knowledge-representation

name	knowledge-representation
description	Knowledge representation in AI
license	MIT
compatibility	opencode
metadata	{"audience":"machine-learning-engineers","category":"artificial-intelligence"}

What I do

• Design knowledge structures
• Build ontologies and taxonomies
• Implement semantic networks
• Create knowledge graphs
• Represent uncertain knowledge
• Build expert systems

When to use me

Use me when:

• Building knowledge-based systems
• Creating semantic search
• Implementing reasoning systems
• Integrating heterogeneous data
• Building question-answering systems

Key Concepts

Knowledge Representation Schemes

1. Semantic Networks

      ┌─────────┐
      │Animal  │
      └────┬────┘
           │is-a
     ┌─────┴─────┐
     ▼           ▼
┌───────┐   ┌────────┐
│Mammal │   │  Bird  │
└───┬───┘   └───┬────┘
    │           │has-a
    ▼           ▼
┌───────┐   ┌────────┐
│ Dog  │   │Wings   │
└───────┘   └────────┘

2. Frames

class Frame:
    def __init__(self, name, slots):
        self.name = name
        self.slots = slots

dog = Frame("Dog", {
    "is_a": ["Mammal"],
    "legs": 4,
    "sound": "bark",
    "eats": ["meat", "dog_food"],
    "can": ["fetch", "guard", "hunt"]
})

3. Logic Representation

// First-Order Logic
∀x (Dog(x) → Mammal(x))
∀x (Mammal(x) → Animal(x))
∃x (Dog(x) ∧ Barks(x))

// Rules
IF patient.has_fever AND patient.cough THEN patient.has_cold

Knowledge Graphs

# RDF-like representation
from rdflib import Graph, Namespace, URIRef, Literal

EX = Namespace("http://example.org/")

g = Graph()
g.add((EX.Person, EX.hasName, Literal("Alice")))
g.add((EX.Person, EX.hasAge, Literal(30)))
g.add((EX.Person, EX.worksAt, EX.Company))

Applications

• Ontologies: OWL, RDF schemas
• Knowledge Graphs: Google, Wikidata
• Expert Systems: MYCIN, DENDRAL
• Semantic Web: Linked data