Ontologies-Based Domain Knowledge Modeling and Heterogeneous Sensor Data Integration for Bridge Health Monitoring Systems

Ren, Li; Mo, Tianjin; Yang, Jianxi; Jiang, Shixin; Li, Tong; Liu, Yiming

doi:10.1109/tii.2020.2967561

Cited by 37 publications

(21 citation statements)

References 35 publications

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“…Another approach by Li et al [ 7 ], introduced a novel model of data integration for monitoring the bridge conditions based on sensor data stored in MySQL databases. This model was labeled as structural health monitoring systems and was implemented on an actual bridge SHM big data platform in Ningxia, China (taking 2 bridges A and B).…”

Section: Related Workmentioning

confidence: 99%

Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19

Thirumahal¹,

Sadasivam²,

Shruti³

2022

SN COMPUT. SCI.

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The enormous outbreak of biomedical knowledge, the aim of reducing computation and processing costs and the widespread availability of internet connection have created a profuse amount of electronic data. Such data are stored across the globe in various data sources that are semantically, structurally and syntactically different. This decentralized nature of biomedical data has made it difficult to obtain a unified view of the data. Data integration plays a crucial role in enhancing access to heterogeneous data making the retrieval easier and faster. A variety of ontology, machine learning, deep learning and fuzzy logic-based solutions are being developed for heterogeneous data integration. The proposed model concentrates on the automatic ontology-based data integration method that can be effectively deployed and used in the healthcare domain. The proposed model is divided into three phases. The first phase includes the automatic mapping of data and generation of local ontology across heterogeneous data sources, the second phase combines the local ontology models developed in the first phase to create a root global schema mapping and the third phase queries diverse databases to retrieve semantically analogous records. The model is created based on the medical records, chest X-ray details and COVID-19 symptom questionnaire data of various patients distributed across three data sources (SQL, mongodb and excel). Based on the data, the patients who have moderate/higher risk of developing serious illness from COVID-19 are retrieved.

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Section: Related Workmentioning

confidence: 99%

Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19

Thirumahal¹,

Sadasivam²,

Shruti³

2022

SN COMPUT. SCI.

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“…Furthermore, our previous research laid the foundation for bridge management C-KBQA. For example, the previous work [ 42 ] constructed bridge structure and health monitoring ontology using Semantic Web technology, and realized multi-angle fine-grained modeling of bridge structure, SHM system, sensor, and perception data, which lays a foundation for the semantic ontology construction of bridge maintenance. With the in-depth research, Li et al [ 43 ] proposed a dictionary-enhanced machine reading comprehension NER neural model for identifying planes and nested entities from Chinese bridge detection texts.…”

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

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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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“…In another recent example of an ontology-based approach to predictive maintenance, fuzzy clustering is employed to infer the criticality of failures, while SWRL rules are employed for predictive reasoning for the transition between states of different criticality, without though applying context-specific modeling an reasoning (Cao et al, 2019). Ontological approaches to support maintenance management that employ industrial scenarios have been developed for a range of assets, including urban infrastructure (Wei et al, 2020), highway infrastructure (France-Mensah and O'Brien, 2019), Building Information Management (BIM) (Farghaly et al, 2019), transport infrastructure (Ren et al, 2019;Li et al, 2020), and railway infrastructure (Dimitrova et al, 2020). Table 5 summarizes of ontologies in maintenance and asset management.…”

Section: Ontologies In Predictive Maintenance and Asset Managementmentioning

confidence: 99%

Ontology-Based Context Modeling in Physical Asset Integrity Management

Al-Shdifat

Emmanouilidis

Khan

et al. 2020

Front. Comput. Sci.

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Asset management is concerned with the management practices, technologies and tools necessary to maximize the value delivered by physical engineering assets. IoT-generated data are increasingly considered as an asset and the data asset value needs to be maximized too. However, asset-generated data in practice are often collected in non-actionable form. Collected data may comprise a wide number of parameters, over long periods of time and be of significant scale. Yet they may fail to represent the range of possible scenarios of asset operation or the causal relationships between the monitored parameters, and so the size of the data collection, while adding to the complexity of the problem, does not necessarily allow direct data asset value exploitation. One way to handle data complexity is to introduce context information modeling and management, wherein data and service delivery are determined upon resolving the apparent context of a service or data request. The aim of the present paper is, therefore, 2-fold: to analyse current approaches to addressing IoT context information management, mapping how context-aware computing addresses key challenges and supports the delivery of monitoring solutions; and to develop a maintenance context ontology focused on failure analysis of mechanical components so as to drive monitoring services adaptation. The approach is demonstrated by applying the ontology on an industrially relevant physical gearbox test rig, designed to model complex misalignment cases met in manufacturing and aerospace applications.

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Ontologies-Based Domain Knowledge Modeling and Heterogeneous Sensor Data Integration for Bridge Health Monitoring Systems

Cited by 37 publications

References 35 publications

Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19

Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19

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

Ontology-Based Context Modeling in Physical Asset Integrity Management

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