The Construction of Knowledge Graph for Personalized Online Teaching

Gong, Zhaokun; Yu, Xiaomei; Fu, Wenxiang; Che, Xueyu; Mao, Qian; Zheng, Xiangwei

doi:10.1007/978-981-16-7502-7_11

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…Following this, semantic rules defined in the previous step are used to extract two or more entities from the relational data and establish matching semantic relationships. The Cypher Load CSV function [39] is employed within the neo4j graph database to convert the relational SPO ternary data into entity graph data.…”

Section: Step 6: Ontology Constructionmentioning

confidence: 99%

A Semantic Network Method for the Identification of Ship’s Illegal Behaviors Using Knowledge Graphs: A Case Study on Fake Ship License Plates

Wan,

Fu,

Zhang

et al. 2023

JMSE

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With the advancement of intelligent shipping, current traffic management systems have become inadequate to meet the requirements of intelligent supervision. In particular, with regard to ship violations, on-site boarding is still necessary for inspection. This paper presents a novel approach for enhancing ships’ management and service capabilities through scientific knowledge graph technology to develop a ship knowledge graph. The proposed approach extracts key characteristics of ship violations from the ship knowledge graph, such as monitoring ships, expired ship certificates, multiple ship tracks, inconsistent ship tracks with port reports, and ships not reported to the port for a long time. Combining the characteristics of ship violations, the approach uses reasoning and identification techniques to detect specific instances of falsely licensed ships and other violations. The development of the ship knowledge graph analysis system enables the identification and verification of illegal ships using fake license plates, while also improving the effective utilization of maritime data and enhancing the ability to make informed decisions related to ship safety. By leveraging cognitive approaches and knowledge graphs, this study offers the potential to develop an intelligent decision-making system for maritime traffic management.

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Section: Step 6: Ontology Constructionmentioning

confidence: 99%