Ontological Semantics

Mon, 01 Jan 0001 00:00:00 +0000

Architecture of Ontological Semantics#

The key components required to build a knowledge framework leveraging Ontological Semantics — the theoretical grounding that has enabled the current field of Large Language Models — which help us create knowledge out of existing sources in a systematic manner.

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title Architecture of Ontological Semantics

package "Static Knowledge Sources" {
 database "Ontology\n(World Model)" as ONT
 database "Fact Repository\n(Domain Facts)" as FACT
 database "Lexicon\n(Word Forms & Meanings)" as LEX
 database "Onomasticon\n(Named Entities)" as ONOM
}

component "Knowledge Representation\nLanguage" as KRL

rectangle "Semantic Analyzer\n(Large Language Model)" as LLM

rectangle "Use Cases" as UC {
 component "Text Summarization" as TS
 component "Question & Answering" as QA
 component "AI Agents" as AG
}

ONT --> KRL : "defines concepts"
FACT --> KRL : "instantiates"
LEX --> KRL : "maps words → meaning"
ONOM --> KRL : "resolves named entities"

KRL --> LLM : "structured meaning\nrepresentation"

LLM --> TS
LLM --> QA
LLM --> AG
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The Four Static Knowledge Sources#

Ontology — the World Model#

The ontology defines the concepts that exist in a domain and the relationships between them. It is the schema of the knowledge graph: what nodes (concepts) and edge types (relationships) are valid. Without an ontology, a knowledge base is just a bag of facts with no shared meaning.

NLP on STEM to STEAM

Ontological Semantics

Architecture of Ontological Semantics#

The Four Static Knowledge Sources#

Ontology — the World Model#