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Joint triple Extraction for construction regulatory documents using graph neural networks
Citation Link: https://doi.org/10.15480/882.13507
Publikationstyp
Conference Paper
Date Issued
2024-09-18
Sprache
English
Author(s)
TORE-DOI
Start Page
10
End Page
17
Citation
35. Forum Bauinformatik, fbi 2024: 10-17
Contribution to Conference
Publisher
Technische Universität Hamburg, Institut für Digitales und Autonomes Bauen
Peer Reviewed
true
The construction industry depends on regulatory documents, to guarantee the consistency and applicability of their operations, which is crucial for maintaining standards and protocols. Information extraction is a time-consuming and labor-intensive process, which aims to extract the relevant data from documents for definite operation. Extracting information and assigning it to a particular rule requires understanding the semantics of the sentences, essential for ensuring code compliance. One approach to address this concern is to parse the unstructured text into triples by extracting entities and their relation. This process enables the identification of subjects, objects, and relationships that provide a comprehensive understanding of the sentence. This paper proposes a joint entity and relation extraction model based on a graph neural network. The model is trained on construction regulatory documents sourced from Eurocode for design. The dataset undergoes preprocessing and labeling in the form of triples to assess the model’s performance. The results demonstrate that the model can effectively predict the sentence triples. The model’s prediction of sentence triples indicates the ability to capture complex semantic relationships within textual data, which can be transformed into a comprehensive knowledge representation.
Subjects
Code Compliance
Graph neural networks
Information extraction
Joint entity recognition
Relation extraction
DDC Class
624: Civil Engineering, Environmental Engineering
005: Computer Programming, Programs, Data and Security
621.3: Electrical Engineering, Electronic Engineering
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Joint Triple Extraction for Construction Regulatory Documents using Graph Neural Networks.pdf
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