An Enhanced Information Retrieval Method Based on Ontology for Bridge Inspection

Abstract

Bridge management systems (BMSs) are widely used to assist an inspector in performing element-level bridge inspection. Retrieving and determining target elements to be inspected becomes an important factor in the efficiency of bridge inspection. This paper presents an enhanced information retrieval (IR) method based on ontology to predict the target elements. The novelty of this method is that an improved seven-step method based on automatic mapping technology is proposed to construct a new bridge inspection ontology (BIontology), which provides a knowledge base for the present IR method. A further novelty is that a new software architecture is designed for integrating ontology, and a promising prototype system based on the software architecture is developed to realize the present IR method using SPARQL query. In addition, a novel prediction algorithm based on the present IR method is proposed to automatically recommend the target elements. A case study of ontology construction is performed to demonstrate that the improved seven-step method can accelerate the construction of the BIontology compared with the manual method. A case study of bridge inspection is implemented to verify that the proposed algorithm outperforms an existing method, thereby validating the effectiveness of the present IR method.