Information extraction from automotive reports for ontology population - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Applied Ontology Année : 2024

Information extraction from automotive reports for ontology population

Résumé

In this paper, we showcase our research on the use of ontologies and information extraction for the purpose of modeling damages incurred on car bodies. With the increasing use of technology in the automotive industry, it is important to have a standardized and efficient way of documenting and analyzing car damage reports. Most existing reports are unstructured, and there is a lack of standardization in describing the damage. To address this issue, we have developed a domain ontology for car damage modeling (OCD),11 industryportal.enit.fr/ontologies/OCD,22 github.com/OntologyCarDamage/OCD and proposed an end-to-end system to extract information from French automotive reports. The information extraction process involves using named entity recognition (NER) and relationship extraction (RE) techniques to identify and extract relevant information from the reports. Then, the extracted information is used to populate the OCD ontology, allowing a structured and standardized representation of the damage information. The proposed system was tested on a real dataset of automotive reports and showed promising results.
Fichier non déposé

Dates et versions

hal-04479466 , version 1 (27-02-2024)

Identifiants

Citer

Hamid Ahaggach, Lylia Abrouk, Eric Lebon. Information extraction from automotive reports for ontology population. Applied Ontology, 2024, pp.1-30. ⟨10.3233/AO-230002⟩. ⟨hal-04479466⟩
8 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More