Ontology-based Sequence Labelling for Automated Information Extraction for Supporting Bridge Data Analytics

The massive amount of data/information buried in textual bridge inspection reports open opportunities to leverage big data analytics for advanced information-rich bridge deterioration prediction. However, utilizing textual data for bridge deterioration prediction is challenging because of its inherently unstructured nature. To this end, this paper proposes an ontology-based information extraction (IE) framework that automatically recognizes and extracts key data/information from unstructured textual reports, and represents the extracted data/information in a structured way that is ready for data analytics. The proposed IE framework is composed of two primary components: (1) ontology-based sequence labelling for term identification, and (2) ontology-based dependency grammar for relationship association. This paper focuses on presenting the proposed sequence labelling methodology. The methodology utilizes ontology-based begin, inside, and outside (BIO) encoding for phrase-level segmentation and Conditional Random Field (CRF) for ontology-based labelling in both token and phrase levels. The experimental results showed that the proposed methodology has a precision of 97% and a recall of 91%.