Knowledge Graph Course Syllabus Overview
This document outlines a comprehensive knowledge graph curriculum covering fundamentals, representation methods, storage solutions, extraction techniques, reasoning, fusion, question answering, graph algorithms, and emerging research directions across nine detailed chapters.
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 Artificial Intelligence
2.3 Symbolic Representation Methods for Knowledge Graphs
2.4 Vector Representation Methods for Knowledge Graphs
Chapter 3: Storage and Query of Knowledge Graphs
3.1 Relational‑Database‑Based Knowledge Graph Storage
3.2 Native Graph‑Database‑Based Knowledge Graph Storage
3.3 Overview of Native Graph Database Implementation Principles
Chapter 4: Extraction and Construction of Knowledge Graphs
4.1 Re‑understanding Knowledge Engineering and Knowledge Acquisition
4.2 Knowledge Extraction – Entity Recognition and Classification
4.3 Knowledge Extraction – Relation Extraction and Attribute Completion
4.4 Knowledge Extraction – Concept Extraction
4.5 Knowledge Extraction – Event Recognition and Extraction
4.6 Frontiers of Knowledge Extraction Technology
Chapter 5: Knowledge Graph 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: Knowledge Graph Fusion
6.1 Overview of Knowledge Graph Fusion
6.2 Concept‑Level Fusion – Ontology Matching
6.3 Instance‑Level Fusion – Entity Alignment
6.4 Frontiers of Knowledge Fusion Technology
Chapter 7: Knowledge Graph 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 Graph 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: Knowledge Graph 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
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