Knowledge Graph Course Syllabus Overview

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Teaching Plan

Chapter 1 – Introduction to Knowledge Graphs

1.1 Language and Knowledge

1.2 Origin of Knowledge Graphs

1.3 Value of Knowledge Graphs

1.4 Technical Connotation of Knowledge Graphs

Chapter 2 – Representation of Knowledge Graphs

2.1 What Is Knowledge Representation

2.2 Knowledge Representation in the History of AI

2.3 Symbolic Representation Methods for Knowledge Graphs

2.4 Vector Representation Methods for Knowledge Graphs

Chapter 3 – Storage and Query

3.1 Relational Database Storage of Knowledge Graphs

3.2 Native Graph Database Storage of Knowledge Graphs

3.3 Overview of Native Graph Database Implementation Principles

Chapter 4 – Extraction and Construction

4.1 Re‑understanding Knowledge Engineering and Acquisition

4.2 Entity Recognition and Classification

4.3 Relation Extraction and Attribute Completion

4.4 Concept Extraction

4.5 Event Recognition and Extraction

4.6 Frontier of Knowledge Extraction Techniques

Chapter 5 – Reasoning

5.1 What Is Reasoning

5.2 Introduction to Knowledge Graph Reasoning

5.3 Symbolic Logic‑Based Knowledge Graph Reasoning

5.4 Representation‑Learning‑Based Knowledge Graph Reasoning

Chapter 6 – Fusion

6.1 Overview of Knowledge Graph Fusion

6.2 Concept‑Level Fusion – Ontology Matching

6.3 Instance‑Level Fusion – Entity Alignment

6.4 Frontier of Knowledge Fusion Techniques

Chapter 7 – Question Answering

7.1 Overview of Intelligent QA Systems

7.2 Template‑Based Knowledge Graph QA

7.3 Semantic‑Parsing‑Based Knowledge Graph QA

7.4 Retrieval‑and‑Ranking‑Based Knowledge Graph QA

7.5 Deep‑Learning‑Based Knowledge Graph QA

Chapter 8 – Graph Algorithms and Data Analysis

8.1 Basic Knowledge of Graphs

8.2 Basic Graph Algorithms

8.3 Graph Neural Networks and Graph Representation Learning

8.4 Graph Neural Networks and Knowledge Graphs

Chapter 9 – Technology Development

9.1 Multimodal Knowledge Graphs

9.2 Knowledge Graphs and Language Pre‑Training

9.3 Factual Knowledge Graphs

9.4 Knowledge Graphs and Low‑Resource Learning