Concept Taxonomy
Date: 2025-11-18 Total Concepts: 200 Target Categories: 12
Taxonomy Categories
1. Foundation Concepts (FOUND)
Description: Core foundational concepts including data structures, data modeling principles, and basic knowledge representation that underpin all graph database learning.
Typical Concepts: Data Modeling, World Models, Knowledge Representation, Schema Design, Hash Maps, Trees, Arrays
2. Database Systems (DBSYS)
Description: Traditional and NoSQL database systems including RDBMS, OLAP, OLTP, key-value stores, document databases, and wide-column stores that provide context for graph databases.
Typical Concepts: RDBMS, NoSQL, Key-Value Stores, Document Databases, CAP Theorem, Normalization, Relational Model
3. Graph Data Model (GRAPH)
Description: Core graph database concepts including the Labeled Property Graph model, nodes, edges, properties, labels, schema approaches, and fundamental graph structures.
Typical Concepts: Labeled Property Graph, Nodes, Edges, Properties, Labels, Index-Free Adjacency, Traversal, Schema-Optional Modeling
4. Query Languages (QUERY)
Description: Graph query languages and syntax including OpenCypher, GSQL, GQL, query patterns, path expressions, and query optimization techniques.
Typical Concepts: OpenCypher, GSQL, GQL, Cypher Syntax, Match Clause, Pattern Matching, Variable Length Paths, Query Optimization
5. Performance (PERF)
Description: Performance analysis, benchmarking, indexing strategies, and metrics for evaluating graph database systems.
Typical Concepts: Performance Benchmarking, Graph Indexes, Query Latency, LDBC SNB Benchmark, Graph 500, Scalability, Hop Count
6. Graph Algorithms (ALGO)
Description: Classic and modern graph algorithms including search, pathfinding, centrality measures, community detection, and graph neural networks.
Typical Concepts: Breadth-First Search, Depth-First Search, PageRank, Community Detection, Pathfinding, Graph Neural Networks
7. Social Networks (SOCIAL)
Description: Social network modeling including friend graphs, influence networks, organizational structures, activity streams, and human resources applications.
Typical Concepts: Social Networks, Friend Graphs, Org Chart Models, Skill Management, Follower Networks, Influence Graphs
8. Knowledge Management (KNOW)
Description: Knowledge representation systems including ontologies, SKOS, taxonomies, glossaries, personal knowledge graphs, and enterprise knowledge management.
Typical Concepts: Ontologies, SKOS, Concept Dependency Graphs, Personal Knowledge Graphs, Enterprise Knowledge, Taxonomies
9. Modeling Patterns (PATTERN)
Description: Graph modeling patterns, anti-patterns, ETL processes, data loading strategies, and schema evolution approaches.
Typical Concepts: Subgraphs, Time-Based Modeling, ETL Pipelines, Data Migration, Hyperedges, Schema Evolution
10. Financial Applications (FIN)
Description: Financial transaction modeling, fraud detection, anti-money laundering, know-your-customer, and account network analysis.
Typical Concepts: Financial Transactions, Fraud Detection, Anti-Money Laundering, Know Your Customer, Account Networks
11. Healthcare Applications (HEALTH)
Description: Healthcare-specific graph applications including provider-patient graphs, electronic health records, and clinical pathways.
Typical Concepts: Healthcare Graphs, Provider-Patient Graphs, Electronic Health Records, Clinical Pathways
12. Supply Chain & IT (SUPPLY)
Description: Supply chain modeling, bill of materials, IT asset management, dependency graphs, network topology, and infrastructure applications.
Typical Concepts: Supply Chain Modeling, Bill of Materials, IT Asset Management, Dependency Graphs, Network Topology
13. Advanced Topics (ADV)
Description: Advanced concepts including distributed graph databases, graph visualization, real-time analytics, and capstone projects.
Typical Concepts: Distributed Graph Databases, Graph Partitioning, Graph Visualization, Real-Time Analytics, Capstone Project Design
Taxonomy Design Principles
- Pedagogical Organization: Categories follow a logical learning progression from foundations to applications
- Even Distribution: Each category targets 12-20 concepts to avoid over-representation
- Clear Boundaries: Each concept has a clear primary category
- Progressive Complexity: Foundation → Core → Advanced → Applications
- Industry Relevance: Application categories reflect real-world use cases