Research on the Construction of a Knowledge Graph and Knowledge Reasoning Model in the Field of Urban Traffic

Tan, Jun; Qiu, Qianqian; Guo, Weiwei; Li, Tingshuai

doi:10.3390/su13063191

Cited by 40 publications

(18 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The data exchange of heterogeneous databases inevitably involves the link between databases. This article mainly uses ODBC (Open Database Connectivity) technology to link the databases 14 . The use of ODBC can flexibly realize the access of heterogeneous database data, which has become an indispensable technology for data sharing in system data switching, and the use of ODBC can obtain strong application independence.…”

Section: Data Switching Methods Between Heterogeneous Power Informati...mentioning

confidence: 99%

“…This article mainly uses ODBC (Open Database Connectivity) technology to link the databases. 14 The use of ODBC can flexibly realize the access of heterogeneous database data, which has become an indispensable technology for data sharing in system data switching, and the use of ODBC can obtain strong application independence. When the system needs to change the server or upgrade the software and hardware of the database, the client application only needs to make fewer changes.…”

Section: Links Between Databases Of Heterogeneous Power Information S...mentioning

confidence: 99%

See 1 more Smart Citation

Data switching method among heterogeneous power information system databases based on knowledge graph

Bao

Tian

Gao

2022

Int J Numerical Modelling

View full text Add to dashboard Cite

In order to solve the problems of long data switching time, low information utilization rate, and low switching accuracy in traditional data switching methods, a data switching method between heterogeneous power information system databases based on knowledge graphs is proposed. Based on the knowledge graph technology and its ternary structure, the data of the heterogeneous power information system is processed, and the N‐Gram model is used to realize the knowledge extraction, and the data error correction is established through the model. Through ODBC technology to complete the link between heterogeneous power information system databases, and then select the data access interface and determine the data extraction task, and finally realize the data exchange between the heterogeneous power information system databases. Experimental results show that the average communication channel bandwidth during the data switching process is 1.8 Gb/s, the highest value of data switching accuracy reaches 82%, and the information utilization rate after data switching is relatively high, indicating that the method has certain effectiveness.

show abstract

Section: Data Switching Methods Between Heterogeneous Power Informati...mentioning

confidence: 99%

Section: Links Between Databases Of Heterogeneous Power Information S...mentioning

confidence: 99%

Data switching method among heterogeneous power information system databases based on knowledge graph

Bao

Tian

Gao

2022

Int J Numerical Modelling

View full text Add to dashboard Cite

show abstract

“…The entity linked knowledge graph is integrated with data extracted from medical web forums showing the impact of social networks' users experiences on enriching the knowledge graph. This integrated knowledge graph aims to be used as a knowledge base for any intelligent healthcare system that could predict diseases or give alerts whenever any disease ailments are detected [11,12]. The system could be used by social networks users and medical professionals, taking into consideration user experiences [15].…”

Section: Weighted Entity Linking and Integration Algorithm For Medica...mentioning

confidence: 99%

Weighted Entity-Linking and Integration Algorithm for Medical Knowledge Graph Generation

Maghawry

Ghoniemy²,

shaaban³

et al. 2023

IJICIS

View full text Add to dashboard Cite

Semantic data integration is the process of interrelating information from multiple heterogeneous resources. There is a need for representing data concepts and their relationships to eliminate heterogeneity among different data sources in healthcare management systems. Standardized medical ontologies provide predefined medical vocabulary serving as a stable interface for concepts related to medical data sources. However, different ontologies have different concepts although these concepts have logical relations between them such as the Human Disease Ontology and the Symptoms ontology. There aroused a need for a knowledge graph providing a reliable knowledge base for any intelligent healthcare expert advisor disease prediction system. The knowledge graph provides a model for linking and integrating different concepts having logical relationships such as diseases and their symptoms. Medical online website and encyclopedia provides a reliable source for building such a knowledge graph. The knowledge graph is enriched with social networks data where information extracted reflects a major source of data based on user experiences. The paper proposes a framework for constructing a disease-symptom entity linked knowledge graph based on online medical encyclopedia and social networks user experiences. Entity linking such an integrated knowledge graph with standardized medical ontologies makes it a reliable knowledge base for a standard system that could be used by social networks user and the professional staff.

show abstract

“…Wang Lu [31] constructed a traffic knowledge graph to represent the relationship between roads according to the knowledge graph and embed entities in a low-dimensional space to achieve destination prediction. Tan et al [32] used a top-down approach to construct a knowledge graph of the urban transportation systems, using the pattern layer of the knowledge graph to achieve knowledge reuse and sharing, completing knowledge complementation based on knowledge inference, and mining the implicit relationships between traffic entities. Wickramarachchi et al [33] argued that the field of autonomous driving was exploring knowledge graphs to manage the large amount of heterogeneous data generated from autonomous vehicles, including data from sensors such as LiDAR.…”

Section: Application Of Knowledge Graph In Automated Driving Systemsmentioning

confidence: 99%

Pedestrian intention estimation and trajectory prediction based on data and knowledge‐driven method

Zhou,

Bai,

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

With the development of deep learning technology, the problem of data‐driven trajectory prediction and intention recognition has been widely studied. However, the pedestrian trajectory prediction and intention recognition methods based solely on data‐driven have weak data description ability and black‐box characteristics, which cannot reason about pedestrian crossing intention and predict pedestrian crossing trajectory as humans do. To address the above problems, the authors proposed a data and knowledge‐driven pedestrian intention estimation and trajectory prediction method by imitating human cognitive mechanisms. In the pedestrian intention inference process, the authors adopted the knowledge‐driven method. As a first step, the authors built a knowledge graph of pedestrian crossing scenes, and then paired it with a Bayesian network to estimate pedestrian crossing intentions. In the pedestrian trajectory prediction process, the authors used a data‐driven approach, combining pedestrian crossing trajectory features and knowledge‐based pedestrian intentions. Experiments show that all evaluation metrics of pedestrian trajectory prediction were improved after adding pedestrian intentions obtained by knowledge‐driven.

show abstract

Research on the Construction of a Knowledge Graph and Knowledge Reasoning Model in the Field of Urban Traffic

Cited by 40 publications

References 31 publications

Data switching method among heterogeneous power information system databases based on knowledge graph

Data switching method among heterogeneous power information system databases based on knowledge graph

Weighted Entity-Linking and Integration Algorithm for Medical Knowledge Graph Generation

Pedestrian intention estimation and trajectory prediction based on data and knowledge‐driven method

Contact Info

Product

Resources

About