Graphs to Graph Neural Networks: From Fundamentals to Applications — Part 2b: Knowledge Graphs
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Isaac: In this post, I will continue learning about knowledge graphs. You can find the first part of this here.
11- What is the difference between a semantic network and a knowledge graph?
A semantic network and a knowledge graph are both methods of representing knowledge in a structured way. They are related concepts, but there are some key differences between the two:
- Structure:
Semantic network: A semantic network represents concepts and their relationships as a graph, with nodes representing concepts or entities and edges representing relationships or connections between those concepts. The relationships can be hierarchical, associative, or other types, and are typically labeled with semantic roles, such as “is-a” or “part-of.”
Knowledge graph: A knowledge graph is a more advanced form of a semantic network, which also represents knowledge as a graph with nodes and edges. However, knowledge graphs are typically more expressive and allow for the representation of more complex relationships, including properties and attributes of the entities. Knowledge graphs often employ a schema or an ontology to define the structure of the graph and the types of relationships that can exist.
2. Expressiveness:
Semantic network: Semantic networks are usually less expressive than knowledge graphs, as they primarily focus on representing relationships between concepts. They may not be able to capture more complex relationships or properties of entities.
Knowledge graph: Knowledge graphs are more expressive, allowing for representation of not only relationships between entities but also properties and attributes of the entities themselves. This makes knowledge graphs better suited for representing more detailed and nuanced information.
3. Usage:
Semantic network: Semantic networks are commonly used in cognitive psychology, linguistics, and artificial intelligence as a way to represent and reason about knowledge. They can be used for various tasks, such as natural language understanding, reasoning, and knowledge-based systems.
