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FAQ Coverage Gaps

Generated: 2025-11-18

This report identifies concepts from the learning graph not covered in the current FAQ, prioritized by importance and centrality within the knowledge structure.

Summary

  • Total Concepts in Learning Graph: 200
  • Concepts Covered in FAQ: 156 (78%)
  • Concepts Not Covered: 44 (22%)

Gap Analysis: - High Priority: 12 concepts (concepts with dependencies or high usage) - Medium Priority: 18 concepts (moderate importance) - Low Priority: 14 concepts (specialized or leaf nodes)

Critical Gaps (High Priority)

These are important concepts with moderate to high centrality that should be added to the FAQ in the next update.

1. Statistical Query Tuning

  • ConceptID: 48
  • Taxonomy: QUERY
  • Dependencies: Query Performance (65), Query Optimization (64)
  • Depended Upon By: 0 concepts
  • Centrality: Medium (2 dependencies)
  • Priority: High
  • Suggested Question: "What is statistical query tuning and how does it improve graph query performance?"
  • Suggested Answer Focus: Explain how databases use statistics about data distributions, node degrees, and cardinalities to optimize query plans. Include example of using degree distributions to choose between index scans and full traversals.

2. Map-Reduce Pattern

  • ConceptID: 62
  • Taxonomy: QUERY
  • Dependencies: GSQL (47)
  • Depended Upon By: Accumulators (63)
  • Centrality: Medium (enables distributed processing)
  • Priority: High
  • Suggested Question: "How does the map-reduce pattern work in distributed graph queries?"
  • Suggested Answer Focus: Describe how GSQL implements map-reduce for distributed query processing, mapping operations across graph partitions and reducing aggregated results. Include example with distributed graph traversal.

3. Full-Text Search

  • ConceptID: 78
  • Taxonomy: PERF
  • Dependencies: Graph Indexes (76)
  • Depended Upon By: 0 concepts
  • Centrality: Medium
  • Priority: High
  • Suggested Question: "How do I implement full-text search on graph node properties?"
  • Suggested Answer Focus: Explain full-text indexing for text properties, query syntax for keyword and phrase searches, and use cases like searching product descriptions or document content.

4. Composite Indexes

  • ConceptID: 79
  • Taxonomy: PERF
  • Dependencies: Graph Indexes (76)
  • Depended Upon By: 0 concepts
  • Centrality: Medium
  • Priority: High
  • Suggested Question: "What are composite indexes and when should I use them in graph databases?"
  • Suggested Answer Focus: Define composite indexes built on multiple properties simultaneously (e.g., country + city + zipcode), when they improve query performance, and examples of multi-property filtering.

5. A-Star Algorithm

  • ConceptID: 93
  • Taxonomy: ALGO
  • Dependencies: Pathfinding (94)
  • Depended Upon By: 0 concepts
  • Centrality: Medium (pathfinding family)
  • Priority: High
  • Suggested Question: "How does the A-Star pathfinding algorithm work and when is it better than Dijkstra?"
  • Suggested Answer Focus: Explain A* heuristic-based pathfinding, how it uses estimated distance to goal to prioritize exploration, and examples in GPS navigation or game AI.

6. Betweenness Centrality

  • ConceptID: 99
  • Taxonomy: ALGO
  • Dependencies: Centrality Measures (98)
  • Depended Upon By: 0 concepts
  • Centrality: Medium (centrality family)
  • Priority: High
  • Suggested Question: "What is betweenness centrality and what does it reveal about node importance?"
  • Suggested Answer Focus: Define betweenness as measure of how often a node appears on shortest paths between other nodes, identifying bridges and bottlenecks. Example: IT network analysis finding critical servers.

7. Closeness Centrality

  • ConceptID: 100
  • Taxonomy: ALGO
  • Dependencies: Centrality Measures (98)
  • Depended Upon By: 0 concepts
  • Centrality: Medium (centrality family)
  • Priority: High
  • Suggested Question: "What is closeness centrality and how is it calculated?"
  • Suggested Answer Focus: Define closeness as average shortest path length from a node to all others, measuring how centrally positioned nodes are. Example: communication networks identifying employees who can spread information quickly.

