This text discusses the use of OpenAI and graph databases to build an academic knowledge graph. It focuses on the use of text embeddings, which are numerical representations of concepts in a vector space, to enable semantic search. The text explains how word embeddings capture the semantic relationships between words and documents, and how they can be used for various tasks such as search, clustering, recommendations, and anomaly detection. It also introduces the concept of cosine similarity for measuring similarity between vectors. The text provides code examples for generating embeddings using OpenAI API and performing semantic search using cosine similarity. Finally, it mentions the scalability challenges of working with large knowledge graphs and suggests future articles on indexing methods and graph algorithms.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Use of GPT-3 for word embeddings and semantic search | Implementation of GPT-3 for text embedding and semantic search | More advanced and efficient word embeddings and semantic search algorithms | Need for more accurate and relevant search results |
| Text embeddings for search, clustering, recommendations, anomaly detection, diversity measurement, and classification | Adoption of text embeddings for various applications | Improved search, clustering, recommendation, anomaly detection, diversity measurement, and classification algorithms | Enhanced understanding and analysis of text data |
| Advantages of word embeddings in semantic search | Shift from traditional text representations to word embeddings | More accurate and relevant search results based on semantic relationships between words | Desire for better search capabilities and results |
| Cosine similarity as a measure of similarity between word embeddings | Use of cosine similarity for comparing word embeddings | Continued use of cosine similarity for similarity measurement | Need for a standardized and effective similarity metric |
| Integration of OpenAI API and graph database for semantic search | Integration of OpenAI API and graph database for semantic search | Integration of AI-powered semantic search in more applications and industries | Desire for more efficient and accurate search capabilities |
| Graph Data Science (GDS) library for graph analytics | Use of GDS library for graph analytics | More advanced and efficient graph analytics algorithms and libraries | Need for better graph analysis and insights |
| Scalability and performance challenges with large data volumes | Development of scalable and efficient techniques for large-scale semantic search | Solutions for handling large data volumes and improving search performance | Demand for effective search capabilities in big data environments |