Subtitle:
From raw data to compressed meaning: designing information for humans, AI, search engines, and future intelligent systems.

This curriculum is different from traditional Computer Science or Information Theory. Instead of focusing mainly on algorithms, databases, or communication channels, it treats information itself as the primary object of engineering. The goal is to understand not just how information is stored or transmitted, but how it is represented, compressed, structured, understood, retrieved, and transformed into knowledge by both humans and AI.

Semester I — Foundations of Information

Module 1. What is Information?

Study the evolution of information from multiple perspectives.

• Shannon information

• Semantic information

• Knowledge

• Wisdom

• Data

• Facts

• Signals

• Symbols

• Meaning

• Truth

• Context

• Uncertainty

Compare

• Information Theory

• Cognitive Science

• Linguistics

• AI

• Philosophy

• Library Science

Module 2. Information Units

Learn to identify the smallest building blocks.

• character

• token

• word

• phrase

• statement

• fact

• entity

• relation

• event

• document

• graph

• ontology

Project

Represent the same knowledge using progressively richer structures.

Module 3. Information Structures

Study

• lists

• trees

• graphs

• networks

• tables

• vectors

• tensors

• ontologies

• semantic networks

• hypergraphs

Module 4. Information Quality

Measure

• correctness

• completeness

• consistency

• precision

• ambiguity

• redundancy

• reliability

• provenance

• freshness

• interpretability

Semester II — Semantics

Module 5. Meaning

Study

• semantics

• syntax

• pragmatics

• denotation

• connotation

• context

• ambiguity

• polysemy

• synonymy

Research

Why can identical words mean different things?

Module 6. Semantic Density

One of the central ideas of the program is maximizing meaning per unit of representation.

Topics

• semantic density

• information density

• concept compression

• abstraction

• encapsulation

• normalization

• canonical forms

• minimal representations

Study questions

• How much meaning fits into one sentence?

• One paragraph?

• One vector?

• One entity?

• One graph node?

Projects

Rewrite the same document while preserving its meaning but reducing its size by 90%.

Module 7. Brevity Engineering

Traditional writing often rewards length. Modern AI systems often reward clarity, structure, and high information content.

Study

• concise writing

• atomic statements

• structured language

• semantic compression

• redundancy removal

• token efficiency

• machine readability

• cognitive load reduction

Learn how to write information that is simultaneously:

• short

• precise

• complete

• unambiguous

• reusable

• searchable

• machine-friendly

Module 8. Semantic Compression

Research

Difference between

• ZIP compression

• semantic compression

Examples

Instead of

"The company that manufactures electric vehicles and is led by Elon Musk..."

Represent

Tesla

One entity replaces dozens of words because shared knowledge carries the additional meaning.

Semester III — Knowledge Engineering

Module 9. Knowledge Formation

Study

• observation

• evidence

• facts

• hypotheses

• models

• theories

• explanations

• predictions

Understand how information becomes knowledge.

Module 10. Knowledge Organization

Study

• taxonomies

• ontologies

• classifications

• knowledge graphs

• entity systems

• schemas

• conceptual models

Module 11. Information Processing

Learn the complete lifecycle.

• acquisition

• extraction

• parsing

• normalization

• enrichment

• linking

• indexing

• retrieval

• reasoning

• generation

• updating

• archiving

Projects

Build an end-to-end information pipeline.

Module 12. Information Retrieval

Study

• indexing

• ranking

• retrieval

• semantic search

• GraphRAG

• vector search

• hybrid retrieval

Compare keyword-based and semantic retrieval.

Semester IV — Information for AI

Module 13. AI-Optimized Information

Traditional documents were designed for humans.

Modern information should serve both people and intelligent machines.

Study

• structured documents

• semantic markup

• JSON-LD

• Schema.org

• entities

• metadata

• citations

• machine-readable knowledge

Module 14. Cognitive Information Design

Topics

• cognitive load

• attention

• chunking

• memory

• concept hierarchies

• progressive disclosure

• visual organization

Learn to design information that is easier to understand and remember.

Module 15. Universal Knowledge Architecture

Research

How to create information that is

• language independent

• culture independent

• machine understandable

• future-proof

• interoperable

• modular

• composable

Design a universal conceptual architecture that different AI systems can reuse.

Module 16. Future Information Science

Explore emerging research directions.

• Semantic Operating Systems

• Knowledge Engines

• Information Architecture for AI Agents

• Scientific Knowledge Networks

• Self-organizing Knowledge Bases

• Machine-readable Scientific Literature

• Autonomous Knowledge Discovery

• Semantic Internet

• Universal Knowledge Layer

Programming and Technical Skills

• Python

• SQL

• Graph Databases

• RDF

• JSON-LD

• XML

• YAML

• Markdown

• Cypher

• SPARQL

Supporting Disciplines

• Information Theory

• Linguistics

• Cognitive Science

• Knowledge Representation

• Library and Information Science

• Human–Computer Interaction

• Artificial Intelligence

• Philosophy of Language

• Systems Design

Research Competencies

Graduates will be able to:

• Design information with high semantic density rather than unnecessary verbosity.

• Convert unstructured text into structured, reusable knowledge.

• Measure and improve semantic quality, clarity, and compressibility.

• Build information architectures optimized for both human understanding and AI processing.

• Engineer documents, datasets, and knowledge bases that remain interpretable, interoperable, and maintainable over time.

• Develop new frameworks for semantic compression, canonical representation, and information lifecycle management.

This program differs from traditional information science by treating information as an engineered semantic object. Instead of asking only "How do we store or transmit information?", it asks deeper questions such as:

• What is the smallest complete representation of an idea?

• How can the same knowledge be expressed with fewer symbols while preserving meaning?

• How should information be structured so both humans and AI systems understand it with minimal ambiguity?

• What characteristics make information durable, reusable, and efficient across future intelligent systems?