Knowledge graphs integrate diverse data sources into a single graph structure, and add semantic interpretation to data. This makes them the ideal knowledge representation format for large language models, which can learn domain semantics from knowledge graphs. – So, how to create knowledge graphs? With our SmartGraph framework, this can be done automatically, with no involvement and very reasonable prices for our customers.
Knowledge graphs integrate diverse data sources into a single graph structure, and add semantic interpretation to data. This makes them the ideal knowledge representation format for large language models, which can learn domain semantics from knowledge graphs. – So, how to create knowledge graphs? With our SmartGraph framework, this can be done automatically, with no involvement and very reasonable prices for our customers.
Get high-quality training and test data, in easily consumable formats, cleansed and based on your project’s domain material.
KG’s are key when it comes to enhancing your models with precise, trustworthy information, supporting inferences. We help you build targeted KGs based on your customer’s domain specific data.
Don’t drown in mass amounts of text – our customizable extractors get the key information values out of large corpora!
We can help you with all kinds of NLP. Working with your customer’s natural language material, we can do transformation, extraction, cleansing, normalization and many other tasks on your data.
LLMs in companies offer tremendous opportunities, such as more effective workflows, better customer communication, and faster access to knowledge within the company. However, this often comes with challenges, such as low-quality output, slow capture of internal data sources, and insufficient IT infrastructure expertise to integrate high-quality data points.
With our knowledge graph technology and our many years of experience in the field of AI, we can support you with fast integration online and offline.
Create high-quality information for your AI with our Knowledge Graph Framework. We support you throughout the process to extract precise information from large amounts of data so that you can generate adequate outputs.
Our framework works completely automatically and cost-effectively to process data that can be used by AI in a variety of ways.
Backend data from Pdf’s, websites, databases, or XML document, all with different formats and semantics, can easily contribute to a KG. Plus, database semantics is very limited and can be augmented through other sources.
Updating an LLM is a nightmare because it may forget what it knew before. And even RAG-style document databases need testing after each update.
Sometimes, it really matters what is being answered – think of legally binding product infortmation, in particular if no human is in the loop. But even if there is human monitoring, think of the difference between 60% accuracy and 85%, in terms of servicing cost.
Explanation is more than saying that “the word ‘blabla’ contributed 23% to your answer”. It means showing the inference chain that led to this answer being generated. With KG-based question answering, you can do just that – bringing up the navigation path that led to the statements finally verbalized.
TextVerstehen helps you to improve your natural language interfaces: Making them more robust, more effective, more reliant, less biased and check their compliance with public regulations.
Using the “grandchild trick” example, we show how LLMs can be guided to make correct inferences with the help of knowledge graphs—and why classic RAG approaches often reach their limits in this regard.
An improved prompting technique based on “least-to-most” thinking solves complex semantic parsing tasks and achieves new best values on the CFQ dataset with only 1% of the training data.
Let’s work together! Send us a message using the form or contact us directly with email.
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