Evidence map›Paper›PMID 35055616›Full record

ArticleInternational journal of environmental research and public health2022

Data-Driven Construction Safety Information Sharing System Based on Linked Data, Ontologies, and Knowledge Graph Technologies.

Akeem Pedro, Anh-Tuan Pham-Hang, Phong Thanh Nguyen, Hai Chien Pham

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Akeem PedroCenter for Systems Engineering and Innovation, Imperial College London, London SW7 2BX, UK.ORCID 0000-0002-7884-5316
Anh-Tuan Pham-HangSchool of Computer Science and Engineering, International University, Ho Chi Minh City 700000, Vietnam.ORCID 0000-0002-3950-5220
Phong Thanh NguyenDepartment of Project Management, Ho Chi Minh City Open University, Ho Chi Minh City 700000, Vietnam.ORCID 0000-0002-6180-2520
Hai Chien PhamApplied Computational Civil and Structural Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam.ORCID 0000-0001-7830-7967

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accident, injury, and fatality rates remain disproportionately high in the construction industry. Information from past mishaps provides an opportunity to acquire insights, gather lessons learned, and systematically improve safety outcomes. Advances in data science and industry 4.0 present new unprecedented opportunities for the industry to leverage, share, and reuse safety information more efficiently. However, potential benefits of information sharing are missed due to accident data being inconsistently formatted, non-machine-readable, and inaccessible. Hence, learning opportunities and insights cannot be captured and disseminated to proactively prevent accidents. To address these issues, a novel information sharing system is proposed utilizing linked data, ontologies, and knowledge graph technologies. An ontological approach is developed to semantically model safety information and formalize knowledge pertaining to accident cases. A multi-algorithmic approach is developed for automatically processing and converting accident case data to a resource description framework (RDF), and the SPARQL protocol is deployed to enable query functionalities. Trials and test scenarios utilizing a dataset of 200 real accident cases confirm the effectiveness and efficiency of the system in improving information access, retrieval, and reusability. The proposed development facilitates a new "open" information sharing paradigm with major implications for industry 4.0 and data-driven applications in construction safety management.

Indexed as

Biological OntologiesSemantic WebInformation DisseminationPattern Recognition, AutomatedSemanticsaccident preventionconstruction safetydata-driveninformation sharingknowledge engineeringknowledge graphknowledge managementlinked dataontologysemantic web

Identifiers

PMID35055616
PMCPMC8776242

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.