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
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424
MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476
Mining association rules between sets of items in large databases
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A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000
Knowledge engineering: Principles and methods
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Exploring the Space of Topic Coherence Measures
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Research works printed but not linked: 24
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- Paulheim, 2017, Knowledge graph refinement: A survey of approaches and evaluation methods, Semant. Web, 8, 489
- Xue, 2022, Knowledge graph quality management: A comprehensive survey, IEEE Trans. Knowl. Data Eng., 35, 4969
- Cyganiak, 2014, RDF 1.1 concepts and abstract syntax, W3C Recomm., 25, 1
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- 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.
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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
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- creativecommons.org/licenses/by/4.0/ (vor)
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- CoChat. (2026). Citation check: Approaches for cleaning assertion errors in knowledge graphs. https://app.cochat.ai/papers/10.1016/j.websem.2026.100888
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- Sommer, D., & Serles, U. (2026). Approaches for cleaning assertion errors in knowledge graphs. Journal of Web Semantics. https://doi.org/10.1016/j.websem.2026.100888
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
