Intelligent Content Services based on Knowledge Graphs & Large Language Models

Large Language Models (LLMs) are a hot topic, discussed from many different angles. There is a lot of excitement about what they can do, but also a lot of concern about how they do it.

It’s one thing to let generative AI write a poem or the description of a talk (not the case here), but can we trust it to give us the right answers to critical questions or make the right decisions? The clear disadvantages of this approach are the lack of explainability of the results, consistency, and accuracy of responses, the need for large amounts of high-quality data, and the potential for bias in the content generated by the models. One possible answer to these challenges is to combine these generative technologies with knowledge graphs that can help explain the results and support the generation of high-quality data to train the models. The combination of generative AI and symbolic AI can ultimately lead to eXplainable AI (XAI), which can help us in our daily work.

We will show the benefits of combining both technologies, how knowledge graphs and intelligent structured source content can help to create better and more explainable results with generative technologies, and how generative technologies can help to create better knowledge graphs. This combination will lead to the creation of intelligent content that enables deeper understanding and informed decision making.

Etteplan and Semantic Web Company have developed unparalleled experiences and capabilities in creating, developing, and maintaining consistent (technical) knowledge bases for many companies across multiple industries and markets – placing them in a unique position to leverage the true potential of Large Language Models.