Systematic Knowledge Management of Construction Safety Standards Based on Knowledge Graphs: A Case Study in China

Jiang, Yukun; Gao, Xin; Su, Wenxin; Li, Jinrong

doi:10.3390/ijerph182010692

Cited by 20 publications

(6 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most recently, Jiang et al [26] developed a knowledge graph to facilitate the analysis, querying, and sharing of the knowledge of construction safety management standards by analyzing the content of existing safety standards. The schema of the knowledge graph was developed based on 218 standards, and they found that the resultant knowledge graph allowed the retrieval of relevant construction safety management standards via natural language processing.…”

Section: Ontologies For Construction Safety Managementmentioning

confidence: 99%

Development of a Knowledge Graph for Automatic Job Hazard Analysis: The Schema

Pandithawatta

Ahn

Rameezdeen

et al. 2023

Sensors

View full text Add to dashboard Cite

In the current practice, an essential element of safety management systems, Job Hazard Analysis (JHA), is performed manually, relying on the safety personnel’s experiential knowledge and observations. This research was conducted to create a new ontology that comprehensively represents the JHA knowledge domain, including the implicit knowledge. Specifically, 115 actual JHA documents and interviews with 18 JHA domain experts were analyzed and used as the source of knowledge for creating a new JHA knowledge base, namely the Job Hazard Analysis Knowledge Graph (JHAKG). To ensure the quality of the developed ontology, a systematic approach to ontology development called METHONTOLOGY was used in this process. The case study performed for validation purposes demonstrates that a JHAKG can operate as a knowledge base that answers queries regarding hazards, external factors, level of risks, and appropriate control measures to mitigate risks. As the JHAKG is a database of knowledge representing a large number of actual JHA cases previously developed and also implicit knowledge that has not been formalized in any explicit forms yet, the quality of JHA documents produced from queries to the database is expectedly higher than the ones produced by an individual safety manager in terms of completeness and comprehensiveness.

show abstract

Section: Ontologies For Construction Safety Managementmentioning

confidence: 99%

Development of a Knowledge Graph for Automatic Job Hazard Analysis: The Schema

Pandithawatta

Ahn

Rameezdeen

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The construction results for relevant entities and relationships are shown in Table 2. The setting can be found in article (7) of Section 3.1. The OBD fault code consists of one letter and four digits.…”

Section: Expanded Knowledgementioning

confidence: 99%

“…( 6) Querying the existence of relationships. (7) Querying the relationship type between entities. (8) Multi-level relationship query.…”

Section: Query Implementation 331 Query Implementation Based On Cyphermentioning

confidence: 99%

See 1 more Smart Citation

Architecture and Application of Traffic Safety Management Knowledge Graph Based on Neo4j

2023

View full text Add to dashboard Cite

A large amount of traffic safety information has been generated. This will further promote the sustainable development of transport. However, its content, form, and structure are complex and scattered, lacking effective information integration and a comprehensive framework. Combined with the concept of safety analysis, a traffic safety management knowledge graph was designed for structured data, which include 54 types of node entities and 14 types of relationship entities. Six types of information were collected and imported, including illegal acts, vehicle failure, emergency response, legal norms, organization information, and road-related information. Ultimately, a knowledge query function was realized using Cypher, and an automatic Q&A function was created based on rule matching. A traffic accident knowledge graph was constructed for unstructured data, with people and institutions involved, vehicles involved, and accidents as the core, including 21 types of node entities and 22 types of relationship entities. Comparing the node entity extraction performance of Bert, Bert-CRF, Bert-BiLSTM, and Bert-BiLSTM-CRF models, Bert BiLSTM-CRF performs the best. The Bert model was used for relationship entity extraction. The traffic accident knowledge graph can structurally display accident information and support a query function to facilitate safety analysis.

show abstract

“…In AEC contexts, linked data and graph-based technologies can boost digitalization and knowledge creation [ 40 ], and a few studies have explored these potentials. Research efforts have developed semantic web- and graph-based applications to improve processes in quality management and defect prevention [ 16 ], construction procurement [ 41 ], asset management [ 42 ], building energy performance management [ 43 ], knowledge management [ 44 ], and look-ahead planning [ 45 ]. While linked data have been used to improve interoperability and link across domains in AEC research [ 46 ], scholarly efforts have barely delved into the potentials for construction safety information sharing.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Pedro

Pham-Hang

Nguyen

et al. 2022

IJERPH

View full text Add to dashboard Cite

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.

show abstract

Systematic Knowledge Management of Construction Safety Standards Based on Knowledge Graphs: A Case Study in China

Cited by 20 publications

References 51 publications

Development of a Knowledge Graph for Automatic Job Hazard Analysis: The Schema

Development of a Knowledge Graph for Automatic Job Hazard Analysis: The Schema

Architecture and Application of Traffic Safety Management Knowledge Graph Based on Neo4j

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

Contact Info

Product

Resources

About