Definition
A semantic network is a type of knowledge representation that shows how concepts are related to each other. In the field of Artificial Intelligence, it structures and organizes data, enabling machines to interpret, process, and use it for decision-making. In a semantic network, the nodes represent concepts, and the edges define the relationships between these concepts, such as "is a," "part of," or "related to." [1]
For instance, the
concept "Agbada" might be connected to "Traditional
Clothing" with an "is a" relationship, showing that agbada is a
type of traditional clothing worn in Nigeria.
Also, the concept "Nollywood" might be connected to "Film Industry" with an "is a" relationship, indicating that Nollywood is a type of film industry specific to Nigeria.
Origin
The concept of semantic networks has its origins from the foundational work of cognitive psychologists and artificial intelligence researchers. Major players Alan M. Collins and M. Ross Quillian, proposed the semantic network model of memory in the 1960s. Their work laid the groundwork for understanding how semantic memory operates and how concepts are interconnected [3].
Context and Usage
They can improve
a number of functions and roles across industries such sales, marketing, retail
and healthcare. Some examples of such roles and functions include the
following:
Chatbots:
Chatbots primarily has to do with inputs that use natural language. Semantic
networks enables chatbots comprehend the meaning behind a user's input, providing
relevant responses in the process. However, not all chatbots need to use
semantic networks.
Answering
queries: Semantic networks assist in establishing the relationships between
different words and concepts in the input question in AI programs designed to
answer user queries.
Natural language processing: In NLP, semantic networks help map words and phrases to their related representations. This enables AI systems to better understand language meanings [4].
Why it Matters
Semantic networks matter greatly in natural language processing, knowledge representation, and information retrieval systems. It centers on grasping the context and meaning of words. This can be seen in processing for tasks like sentiment analysis, text summarization, and question answering. In addition, semantic networks support machine reasoning, enabling AI systems to draw logical conclusions based on the connections within the network. This ability makes it crucial in problem-solving and decision-making situations. By improving data intelligence and manipulation, semantic networks equip AI to produce more intelligent and context-aware solutions, ultimately enhancing user experiences across diverse applications and domains.
In Practice
A good example of a real-life case study of semantic network in practice is WordNet. WordNet is a semantic network, which is organized in such a way that synsets and wordsenses are the nodes of the network, and relations among the synsets and wordsenses are the edges of the network. In WordNet, each meaning of a word is represented by a unique wordsense of the word, and a synset (stands for “synonym set”) is consisting of a group of wordsenses sharing the same meaning. More than two thirds of the nodes in WordNet are synsets. hyponymOf is the key relationship for noun synsets in WordNet, which has been widely used to estimate the semantic relatedness among nouns [5].
See Also
Related NLP and
Text Processing terms:
- Semantic (AI): Relating to the meaning and interpretation of words, phrases, or symbols
- Semantic Annotation: Process of adding meaningful metadata or labels to content for better understanding
- 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.
References
- Geeksforgeeks. (2024). Semantic Networks in Artificial Intelligence.
- Pushkar, A. (2025). What is Semantic Networks in Artificial Intelligence?
- Muns, A. (2025). How semantic networks represent knowledge.
- Gillis, A., S. (2024). What is a semantic network?
- Gole, S. (2015). Understanding words similarity / relatedness using WordNet - Semantic similarity.