A semantic network is a way of representing knowledge as a graph of interconnected concepts, used extensively in artificial intelligence and cognitive science. In a semantic network, each node represents an entity, concept, or idea, and each link (or edge) represents a relationship between these concepts. For example, the concept “cat” might be connected to “animal” (with the relationship “is a”), and to “fur” (with the relationship “has”). This structure allows machines and humans to visualize and reason about relationships in a way that resembles natural human thinking.
Semantic networks first gained popularity in the 1960s and 70s, playing an important role in early AI research aimed at modeling human memory and reasoning. They have since evolved and are now used in a variety of applications such as natural language processing, knowledge representation, search engines, and expert systems. Modern AI systems often use sophisticated versions of semantic networks to enable more nuanced understanding and manipulation of concepts, supporting features like question answering and contextual search.
What makes semantic networks especially valuable in AI is their flexibility. Unlike rigid databases or flat lists of facts, semantic networks can represent complex and layered relationships. They can handle things like inheritance (where, for example, all mammals inherit the property of being warm-blooded), exceptions (such as bats being mammals that can fly), and multiple types of relationships (such as “part of,” “causes,” or “located in”). This structure supports both deductive and associative reasoning, letting systems infer new facts based on existing connections.
Semantic networks are closely tied to the idea of ontologies, which are more formal systems for organizing knowledge in a particular domain. While ontologies often involve strict rules and standardized vocabularies, semantic networks can be more informal and intuitive. This makes them useful for everything from educational software to sophisticated AI assistants that need to navigate the messy, ambiguous world of human language.
In practice, building a semantic network involves defining nodes for each relevant concept and then specifying the relationships that connect them. These relationships may be labeled (like “is a” or “part of”) or unlabeled, depending on the application. The resulting network can then be searched, traversed, or analyzed using algorithms from graph theory and computer science.
Many modern graph databases and knowledge graphs are built on principles similar to semantic networks. They store vast webs of interconnected facts, enabling AI models to quickly retrieve relevant information and draw connections that would be hard to find in more traditional data structures. As AI continues to advance, semantic networks remain a foundational tool for making sense of complex, interrelated data in a way that is both machine-readable and human-friendly.