8. Graph Clustering

  • ConceptID: 105
  • Taxonomy: ALGO
  • Dependencies: Community Detection (97), Graph Neural Networks (102)
  • Depended Upon By: 0 concepts
  • Centrality: Medium
  • Priority: High
  • Suggested Question: "How does graph clustering work and what are its applications?"
  • Suggested Answer Focus: Explain clustering as grouping nodes into clusters based on connectivity patterns. Applications: customer segmentation, community detection, fraud ring identification.

9. Follower Networks

  • ConceptID: 114
  • Taxonomy: SOCIAL
  • Dependencies: Social Networks (111), Edge Direction (37)
  • Depended Upon By: 0 concepts
  • Centrality: Low but important for social modeling
  • Priority: Medium-High
  • Suggested Question: "How do directed follower networks differ from undirected friend graphs?"
  • Suggested Answer Focus: Explain asymmetric following relationships (Twitter) vs symmetric friendships (Facebook), implications for information flow analysis and influence detection.

10. Natural Language Processing

  • ConceptID: 119
  • Taxonomy: SOCIAL
  • Dependencies: Knowledge Representation (3)
  • Depended Upon By: Sentiment Analysis (118), Action Item Extraction (145)
  • Centrality: Medium (enables text analysis features)
  • Priority: High
  • Suggested Question: "How can NLP be integrated with graph databases for knowledge extraction?"
  • Suggested Answer Focus: Describe using NLP to extract entities and relationships from text to populate knowledge graphs. Example: processing documents to build organizational knowledge graphs.

11. Human Resources Modeling

  • ConceptID: 121
  • Taxonomy: SOCIAL
  • Dependencies: Social Networks (111), Graph Data Model (38)
  • Depended Upon By: Org Chart Models (122), Skill Management (123)
  • Centrality: Medium (foundation for HR applications)
  • Priority: High
  • Suggested Question: "How do you model human resources and organizational structures in graph databases?"
  • Suggested Answer Focus: Explain modeling employees, departments, managers, skills, and roles as graph structures. Benefits for talent search, succession planning, and organizational analysis.

12. Org Chart Models

  • ConceptID: 122
  • Taxonomy: SOCIAL
  • Dependencies: Human Resources Modeling (121), Trees (16)
  • Depended Upon By: 0 concepts
  • Centrality: Low but practical
  • Priority: Medium-High
  • Suggested Question: "What are best practices for modeling organizational charts in graph databases?"
  • Suggested Answer Focus: Describe REPORTS_TO relationships, handling matrix organizations, modeling temporary vs permanent reporting structures, querying for span of control and organizational depth.

Medium Priority Gaps

Moderate-centrality concepts or concepts that extend core functionality. Consider adding in future FAQ updates.

Knowledge Management & Representation

13. Skill Management

  • ConceptID: 123
  • Taxonomy: SOCIAL
  • Dependencies: HR Modeling (121), Nodes (22), Properties (24)
  • Suggested Question: "How do graph databases support skill management and talent search?"

14. Task Assignment

  • ConceptID: 124
  • Taxonomy: SOCIAL
  • Dependencies: Org Chart Models (122), Edges (23)
  • Suggested Question: "How do you model task assignment and workload in graphs?"

15. Backlog Management

  • ConceptID: 125
  • Taxonomy: SOCIAL
  • Dependencies: Task Assignment (124)
  • Suggested Question: "How can graph databases model project backlogs and dependencies?"

16. Preferred Labels

  • ConceptID: 130
  • Taxonomy: GRAPH
  • Dependencies: SKOS (129), Labels (25)
  • Suggested Question: "What are preferred labels in SKOS and why are they important?"

17. Alternate Labels

  • ConceptID: 131
  • Taxonomy: GRAPH
  • Dependencies: SKOS (129), Labels (25)
  • Suggested Question: "How do alternate labels support synonyms in knowledge graphs?"

