Intelligent bridge management via big data knowledge engineering

Yang, Jianxi; Xiang, Fangyue; Ren, Li; Zhang, Luyi; Yang, Xiaoxia; Jiang, Shixin; Zhang, Hongyi; Wang, Di; Fan, Haosen

doi:10.1016/j.autcon.2021.104118

Cited by 29 publications

(19 citation statements)

References 70 publications

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“…The research on domain text information extraction provides technical support for the construction of bridge management knowledge graph. Yang et al [ 45 ] proposed a novel BigKE-based intelligent bridge management and maintenance framework according to the big data knowledge engineering paradigm, pointing out the direction for bridge management knowledge services. However, the above-mentioned work is partial to theoretical research and technical preparation.…”

Section: Related Workmentioning

confidence: 99%

Complex Knowledge Base Question Answering for Intelligent Bridge Management Based on Multi-Task Learning and Cross-Task Constraints

Yang

et al. 2022

Entropy

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In the process of bridge management, large amounts of domain information are accumulated, such as basic attributes, structural defects, technical conditions, etc. However, the valuable information is not fully utilized, resulting in insufficient knowledge service in the field of bridge management. To tackle these problems, this paper proposes a complex knowledge base question answering (C-KBQA) framework for intelligent bridge management based on multi-task learning (MTL) and cross-task constraints (CTC). First, with C-KBQA as the main task, part-of-speech (POS) tagging, topic entity extraction (TEE), and question classification (QC) as auxiliary tasks, an MTL framework is built by sharing encoders and parameters, thereby effectively avoiding the error propagation problem of the pipeline model. Second, cross-task semantic constraints are provided for different subtasks via POS embeddings, entity embeddings, and question-type embeddings. Finally, using template matching, relevant query statements are generated and interaction with the knowledge base is established. The experimental results show that the proposed model outperforms compared mainstream models in terms of TEE and QC on bridge management datasets, and its performance in C-KBQA is outstanding.

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Section: Related Workmentioning

confidence: 99%

Complex Knowledge Base Question Answering for Intelligent Bridge Management Based on Multi-Task Learning and Cross-Task Constraints

Yang

et al. 2022

Entropy

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“…Extensible markup language (XML) has been widely used as the data exchange format over the internet [1], [2]. Big data analytics is the trend in various industries to boost their industrial performance, and in fact, XML data format usually forms the basis of data streaming used in the analytical process [3], [4]. However, XML data represent the data only at the syntactic level.…”

Section: Introductionmentioning

confidence: 99%

Mapping of extensible markup language-to-ontology representation for effective data integration

Haw

Chew²,

Kusumo³

et al. 2023

IJ-AI

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<span lang="EN-US">Extensible markup language (XML) is well-known as the standard for data exchange over the Internet. It is flexible and has high expressibility to express the relationship between the data stored. Yet, the structural complexity and the semantic relationships are not well expressed. On the other hand, ontology models the structural, semantic and domain knowledge effectively. By combining ontology with visualization effect, one will be able to have a closer view based on respective user requirements. In this paper, we propose several mapping rules for the transformation of XML into ontology representation. Subsequently, we show how the ontology is constructed based on the proposed rules using the sample domain ontology in University of Wisconsin-Milwaukee (UWM) and mondial datasets. <br /> We also look at the schemas, query workload, and evaluation, to derive the extended knowledge from the existing ontology. The correctness of the ontology representation has been proven effective through supporting various types of complex queries in simple protocol and resource description framework query language (SPARQL) language.</span>

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“…Tese systems or knowledge bases are developed independently and deployed separately. As a result, a heterogeneous semantics problem exists among these knowledge bases due to the design diferences of RDB [10], which can cause bridge maintenance knowledge to be hard to share and reuse. It is difcult for bridge engineers to integrate the scattered knowledge required to make bridge maintenance decisions in the absence of efective tools.…”

Section: Introductionmentioning

confidence: 99%

“…Fang et al [19] applied KG to identifying hazards on construction sites. However, due to the complexity of KG, there are only a few research results relevant to KG in the bridge engineering domain [10]. For example, Ma et al [20] thought of KG as a future vision for a standardized database of fatigue cracks on steel box girders.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Ma et al [20] thought of KG as a future vision for a standardized database of fatigue cracks on steel box girders. Yang et al [10] viewed KG as the core of the intelligent bridge management and maintenance framework based on big data knowledge engineering. Tiwary et al [21] proposed a KG framework for monitoring and analysis of bridges.…”

Section: Introductionmentioning

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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Intelligent bridge management via big data knowledge engineering

Cited by 29 publications

References 70 publications

Complex Knowledge Base Question Answering for Intelligent Bridge Management Based on Multi-Task Learning and Cross-Task Constraints

Complex Knowledge Base Question Answering for Intelligent Bridge Management Based on Multi-Task Learning and Cross-Task Constraints

Mapping of extensible markup language-to-ontology representation for effective data integration

Building a Knowledge Base of Bridge Maintenance Using Knowledge Graph

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