Definition
In Artificial Intelligence, semantics is an area that uses both machine learning (ML) and natural language processing (NLP) to understand and process human language at a level similar to humans. Semantics involves comprehending meaning of words, sentences, their context, and user intent. This comprehension is key for tasks involving summarizing articles, answering questions, or translating text [1].
For example, let’s
take a look at the following complaint from a customer to a bank’s customer
service chatbot;
Customer complaint
(in pidgin English): "My card no dey work for POS, wetin I go do?"
The semantic AI
system would:
Understand that
"no dey work" means "is not working"
Recognize
"POS" refers to Point of Sale terminals
Interpret the
customer's frustration and need for immediate help
The system will then respond appropriately: "I understand your ATM card isn't working at POS terminals. Let me help you resolve this quickly."
Semantic AI: The steps involved in its process [2].
Origin
Semantics originated from the groundwork of linguistic theories. Since then, the concept has witnessed extraordinary development as a result of the rapid advancements in technology and the increasing digitization of human interactions. The developmental stride of semantics attests to how traditional linguistic theories have integrated perfectly with the technological domain, rethinking the quintessence of meaning in the digital age [3].
Context and
Usage
Companies across
industries are integrating semantic AI to improve operations and user
experience. For example;
In e-commerce
platforms, it's used to improve product search and recommendations based on
user intent.
In customer
service, Semantic AI enables chatbots to understand customer queries better,
providing more accurate responses.
In healthcare,
it can help interpret patient symptoms described in natural language, aiding
diagnosis and treatment.
In search
engines, semantic AI is used to understand the context and intent behind user
queries, providing more relevant results and even answering questions directly.
Why it Matters
Semantic AI is prominent in the field of AI. It is a process that deals with understanding the meaning and context of data. By comprehending language and relationships between entities, it enables more human-like interactions, precise information retrieval, and contextually relevant responses. This capability to comprehend at an advanced level enables AI systems to tackle complex challenges, making it a major player in determining the future of intelligent technologies [4].
In Practice
A real-life case study of a company that practices semantic AI is Deepgram. Deepgram's AI Agents represent a significant advancement in speech recognition technology, transforming how organizations process and analyze voice data. Deepgram is a powerful speech recognition platform that uses deep learning to convert spoken words into text with exceptional accuracy. The platform processes millions of hours of audio daily across various industries, continuously learning and improving its understanding of human speech. It excels at processing domain-specific terminology and can be fine-tuned for particular industries or use cases [5].
See Also
Related NLP and Text Processing terms:
- Semantic Search: Search technique that understands meaning and context rather than just matching keywords
- Sentiment Analysis: Process of determining emotional tone or opinion expressed in text
- Speech Analytics: Extracting insights and patterns from audio speech data
- Speech Recognition: Converting spoken language into written text
- Syntax Analysis: Understanding sentence structure
References
- Walker II, S., M. (n.d). Semantics.
- Agarwal, P. (2020). What is Semantic AI? Is it a step towards Strong AI?
- Lark Editorial Team. (2023). Semantics.
- Devrix. (2023). What is Semantic AI: Importance, Applications, Benefits.
- Relevanceai. (2025). Deepgram.