Keskeiset käsitteet
pyKCN, a Python toolkit, automates keyword cleaning, extraction, and trend analysis from academic corpora to visualize research trends and predict future directions.
Tiivistelmä
This content introduces pyKCN, a Python toolkit designed for analyzing research trends through keyword co-occurrence analysis. It covers the importance of understanding scientific development trends, the limitations of existing tools, and the significance of predicting research trends. The content details the architecture of pyKCN, its modules for text processing and network analysis, as well as its application in various fields like pain research, asset life cycle management, and AI-assisted vehicle maintenance. The article also acknowledges related work in scientometric reviews and provides references to studies utilizing pyKCN.
Directory:
- Introduction
- Literature reviews classified into research landscape analysis and detailed topical reviews.
- Automated tools increasingly needed in academic research.
- Related Work
- Comparison of literature review tools like VOSviewer and Connected Papers.
- Architecture and Core Functionality
- Overview of pyKCN's architecture with main modules like Logging System and File System.
- Data Extraction Modules
- BaseExtractor for foundational data extraction framework.
- Text Processing Modules
- BaseProcessor for data processing pipeline with methods for text transformation.
- Downstream Tasks: Pain Research
- Application of KCN methodology in pain research to analyze keywords from 264,560 articles.
- Downstream Tasks: Asset Life Cycle Management Research
- Exploration of Industry 4.0 technology applications in sustainable asset life cycle management using KCNs from 3,896 articles.
- Downstream Tasks: AI-assisted Vehicle Maintenance
- Analysis of keywords from 3153 papers on AI applications for vehicle maintenance using KCN methodology.
Tilastot
VOSviewer utilizes bibliometric data to create bibliometric network visualizations.
Connected Papers generates graphical maps showing relationships between papers in a field.
PDF.ai is an LLM-powered tool that extracts key findings from research papers.