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

Kaijian Liu, Nora El-Gohary

Research output: Contribution to journalConference article


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%.

Original languageEnglish (US)
Pages (from-to)504-510
Number of pages7
JournalProcedia Engineering
StatePublished - Jan 1 2016
EventInternational Conference on Sustainable Design, Engineering and Construction, ICSDEC 2016 - Tempe, United States
Duration: May 18 2016May 20 2016


  • Bridge deterioration prediction
  • Information extraction
  • Infrastructure system data analytics
  • Ontology
  • Sequence labelling

ASJC Scopus subject areas

  • Engineering(all)

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