The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia
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Citations
Cites
Measuring nominal scale agreement among many raters.
Fleiss, J. L. (1971) · Psychological Bulletin · doi:10.1037/h0031619 · Cited by 8,733
Maintaining knowledge about temporal intervals
Allen, J. F. (1983) · Communications of the ACM · doi:10.1145/182.358434 · Cited by 7,593 · cited 1×
“Our time predicates are a subset of those identified by Allen [16].”
BackgroundComputing inter‐rater reliability and its variance in the presence of high agreement
Gwet, K. L. (2008) · British Journal of Mathematical and Statistical Psychology · doi:10.1348/000711006x126600 · Cited by 2,167
Toward an architecture for never-ending language learning
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E., & Mitchell, T. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7519 · Cited by 2,000
Cited by
Knowledge vault: a web-scale approach to probabilistic knowledge fusion
Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., Strohmann, T., Sun, S., & Zhang, W. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623623 · Cited by 1,533
A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
Zhou, X., & Zafarani, R. (2021) · ACM Computing Surveys · doi:10.1145/3395046 · Cited by 1,184
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
Bosselut, A., Rashkin, H., Sap, M., Malaviya, C., Celikyilmaz, A., & Choi, Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1470 · Cited by 904
Learning Sequence Encoders for Temporal Knowledge Graph Completion
García-Durán, A., Dumančić, S., & Niepert, M. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1516 · Cited by 449
Multiscale Feature Extraction and Fusion of Image and Text in VQA
Lu, S., Ding, Y., Liu, M., Yin, Z., Yin, L., & Zheng, W. (2023) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-023-00233-6 · Cited by 422
Metadata
- Authors
- Hoffart, J., Suchanek, F. M., Berberich, K., & Weikum, G.
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2013-01
- Volume
- 194
- Pages
- 28-61
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2122865749
- Cited by
- 1,232
- References
- 4
- FWCI
- 101.64
- Type
- journal article
- Language
- en
- Open access
- bronze
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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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.
