Knowledge Graphs

Type: paper Tier: 2 (Foundational Paper) Author(s): Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, Jose Emilio Labra Gayo, Sabrina Kirrane, Sebastian Neumaier, Axel Polleres, Roberto Navigli, Axel-Cyrille Ngonga Ngomo, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, Antoine Zimmermann Date: 2021 URL: https://arxiv.org/abs/2003.02320 Accessed: 2026-06-02

Why This Source Matters

This survey provides a broad foundation for knowledge graphs: graph-based data models, relationships, query languages, reasoning, and applications. It closes the Project 09 source gap while keeping the capstone's tag-linked graph modest.

Key Claims

• Knowledge graphs represent entities and relationships as graph-shaped knowledge.
• Knowledge graph work spans graph data models, schema and constraint languages, query languages, deductive reasoning, and embeddings.
• KGs are useful when relationships between entities are first-class, not just metadata on isolated records.
• The field includes symbolic, database, and machine-learning approaches; a lightweight educational graph should not claim to implement the whole research area.
• For Project 09, the relevant claim is narrow: a graph is a useful structure when saved memories or concepts need explicit links that can be traversed.

Relevant To

• concepts: [knowledge-graph, graph-data-model, entities-and-relations, personal-knowledge-graph, system-design]
• projects: [09-personal-learning-os]

Notes

This source grounds the term "knowledge graph." The Project 09 implementation remains a lightweight tag-linked personal graph, not a full RDF, SPARQL, ontology, or KG embedding system.