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The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.

Approaches for cleaning assertion errors in knowledge graphs

Sommer, Dennis, Serles, Umutcan·2026·Journal of Web Semantics

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Crossref · 10.1016/j.websem.2026.100888

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Citations

Cites

Research works printed but not linked: 24

  1. Singhal, 2012, Introducing the knowledge graph: things, not strings, Off. Google Blog, 5, 16
  2. Paulheim, 2017, Knowledge graph refinement: A survey of approaches and evaluation methods, Semant. Web, 8, 489
  3. Xue, 2022, Knowledge graph quality management: A comprehensive survey, IEEE Trans. Knowl. Data Eng., 35, 4969
  4. Cyganiak, 2014, RDF 1.1 concepts and abstract syntax, W3C Recomm., 25, 1
  5. Hogan, 2021, Knowledge graphs, Synth. Lect. Data Semant. Knowl., 12, 1
  6. Manuela Nayantara Jeyaraj, Srinath Perera, Malith Jayasinghe, Nadheesh Jihan, Probabilistic error detection model for knowledge graph refinement, in: Proceedings of the International Conference on Computational Linguistics and Intelligent Text Processing, CiCLing’19, 2019.
  7. Wang, 2022, TransGAT: A dynamic graph attention residual networks for traffic flow forecasting, Sustain. Comput.: Inform. Syst., 36
  8. Qian, 1986, Knowledge-based integrity constraint validation, Vol. 86, 25
  9. Knublauch, 2011, SPIN-overview and motivation, W3C Memb. Submiss., 22, W3C
  10. J Baungard Hansen, A Beveridge, R Farmer, L Gehrmann, AJG Gray, S Khutan, T Robertson, J Val, J Malone, R Stevens, et al., Validata: an online tool for testing RDF data conformance, in: Proceedings of the 8th Semantic Web Applications and Tools for Life Sciences International Conference, Cambridge UK, Vol. 1546, 2015, pp. 157–166.
  11. Trojahn, 2022, Foundational ontologies meet ontology matching: A survey, Semant. Web, 13, 685
  12. Albertoni, 2020, Introducing the data quality vocabulary (DQV), Semant. Web, 12, 81
  13. Zeman, 2021, RDFRules: Making RDF rule mining easier and even more efficient, Semant. Web, 12, 569
  14. De Meester, 2021, RDF graph validation using rule-based reasoning, Semant. Web, 12, 117
  15. Bordes, 2013, Translating embeddings for modeling multi-relational data, Adv. Neural Inf. Process. Syst., 26
  16. Lerer, 2019, Pytorch-biggraph: A large scale graph embedding system, Proc. Mach. Learn. Syst., 1, 120
  17. Rossi, 2021, Knowledge graph embeddings or bias graph embeddings? a study of bias in link prediction models, Vol. 3034
  18. Zloch, 2021, Charaterizing RDF graphs through graph-based measures–framework and assessment, Semant. Web, 12, 789
  19. Esteves, 2018, Toward veracity assessment in RDF knowledge bases: an exploratory analysis, J. Data Inf. Qual. (JDIQ), 9, 1
  20. Robertson, 1995, Okapi at TREC-3, vol. 109, 109
  21. Chai, 2020, Human-in-the-loop techniques in machine learning, IEEE Data Eng. Bull., 43, 37
  22. Ndapandula Nakashole, Gerhard Weikum, Fabian Suchanek, PATTY: A taxonomy of relational patterns with semantic types, in: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012, pp. 1135–1145.
  23. Thomas Bosch, Kai Eckert, Guidance, please! towards a framework for RDF-based constraint languages, in: International Conference on Dublin Core and Metadata Applications, 2015, pp. 95–111.
  24. Melo, 2020, Automatic detection of relation assertion errors and induction of relation constraints, Semant. Web, 11, 801

Metadata

Authors
Sommer, D., & Serles, U.
Venue
Journal of Web Semantics
Publisher
Elsevier BV
Published
2026-12
Volume
91
Pages
100888
ISSN
1570-8268
Cited by
0
References
75
Language
en
Licence

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