In artificial intelligence (AI), a conceptual graph (CG) is a formalism for representing knowledge in a structured and graph-based way. It is a type of semantic network that captures the relationships between concepts through nodes and edges. Conceptual graphs were introduced by John F. Sowa in the late 1970s as a way to represent the meaning of natural language expressions in a form that could be used for automated reasoning and knowledge processing.
A conceptual graph is composed of nodes and edges that form a bipartite graph:
Concept Nodes (C): Denoted by rectangles. Represents entities.
Relation Nodes (R): Denoted by circles. Represents abstract concepts.
Each edge in the graph connects a concept node to a relation node, and each relation node is connected to one or more concept nodes.
The first step is to find the concept nodes and relation nodes.
Concept Nodes:
[Motorbike] represents the concept of a motorbike.
[Handle] representing the concept of a handle.
Relation Node:
(Has) representing the relationship between the motorbike and the handle.
We can also write it as:
∀x (x:Motorbike) → ∃y (y:Handle)
[ (x) --(Has)--> (y) ]
The same can be represented in First Order Predicate Logic as:
∀x : Motorbike(x)→∃y : Handle(y)∧Has(x,y)