansion and filtering was investigated. For the semantic-based algorithms, different semantic similarity calculation methods were evaluated. All the algorithms were tested on a testing data set of 1,328 webpages, which were collected from the FHWA Environmental Review Toolkit Website, and evaluated in terms of Mean Average Precision (MAP) and average precision. The final, proposed SA algorithm achieved 84.07% MAP and 90.67% average precision at top 50 documents, on the testing data.