What is Semantic AI?

Semantically enriched data serves as a basis for better data quality and provides more options for feature extraction. This results in a higher precision of prediction & classification calculated by machine learning algorithms. With knowledge graphs in place, an advanced data model can be used in order to make data interpretable and reusable in various contexts.

At the core of a Semantic AI architecture is a “semantic layer” which acts as the glue between company databases and front-end applications. A semantic layer, which encompasses knowledge graphs, semantic tagging, text mining, and semantic search, aggregates and unifies the data with enriched metadata, making all the necessary links to the systems themselves. A company can have various types of platforms – be it CMS, DAM, user portals, etc – and still utilize the semantic layer without hassle. Companies can begin experiencing the benefits of AI rather seamlessly. 

Machine learning can help to extend knowledge graphs (e.g., through ‘corpus-based ontology learning’ or through graph mapping based on ‘spreading activation’), and in return, knowledge graphs can help to improve ML algorithms (e.g., through ‘distant supervision’). This integrated approach ultimately leads to systems that work like self optimizing machines after an initial setup phase, while being transparent to the underlying knowledge models.

An additional strength to Semantic AI is that while offers automated processes, the human is always kept in the loop; we operate on explainable AI principles where not all the power is left to the machines. While this integrated approach enable self-optimization, it also gives room for the product owners or subject matter experts to modify and adjust the output as needed through expert-assisted workflows.

Semantic AI is the combination of methods derived from symbolic AI and statistical AI. Semantic AI has always focused on being able to infer and classify the meaning of large content sets in a largely automated way; it doesn’t just tackle the surface of the text, but uses natural language processing (NLP) to understand it more deeply. This is becoming especially important during the rise of Generative AI and Large Language Models (LLMs). 

LLMs like ChatGPT are admittedly powerful but have a strong deficit in traceability and accuracy – which are two qualities an organization cannot skip out on. Employees have very specific requirements when using technology to complete their tasks and they don’t want to be flooded with half-baked answers or incorrect output (in the GenAI world, this is called a “hallucination”). Specific knowledge models that are enriched with context are the way to mitigate this, and a knowledge graph is the most suitable option, which we hone through a specific technology design pattern called Graph RAG (or Semantic RAG).

Some of the main benefits of this approach include:

• Fewer hallucinations
• Context-based answer retrieval
• Trsutworthy data source based on a company’s own training data
• Traceability of answers
• Reduced costs for maintenance

Read more about Graph RAG here >