核心概念
AI algorithms can revolutionize scientometrics, webometrics, and bibliometrics by automating data collection, enhancing analysis, improving accuracy, and providing deeper insights into research trends, collaborations, and impact.
摘要
This comprehensive review examines the potential of integrating artificial intelligence (AI) into the fields of scientometrics, webometrics, and bibliometrics. The key findings are:
Scientometrics:
AI can enhance publication analysis, citation analysis, research impact prediction, collaboration analysis, research trend analysis, and knowledge mapping.
AI algorithms can automate data collection, improve analysis accuracy, and provide deeper insights into scientific literature.
Webometrics:
AI can improve web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis, and recommender systems.
AI techniques can enhance the efficiency and accuracy of web-based data analysis, enabling researchers to gain insights into online user behavior and web-based scientific ecosystems.
Bibliometrics:
AI can automate data collection, improve citation analysis, facilitate author disambiguation, analyze co-authorship networks, assess research impact, and provide personalized recommendations.
AI algorithms can streamline bibliometric processes, increase reliability, and offer comprehensive evaluation of scholarly publications and their impact.
The review also discusses the future potential of these fields with AI, highlighting how advancements in AI can lead to more accurate, efficient, and insightful analyses. Additionally, it addresses the ethical considerations surrounding the use of AI in these domains, emphasizing the importance of data privacy, bias mitigation, transparency, and responsible implementation.
統計資料
AI algorithms can automate the identification, classification, and analysis of scientific literature, improving the efficiency and accuracy of data collection and analysis.
AI techniques can enhance web crawling and data collection, enabling researchers to gather larger and more diverse datasets for webometric analyses.
AI-powered algorithms can analyze large-scale bibliographic and citation databases to uncover patterns, trends, and relationships among scientific productions, facilitating evidence-based decision-making.
引述
"AI algorithms can analyze large volumes of scientific publications and extract valuable information, such as author and co-author names, affiliations, keywords, and citations."
"AI techniques can analyze citation networks to identify the impact and influence of scientific papers, as well as the relationships between different research works."
"AI can analyze collaboration networks among researchers in bibliometrics, helping to identify influential researchers, research groups, and institutions."