Futures

Building a Knowledge Graph in Neo4j Using Chat-GPT and Python for Article Content, (from page 20290911.)

External link

Keywords

Themes

Other

Summary

This article outlines the process of using Chat-GPT and Python to build a knowledge graph in Neo4j from personal articles, specifically over 120 articles related to mathematics and data science. It discusses the potential of NLP techniques and generative AI, particularly with LLMs like Chat-GPT, which have gained traction in various industries. The guide details a step-by-step approach that includes setting up OpenAI’s API, prompt engineering, downloading and pre-processing articles, extracting data with Chat-GPT, and structuring this data into a graph using Cypher. The author emphasizes the ease of starting this project and the exciting opportunities it presents for data exploration.

Signals

name description change 10-year driving-force relevancy
Increased accessibility to AI tools Amateurs can easily access AI technologies like Chat-GPT for content structuring. Change from limited access to AI tools for experts only to widespread use by amateurs. In 10 years, AI tools may become commonplace for content creation and analysis across various fields. The rapid development of user-friendly AI technologies encourages broader adoption among non-experts. 4
Growth of generative AI in businesses Companies are increasingly implementing generative AI technologies for various applications. Shift from AI being a niche technology to a standard tool in many business operations. In a decade, generative AI could be integral to business strategies and operations. The need for innovation and efficiency drives businesses to adopt generative AI solutions. 5
Emergence of knowledge graphs Knowledge graphs are becoming popular for structuring large amounts of data. Transition from traditional databases to more interconnected and structured data representations. In 10 years, knowledge graphs may dominate data organization practices across industries. The demand for better data visualization and relationship mapping boosts knowledge graph adoption. 4
Integration of NLP with programming NLP techniques are being increasingly integrated with programming languages like Python. From separate domains to a more cohesive integration of NLP within programming workflows. In 10 years, programming languages may have built-in NLP capabilities for easier data processing. The continuous improvement of NLP technologies fuels their integration with programming tools. 3

Concerns

name description relevancy
Data Privacy Concerns Using generative AI and APIs to extract data may lead to unauthorized use or exposure of personal information from articles. 5
Dependence on Proprietary Technology Reliance on OpenAI’s GPT-4 and APIs could create risks related to accessibility and cost barriers for users. 4
Quality Control of Generated Content The accuracy and reliability of structured knowledge from generative models can be inconsistent, affecting the integrity of information. 4
Misuse of Generative AI The ease of access to generative AI can lead to its misuse for creating misleading or harmful content. 5
Algorithmic Bias Generative models may perpetuate existing biases in the training data, leading to skewed or unfair knowledge representation. 5
Intellectual Property Issues The use of online articles for training may raise legal questions regarding ownership and copyright of generated outputs. 4
Technical Skills Gap There is a potential barrier for users lacking technical skills to effectively utilize AI and graph technologies, widening digital divides. 3

Behaviors

name description relevancy
Use of NLP for Data Structuring Increasing reliance on NLP techniques to convert unstructured data into structured formats for better analysis and understanding. 4
Accessibility of LLMs for Amateurs The rise of user-friendly tools like Chat-GPT enables non-experts to leverage advanced machine learning capabilities. 5
Integration of Generative AI in Business Growing adoption of generative AI technologies across various industries for content generation and data processing. 4
Programming and Data Exploration A trend towards using programming languages like Python for data manipulation and exploration in conjunction with graph technologies. 3
Knowledge Graph Development Emergence of knowledge graphs as a method to visualize and explore relationships in data derived from personal articles. 4
Prompt Engineering for AI Models The practice of refining prompts to improve the output quality from AI models like GPT-4. 4

Technologies

name description relevancy
Large Language Models (LLMs) Advanced AI models capable of understanding and generating human-like text, enabling various applications in natural language processing. 5
Generative AI AI systems that can create new content, including text, images, and more, based on learned patterns from existing data. 5
Knowledge Graphs A structured representation of knowledge that allows for efficient retrieval and exploration of information relationships. 4
Cypher Query Language A declarative graph query language for querying and updating graph databases, particularly Neo4j. 3
NLP Techniques Methods and technologies for processing and analyzing human language data, often used to convert unstructured data into structured formats. 4
OpenAI API An application programming interface that allows developers to access OpenAI’s AI models for various applications, including text generation. 5
Python Programming Language A versatile programming language commonly used in data science, AI, and web development, favored for its readability and ease of use. 4
Neo4j Database A graph database management system that enables storing and querying data in a graph format, facilitating complex relationships analysis. 4

Issues

name description relevancy
Accessibility of LLMs for Amateurs The rise of LLMs like Chat-GPT has democratized access to advanced NLP techniques for non-experts. 4
Generative AI in Business Generative AI is increasingly being integrated into business strategies, indicating a shift in operational paradigms. 5
Data Structuring through Graph Technology Utilizing graph technology for structuring article content presents new opportunities for knowledge management. 3
Prompt Engineering for AI Models The importance of prompt engineering in effectively utilizing AI models highlights a growing skill set for users. 4
Integration of NLP and Data Science The combination of NLP techniques and data science is emerging as a critical area for innovation and application. 5