Why should you combine ChatGPT with Knowledge Graphs?

ChatGPT, a language model developed by OpenAI, has revolutionised the field of natural language generation with its ability to generate human-like text. However, like any machine learning model, it has its limitations.

One of the limitations is that it does not always fully understand the context and background knowledge of the text it generates.

For example, if the model is asked to write about a specific topic, it may generate a grammatically correct text, but it may lack the depth and nuance of an expert in the field. Another limitation is the limited understanding of logical relationships between different concepts and facts. While the language model can produce a coherent text, it is unable to draw independent conclusions based on the information provided to it.

A knowledge graph is a type of database that stores and organises information in a way that reflects the relationships between different pieces of data. Compared to traditional relational databases, this allows for a more accurate and intuitive representation of real-world concepts and their connections and enables easy access and queries.

By connecting knowledge graphs to language models such as ChatGPT, the model can access a wealth of background information and context, allowing it to generate text with a greater depth of understanding. In addition, knowledge graphs provide a way to establish logical connections between concepts. By linking concepts in a structured way, the language model can use this information to draw more informed conclusions, adding an extra layer of intelligence to text generation.

In addition, a knowledge graph can provide ChatGPT with accurate information that it can then incorporate into its text generation. This improves the quality of the content generated by providing specific and accurate information on a given topic.

Furthermore, knowledge graph can provide accurate information to the OpenAI model, which can then incorporate that information in its text generation. It can provide specific and accurate information about any subject, making the text generated more informative and useful.

In conclusion, while ChatGPT is a powerful language model, it has limitations in terms of context, background knowledge and reasoning. Combining it with data from knowledge graphs can help overcome these limitations, resulting in a more intelligent and informative text generation system.

By combining ChatGPT with knowledge graphs, the strengths of both technologies can be utilized: the flexible and natural language generation of AI-powered text creation, combined with the structured and fact-based data organization of a knowledge graph. This results in more precise, intelligent, and relevant responses for a wide range of use cases.