Created on 2024-11-21 15:07
Published on 2024-11-21 16:34
This article explores the foundational concepts of Knowledge Graphs, Knowledge Networks, Neural Networks, and Large Language Models (LLMs), shedding light on how they interrelate. These systems embody the principles that signs and symbols enable the communication of meanings—directly through signs (denotation) and indirectly through symbolic representations (connotation).
Knowledge Graphs: Structured, symbolic representations of entities and their relationships, where hyperlinks can act as signs denoting entities and the W3C's Resource Description Framework (RDF) is used to provide symbolic descriptions (or connotations) of those entities.
Neural Networks: Implicit representations of knowledge, where relationships and patterns are abstracted and learned from data, resembling symbols that derive meaning through associations.
Structured vs. Emergent Knowledge:
Reasoning:
Integration:
Knowledge Networks: Flexible, often informal systems of interconnected entities, ideas, or agents, emphasizing dynamic relationships where meanings emerge from the connections between entities.
Neural Networks: Operate similarly by learning patterns and relationships dynamically, mimicking the adaptability of Knowledge Networks in representing and communicating meaning.
Dynamic Connections:
Emergent Knowledge:
Signs and Symbols in Hybrid Models:
Scalability:
Emergence of LLMs:
Semantic Web Vision & LLMs:
Knowledge Graph: A precise encyclopedia—organized, curated, and sign-like in its clarity.
Knowledge Network: A collaborative web of ideas—dynamic, interconnected, and symbol-like in its adaptability.
Neural network-based LLM: A well-read storyteller—drawing meaning dynamically like a symbol, but benefiting from signs (Knowledge Graphs) to verify and refine its outputs.
Sign: A sign is something that stands for, represents, or conveys a meaning about something else in a given context. It provides a direct and explicit reference to the entity it denotes, often serving as a concrete pointer or indicator.
Symbol: A representation, typically constructed from signs, that conveys meaning indirectly, relying on context, associations, or shared understanding for interpretation.
Linked Data: A methodology (or best practice) that uses hyperlinks as signs to denote entities.
RDF (Resource Description Framework): A framework for describing entities symbolically, using entity relationships.
Ontology: A formal specification of entity types and their corresponding relationship types. An ontology can be expressed using RDF as per Schema.org, RDF Schema, Web Ontology (OWL), and many others.
Knowledge Graph: A structured representation of entities and their relationships, where relationship type semantics are machine-computable.
RDF-based Knowledge Graph: A Knowledge Graph where standardized identifiers act as signs for entity denotation and RDF provides symbolic descriptions of their properties and connections.
RDF & Linked Data-based Knowledge Graph: A Knowledge Graph where hyperlinks act as signs for entity denotation and RDF provides symbolic descriptions of their properties and connections.
Knowledge Network: A broader, often informal system of interconnected knowledge elements that emphasizes dynamic relationships and emergent meanings.
Neural Network: A machine learning model inspired by assumptions about the workings of the human brain, using layers of neurons to infer patterns and relationships. Neural networks infer meaning indirectly through learned associations.
LLM (Large Language Model): A type of neural network trained on massive text datasets to interpret and generate language, acting as a flexible conversational engine capable of dynamically generating meaning of variable quality.
Semantic Web: A web-scale Giant Global Knowledge Graph constructed in RDF using Linked Data principles. It combines signs (hyperlinks) to denote entities with RDF (symbolization) to describe their relationships, creating a powerful foundation for explicit and emergent reasoning.
Emergent Reasoning: The process of deriving meaning indirectly through patterns learned in neural networks.
Neuro-symbolic AI: An approach that combines symbolic reasoning (e.g., Knowledge Graphs) with the emergent reasoning capabilities of neural networks.
Signs & Symbols: Explore the foundational concepts of signs and symbols in communication and their applications to knowledge systems. Read more.
Signs & Reality: Signs and reality as two foundational topics of ontology. Read more.
Knowledge Graphs, Ontologies, and Linked Data: A detailed overview of how Knowledge Graphs use Linked Data principles, combining signs and symbols for effective representation. Read more.
Semantic Web & LLM Symbiosis: Insights into how Semantic Web principles and LLMs complement each other in creating a robust AI ecosystem. Read more.