Construction and Application of Knowledge Graph for Bridge Inspection

Luo, Mengting; Yang, Xiaoxia; Zhang, Hongyi; Zhang, Yue; Ji, Lin; Li, Ren

doi:10.1109/itaic54216.2022.9836776

Cited by 4 publications

(5 citation statements)

References 3 publications

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“…Te proposed hybrid method is entirely unrelated to specifc concepts and entities of the BMKG. Hence, the proposed method has better generality than the existing construction method [22] of KGs in bridge domain.…”

Section: Construction Methodmentioning

confidence: 99%

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Building a Knowledge Base of Bridge Maintenance Using Knowledge Graph

Zhang

Liu

Hou

2023

Advances in Civil Engineering

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To effectively manage the heterogeneous and discrete knowledge of the bridge maintenance domain, this study adopts knowledge graph technology to build a knowledge base of bridge maintenance, called the bridge maintenance knowledge graph (BMKG). The BMKG uses an ontology as the knowledge organization and representation framework and a graph database as the knowledge storage tool. To facilitate the construction of the BMKG, a hybrid method combining a top-down approach and a bottom-up approach is proposed. Firstly, a bridge maintenance domain ontology (BMDO) is coded with Protégé and represented in Web Ontology Language. Secondly, rule reasoning and ontology reasoning are implemented on the BMDO in Protégé in order to automatically complete missing relations or attribute values. Thirdly, ontology reasoning is adopted to perform consistency check on the BMDO. Lastly, the BMDO model is stored in the Neo4j graph database through data format conversion, thus completing the construction of the BMKG. The BMKG is applied in a typical scenario of bridge maintenance to demonstrate its application value. Results show that the proposed hybrid method can create a knowledge graph that can realize the transformation from discrete data into interconnected knowledge. Knowledge graph offers a novel idea to create a knowledge base in the bridge maintenance domain.

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Section: Construction Methodmentioning

confidence: 99%

“…Tiwary et al [21] proposed a KG framework for monitoring and analysis of bridges. Luo et al [22] constructed a Chinese bridge inspection knowledge graph. Te construction method they proposed is directly related to bridge component, which are specifc entities of the KG they constructed.…”

Section: Introductionmentioning

confidence: 99%

Building a Knowledge Base of Bridge Maintenance Using Knowledge Graph

Zhang

Liu

Hou

2023

Advances in Civil Engineering

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“…This new algorithm is built on top of an existing algorithm (presented in the paper [4]), which computes Connected Components in graphs. The fundamental idea is to calculate 2-ECCs by finding all the bridges [15] (Bridge is explained in Background Survey section of this paper) in a graph and dropping those bridges one by one in each iteration until there are no more bridges left in the original graph. This is explained in great detail in the algorithm section of this manuscript.…”

Section: Our Proposed Approach In Briefmentioning

confidence: 99%

“…An algorithm to find connected components in big graphs in parallel [4] is extended to find 2-ECCs [3]. The fundamental idea behind the proposed approach is that a 2-edge connected component is also a connected component without bridges [15]. The proposed algorithm is implemented on top of the Hadoop [28] [7] framework.…”

Section: Algorithm Proposal and Illustrationmentioning

confidence: 99%

MapReduce for Graphs Processing: New Big Data Algorithm for 2-Edge Connected Components and Future Ideas

Dahiphale¹

2023

Preprint

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<p>Finding connectivity in graphs has numerous applications, such as social network analysis, data mining, intra-city or inter-cities connectivity, neural network, and many more. A deluge of graph applications makes graph connectivity problems extremely important and worthwhile to explore. Currently, there are many single-node algorithms for graph mining and analysis; however, those algorithms primarily apply to small graphs and are implemented on a single machine node. Finding 2-Edge Connected Components (2-ECCs) in massive graphs (billions of edges and vertices) is impractical and time-consuming, even with the best-known single-node algorithm. Processing a big graph in a parallel and distributed fashion will save considerable time to finish processing. Moreover, it enables stream data processing by allowing quick results for vast and continuous nature data sets. In this research, we propose a distributed and parallel algorithm for finding 2-ECCs in big undirected graphs (subsequently called ”BiECCA”) and present its time complexity analysis. The proposed algorithm is implemented on a MapReduce framework. The proposed algorithm uses an existing algorithm to find Connected Components (CCs) in a graph as a sub- step. Finally, we suggest a few novel ideas and approaches as extensions to our work. </p>

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“…This new algorithm is built on top of an existing algorithm (presented in the paper [4]), which computes connected components in graphs. The fundamental idea is to calculate 2-ECCs by finding all the bridges [16] (Bridge is explained in the Background Survey section of this paper) in a graph and dropping those bridges one by one in each iteration until there are no more bridges left in the original graph. This is explained in great detail in the algorithm section of this manuscript.…”

Section: Our Contributionmentioning

confidence: 99%

MapReduce for Graphs Processing: New Big Data Algorithm for 2-Edge Connected Components and Future Ideas

Dahiphale

2023

IEEE Access

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Finding connectivity in graphs has numerous applications, such as social network analysis, data mining, intra-city or inter-cities connectivity, neural network, and many more. The deluge of graph applications makes graph connectivity problems extremely important and worthwhile to explore. Currently, there are many single-node algorithms for graph mining and analysis; however, those algorithms primarily apply to small graphs and are implemented on a single machine node. Finding 2-edge connected components (2-ECCs) in massive graphs (billions of edges and vertices) is impractical and time-consuming, even with the best-known single-node algorithms. Processing a big graph in a parallel and distributed fashion saves considerable time to finish processing. Moreover, it enables stream data processing by allowing quick results for vast and continuous nature data sets. This research proposes a distributed and parallel algorithm for finding 2-ECCs in big undirected graphs (subsequently called ''BiECCA ′′ ) and presents its time complexity analysis. The proposed algorithm is implemented on a MapReduce framework and uses an existing algorithm to find connected components (CCs) in a graph as a sub-step. Finally, we suggest a few novel ideas and approaches as extensions to our work.

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Construction and Application of Knowledge Graph for Bridge Inspection

Cited by 4 publications

References 3 publications

Building a Knowledge Base of Bridge Maintenance Using Knowledge Graph

Building a Knowledge Base of Bridge Maintenance Using Knowledge Graph

MapReduce for Graphs Processing: New Big Data Algorithm for 2-Edge Connected Components and Future Ideas

MapReduce for Graphs Processing: New Big Data Algorithm for 2-Edge Connected Components and Future Ideas

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