Migration knowledge graph framework and its application

Pu, Tongzheng; Huang, Minjuan; Yang, Jingjing

doi:10.1088/1742-6596/1955/1/012071

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The detected vicious cycles and the root problems were estimated using SPARQL queries and evaluated with domain experts (Egami et al 2021). Pu et al (2021) used a spatiotemporal migration knowledge graph framework based on the Global Migration Dataset and the Baidu Encyclopedia to "[…] quickly and accurately obtain the comprehensive knowledge of migration, significantly improve the ability of human being to apply and analyse the knowledge and data of migration, which has wide theoretical and practical value in the field of migration." (Pu et al 2021).…”

Section: Relevant Approaches In the Field Of Data Analysismentioning

confidence: 99%

Sustainable and Equitable Land Management

2024

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This new volume of European Academy of Land Use and Development (EALD) contains broad view and interdisciplinary peer reviewed articles that inform the reader of most recent scientific investigations in Land Management. The spectrum of contributions cover: regulations, governance and implementation of land management along with assessment of relevant data. Some of the key highlights include various scientific and practical approaches, applied methods and systems together with a discussion and understanding of the government’s role in various European countries. Committed to sustainability, the articles entailed give evidence to Europe as an experimental ground for land management issues as well as allows for collaboration in pursuit of best practices.

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Section: Relevant Approaches In the Field Of Data Analysismentioning

confidence: 99%

Sustainable and Equitable Land Management

2024

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“…Scholars use the vocabulary provided by the semantic network to achieve short text understanding, word segmentation, type annotation, and concept labeling [3]. Knowledge graph technology has made remarkable achievements in medicine, film and television, transportation, and other fields, but there is still a lot of work to be done in the field of social networking [23,24].…”

Section: Related Workmentioning

confidence: 99%

Cross-Domain Transfer Learning Prediction of COVID-19 Popular Topics Based on Knowledge Graph

Chen

Wei

et al. 2022

Future Internet

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The significance of research on public opinion monitoring of social network emergencies is becoming increasingly important. As a platform for users to communicate and share information online, social networks are often the source of public opinion about emergencies. Considering the relevance and transmissibility of the same event in different social networks, this paper takes the COVID-19 outbreak as the background and selects the platforms Weibo and TikTok as the research objects. In this paper, first, we use the transfer learning model to apply the knowledge obtained in the source domain of Weibo to the target domain of TikTok. From the perspective of text information, we propose an improved TC-LDA model to measure the similarity between the two domains, including temporal similarity and conceptual similarity, which effectively improves the learning effect of instance transfer and makes up for the problem of insufficient sample data in the target domain. Then, based on the results of transfer learning, we use the improved single-pass incremental clustering algorithm to discover and filter popular topics in streaming data of social networks. Finally, we build a topic knowledge graph using the Neo4j graph database and conduct experiments to predict the evolution of popular topics in new emergencies. Our research results can provide a reference for public opinion monitoring and early warning of emergencies in government departments.

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Migration knowledge graph framework and its application

Cited by 2 publications

References 11 publications

Sustainable and Equitable Land Management

Sustainable and Equitable Land Management

Cross-Domain Transfer Learning Prediction of COVID-19 Popular Topics Based on Knowledge Graph

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