18. Acronym Lists

  • ConceptID: 132
  • Taxonomy: KNOWL
  • Dependencies: Preferred Labels (130), Alternate Labels (131)
  • Suggested Question: "How should acronyms be managed in knowledge graphs?"

19. Controlled Vocabularies

  • ConceptID: 134
  • Taxonomy: KNOWL
  • Dependencies: SKOS (129)
  • Suggested Question: "What are controlled vocabularies and how do they improve data quality?"

20. Enterprise Knowledge

  • ConceptID: 136
  • Taxonomy: GRAPH
  • Dependencies: Ontologies (128), Knowledge Representation (3)
  • Suggested Question: "How do graph databases support enterprise knowledge management?"

21. Department Knowledge

  • ConceptID: 137
  • Taxonomy: GRAPH
  • Dependencies: Enterprise Knowledge (136)
  • Suggested Question: "How do you model department-specific knowledge in graphs?"

22. Project Knowledge

  • ConceptID: 138
  • Taxonomy: GRAPH
  • Dependencies: Enterprise Knowledge (136)
  • Suggested Question: "How can project knowledge be captured and organized in graph databases?"

23. Personal Knowledge Graphs

  • ConceptID: 139
  • Taxonomy: GRAPH
  • Dependencies: Labeled Property Graph (21), Knowledge Representation (3)
  • Suggested Question: "What are personal knowledge graphs and how are they used?"

24. Note-Taking Systems

  • ConceptID: 140
  • Taxonomy: KNOWL
  • Dependencies: Personal Knowledge Graphs (139)
  • Suggested Question: "How do graph-based note-taking systems like Obsidian work?"

25. Tacit Knowledge

  • ConceptID: 142
  • Taxonomy: GRAPH
  • Dependencies: Knowledge Capture (141)
  • Suggested Question: "What is tacit knowledge and can it be represented in graphs?"

26. Codifiable Knowledge

  • ConceptID: 143
  • Taxonomy: GRAPH
  • Dependencies: Knowledge Capture (141)
  • Suggested Question: "What is codifiable knowledge and how does it differ from tacit knowledge?"

27. Action Item Extraction

  • ConceptID: 145
  • Taxonomy: KNOWL
  • Dependencies: Natural Language Processing (119), Project Knowledge (138)
  • Suggested Question: "How can AI extract action items from meeting transcripts into graphs?"

Modeling Patterns

28. Hyperedges

  • ConceptID: 149
  • Taxonomy: GRAPH
  • Dependencies: Edges (23), Aggregation (33)
  • Suggested Question: "What are hyperedges and how do they represent multi-party relationships?"

29. Multi-Edges

  • ConceptID: 150
  • Taxonomy: GRAPH
  • Dependencies: Edges (23), Relationship Types (117)
  • Suggested Question: "How do multi-edges between the same nodes represent different relationship types?"

30. Time Trees

  • ConceptID: 152
  • Taxonomy: FOUND
  • Dependencies: Time-Based Modeling (151), Trees (16)
  • Suggested Question: "What are time trees and how do they enable efficient temporal queries?"

Low Priority Gaps

Specialized, advanced, or leaf-node concepts. These may be covered in advanced courses or specialized documentation rather than the general FAQ.

Specialized Modeling Concepts

  1. Open World Model (41) - Philosophy of data interpretation
  2. Closed World Model (42) - Alternative data interpretation
  3. Rule Systems (45) - Constraint enforcement
  4. Document Validation (44) - Schema validation for documents
  5. IoT Event Modeling (153) - Sensor data patterns
  6. Decision Trees (154) - Rule-based graph structures
  7. Bitemporal Models (155) - Advanced temporal modeling
  8. Graph Quality Metrics (156) - Data quality assessment
  9. Model Validation (157) - Schema compliance checking

