The Secret Weapon of Enterprise AI: Knowledge Graphs

Businesses want most of the output from AI, for this they need to mix up their old technology with the advanced one.but here is the problem that all the companies are framing and storing their data in structured way but the AI always works best in unstructured Data.

Companies have been using general graphs for several years as the solution for this problem. This technology finds out the interconnected relationship between the piece of data, but somehow it’s a very complex process which requires a specific person incubated with high skill in this field.
In 2023, according to the experts’ suggestion , companies always focused on using the blended technology for the better result and also some of the companies already on it.A company Nebula Graph come with a tool known to be Graph RAG in September 2023, which is a combination of both knowledge graph and AI. and now a days big companies like microsoft, google , neo4 using their own version for this.
Utilisation of Retrieval Augmented Generation (RAG) method improved AI performance . The help of this AI can answer questions more accurately. Without RAG, AI can only respond according to the original data. With RAG, businesses can improve AI performance by adding document and day to day information .
By November 2023, AI experts found Graph RAG played a key in the development of AI. They also estimate that within 5 years it will be widely accepted . However, it is not easy. It can be expensive and requires technical knowledge. Some industries, like pharmaceuticals and media, have already gotten into it , but most companies have not. Instead, they prefer simpler ways of combining data.
Building a knowledge graph is difficult, but now AI is making the process easier. In the past,connection of data points was in manual mode . Now, with the help of AI it’s in automation mode and speeding up the process and improving accuracy.

Linking of different types of data needed for working on a knowledge graph, and the data may be from one or from m multiple sources, It does this without changing the original structure of the data. Traditional databases only connect data by identifying fixed relationships. But in the case of Knowledge graphs, it goes with deeper connection for making AI responses more helpful and accurate.
Accuracy rates of AI systems using RAG is between 70% and 80%. But industries, like healthcare and finance need even higher accuracy. AI expert Daniel Bukowski found out that some are ok with the 80% accuracy rate while some others need 99% accuracy.

Knowledge graphs also help AI to work in a proper way without any mistakes.Sometimes, AI generates wrong information. A knowledge graph helps to generate the right information and also ensure that the AI context is purely in a proper way. Businesses like Infosys are now exploring the different uses of knowledge graphs.

One company that successfully combined RAG with a knowledge graph is LinkedIn. In early 2024, LinkedIn gave the report that accuracy of its customer service AI by 78%. It also reduced the timeframe needed to solve customer issues by 29% over six months.
Adding more context to AI queries can give better results , but. AI companies charge based on the amount of data processed, so adding more context can become expensive. A Microsoft study in April 2024 found that due to the n utilization Graph RAG, it reduced the amount of data needed by up to 97%, making AI faster and affordable.

Building a knowledge graph needs a lot of effort. Companies had to focus in different sectors during this process. This was especially difficult for dealing with large and complex data sets. In the past, businesses used machine learning and natural language processing (NLP) to create knowledge graphs, but this required proper knowledge and investment of time.

Now, AI can contribute to building Knowledge graphs in a faster way. AI expert Vamsi Duvvuri has already implemented this and suggests that this system has both performance and cost efficiency. AI can quickly identify relationships which would take longer time by humans.
Professor Pierre Liang from Carnegie Mellon University assumes that AI can generate knowledge in a better way which was not possible in the prior stage . He sees a lot of businesses are already using AI for creating and utilisation for knowledge graphs.

In summary, integration of AI and knowledge graphs are a powerful combination,where knowledge graphs are very difficult to set up, but now AI is making the process easier. Companies that invest in this technology will benefit from better, faster, and more accurate AI insights, helping them stay ahead in a competitive business world.