Relationship Extraction with GPT-3
Accelerate knowledge graph construction with GPT-3
In my previous articles (1, 2, and 3), I have demonstrated that GPT-3 from OpenAI is a game-changing Natural Language Understanding (NLU) engine. GPT-3 is easy to learn and easy to repurpose. It is highly accurate in both content and format. In the case of Doctor.ai, compared to AWS Lex, GPT-3 delivers better results with far less learning and development time.
But GPT-3 is more than a chatbot. It can do keyword and relationship extractions, too. This is especially important for knowledge graph construction. I have shown in my previous article how to integrate public knowledge graphs into Doctor.ai, but it takes time for these public sources to incorporate the newest research results. So in order to keep Doctor.ai always up-to-date, we need a fast and automatic way to extract relationships — gene regulations, drug interactions and protein interactions — from research articles ourselves. You can imagine my excitement when I found out that GPT-3 also excelled at this task.
And more importantly, GPT-3 does not just mechanically extract the relationships. It has a good semantic understanding. It does the necessary noun-verb conversion and entity expansion, too. For example, “upregulation of A by B” can be correctly transformed into B,upregulate,A. Or “A…