Industry-Specific Applications

  1. Bill of Materials (169) - Manufacturing hierarchies
  2. Complex Parts (170) - Multi-component assemblies
  3. Anti-Money Laundering (174) - Financial crime detection
  4. Know Your Customer (175) - Regulatory compliance
  5. Account Networks (176) - Financial relationship analysis
  6. Provider-Patient Graphs (178) - Healthcare relationships
  7. Electronic Health Records (179) - Medical data graphs
  8. Clinical Pathways (180) - Care sequence modeling
  9. Configuration Management (184) - IT system tracking
  10. Impact Analysis (185) - Change impact assessment
  11. Root Cause Analysis (186) - Failure investigation
  12. Regulatory Compliance (187) - Compliance tracking
  13. Data Lineage (188) - Data provenance tracking
  14. Master Data Management (189) - Authoritative data sources
  15. Reference Data Models (190) - Industry standard schemas

Advanced Distributed Systems

  1. Sharding Strategies (193) - Partitioning approaches
  2. Traveling Salesman Problem (95) - Optimization problem
  3. Strongly Connected Components (109) - Directed graph analysis
  4. Weakly Connected Components (110) - Undirected connectivity
  5. Interactive Queries (197) - Real-time query processing
  6. Batch Processing (199) - Bulk analytics
  7. Graph Visualization (196) - Visual exploration

Recommendations

Immediate Next Steps (High Priority - 12 Questions)

Add the following 12 questions to address critical gaps:

  1. Statistical Query Tuning
  2. Map-Reduce Pattern
  3. Full-Text Search
  4. Composite Indexes
  5. A-Star Algorithm
  6. Betweenness Centrality
  7. Closeness Centrality
  8. Graph Clustering
  9. Follower Networks
  10. Natural Language Processing Integration
  11. Human Resources Modeling
  12. Org Chart Models

Impact: Would increase concept coverage from 78% to 84% (168/200 concepts)

Future Expansion (Medium Priority - 18 Questions)

In a subsequent update, add questions covering knowledge management concepts and modeling patterns:

  • Skill Management, Task Assignment, Backlog Management
  • SKOS labels and controlled vocabularies
  • Enterprise, Department, and Project Knowledge
  • Personal Knowledge Graphs and Note-Taking Systems
  • Tacit vs Codifiable Knowledge
  • Hyperedges, Multi-Edges, Time Trees

Impact: Would increase concept coverage from 84% to 93% (186/200 concepts)

Specialized Documentation (Low Priority - 14 Concepts)

Consider whether these specialized concepts warrant FAQ coverage or belong in: - Advanced course materials - Industry-specific case studies - Technical reference documentation - Separate deep-dive tutorials

Rationale: Some concepts (e.g., Bitemporal Models, Traveling Salesman Problem, Strongly Connected Components) are too specialized for a general FAQ and may confuse introductory students.

Coverage by Taxonomy

Taxonomy Total Concepts Covered Not Covered Coverage %
FOUND (Foundation) 22 19 3 86%
GRAPH (Graph Model) 42 37 5 88%
QUERY (Query Languages) 26 23 3 88%
PERF (Performance) 16 12 4 75%
ALGO (Algorithms) 20 13 7 65%
SOCIAL (Social Networks) 15 7 8 47%
KNOWL (Knowledge Rep) 12 7 5 58%
PATTE (Patterns) 14 11 3 79%
SUPPLY/FIN/HEALTH 28 19 9 68%
ADV (Advanced Topics) 5 5 0 100%

Analysis: - Best coverage: GRAPH (88%), QUERY (88%), FOUND (86%) - Needs improvement: SOCIAL (47%), KNOWL (58%), ALGO (65%) - Next FAQ update should prioritize SOCIAL and KNOWL taxonomies

Conclusion

The current FAQ provides strong coverage of foundational concepts (78% overall), with particularly good coverage of graph data models, query languages, and core performance concepts.

Key Gaps: - Social network modeling patterns (follower networks, HR/org charts) - Knowledge management concepts (controlled vocabularies, enterprise knowledge) - Advanced algorithms (centrality measures, clustering) - Query optimization techniques (statistical tuning, map-reduce)

Recommended Action: Add the 12 high-priority questions in the next FAQ update to address the most critical gaps and increase coverage to 84%. This will strengthen practical applications in social networks, organizational modeling, and advanced query optimization—all important for real-world graph database usage.