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Knowledge Graphs

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive licence to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2003.02320v6

Summary

This tutorial-style survey addresses the absence of a single accessible overview of what knowledge graphs are, which techniques they rely on, and how they relate to established data management research. A large international author team adopts a deliberately broad definition: a data graph built to collect and communicate real-world knowledge, where nodes stand for entities and edges for the relations between them.

Using a hypothetical tourism knowledge graph for Chile as a running example, the paper covers:

  • graph data models (e.g., directed edge-labelled graphs, property graphs) and their query languages;
  • schema, identity and context;
  • deductive knowledge via ontologies and rules;
  • inductive approaches such as graph analytics, embeddings and graph neural networks;
  • how knowledge graphs are created, enriched, assessed for quality, refined and published.

It then reviews prominent open and enterprise knowledge graphs and their applications, and ends with research directions. Its value lies in serving as a shared entry point for researchers and practitioners new to the field. The authors explicitly position it against earlier surveys that each treat narrower topics.

Key points

  • Origins: The term dates back to at least 1972, but its current sense comes from Google's 2012 Knowledge Graph announcement. Airbnb, Amazon, eBay, Facebook, IBM, LinkedIn, Microsoft and Uber followed with their own knowledge graphs.
  • Two categories:
    • Open knowledge graphs, such as DBpedia, Freebase, Wikidata and YAGO, plus domain-specific ones for geography, government, media, tourism and life sciences.
    • Enterprise knowledge graphs, used in search (Bing, Google), commerce, social networks and finance (e.g., Banca d'Italia, Bloomberg, Capital One, Wells Fargo).
  • Comparison with prior work: Table 1 compares the paper with 13 earlier books and surveys from 2017–2020 (e.g., Pan et al. 2017, Paulheim 2017, Wang et al. 2017, Ji et al. 2020).
  • Structure: The paper has 11 sections. Appendix A covers history and prior definitions; Appendix B gives formal definitions. It was published in ACM Computing Surveys 54(4), 2021.
  • Future directions:
    • Specific topics include formal semantics for property graphs, shape induction, contextual and entailment-aware embeddings, and rule/axiom mining.
    • Broader open challenges are scalability, quality, diversity, dynamicity and usability.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: AI & machine learning

Cited by

107 papers of our library cite it (of the 140673 whose bibliographies we read).

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1145/3447772 · arXiv 2003.02320

@misc{hogan2020knowledge,
  title = {{Knowledge Graphs}},
  author = {Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and d'Amato, Claudia and Melo, Gerard de and Gutierrez, Claudio and Gayo, José Emilio Labra and Kirrane, Sabrina and Neumaier, Sebastian and Polleres, Axel and Navigli, Roberto and Ngomo, Axel-Cyrille Ngonga and Rashid, Sabbir M. and Rula, Anisa and Schmelzeisen, Lukas and Sequeda, Juan and Staab, Steffen and Zimmermann, Antoine},
  year = {2020},
  doi = {10.1145/3447772},
  eprint = {2003.02320},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1145/3447772}
}

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