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Knowledge Graph Refinement: A Survey of Approaches and Evaluation Methods

Paper · 2017 · Heiko Paulheim

Summary

Large web knowledge graphs like DBpedia, YAGO, Freebase, Wikidata, and NELL are built through curation, crowdsourcing, or automatic extraction. None of them can be both complete and fully correct. Paulheim reviews research on knowledge graph refinement: methods that take an existing graph and improve it afterward, either by adding missing facts or by finding wrong ones. He keeps this separate from building a graph from scratch. He first gives a working description of a knowledge graph: focused on instances, backed by a schema, spanning many domains, and allowing arbitrary links between entities. He then sorts approaches along two axes: completion vs. error detection, and internal methods (which use only the graph) vs. external ones (which draw on outside sources). His review of evaluation practice finds that three strategies are used about equally often: silver standards, partial gold standards, and retrospective checks. Most studies test on a single graph, usually DBpedia, and rarely report runtime. The survey maps the field and argues for shared benchmarks and for holistic, scalable refinement.

Key points

  • Sizes of the reviewed graphs:
    • Freebase (shut down March 2015): ~50 million entities and ~3 billion facts.
    • DBpedia 2015-04: 4.8 million entities, 176 million statements, 735 classes.
    • Wikidata: ~16 million instances and 66 million statements.
    • OpenCyc: ~120,000 instances.
  • Resources excluded as knowledge graphs:
    • DOLCE, because it has no instances.
    • WordNet, because it mostly covers common nouns.
    • GeoNames, because it covers only one domain.
  • DBpedia is the most common test bed. About two thirds of approaches evaluated on DBpedia use no other graph, and roughly half of all surveyed approaches use only one graph.
  • Precision and recall are the dominant metrics. Most authors publish their partial gold standards, but computational performance is seldom reported.
  • No surveyed approach improves completeness and correctness at the same time. Few methods are truly graph-based, and the author points to graph mining and RDF graph kernels as untapped options.

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

 Institutions

🎓University of MannheimMannheim · Germany29 works · 8,347 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Knowledge graphs & ontologies · 192 works

💡 Concepts 1Key ideas and methods explained on our concept pages.Knowledge graph · 49 works

Cite

Cite as Heiko Paulheim. 2017. Knowledge Graph Refinement: A Survey of Approaches and Evaluation Methods. Semantic Web Journal 8(3).

The paper itself, not this page. BibTeX from the library's record of the work.

@article{paulheim2017knowledge,
  title = {{Knowledge Graph Refinement: A Survey of Approaches and Evaluation Methods}},
  author = {Heiko Paulheim},
  journal = {Semantic Web Journal 8(3)},
  year = {2017},
  doi = {10.3233/sw-160218},
  url = {https://doi.org/10.3233/sw-160218}
}

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