The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Learning Probabilistic Relational Models
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Advances in neural information processing systems 7
Cios, K., & Shields, M. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,059
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
Breese, J. S., Heckerman, D. E., & Kadie, C. M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.7363 · Cited by 4,772
Causation, Prediction, and Search
Spirtes, P., Glymour, C., & Scheines, R. (1993) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2748-9 · Cited by 4,617
A Bayesian method for the induction of probabilistic networks from data
Cooper, G. F., & Herskovits, E. (1992) · Machine Learning · doi:10.1007/bf00994110 · Cited by 4,204
Optimal Statistical Decisions.
Siegmund, D., & DeGroot, M. H. (1972) · Journal of the American Statistical Association · doi:10.2307/2284675 · Cited by 3,386
Probabilistic Reasoning in Intelligent Systems
(1988) · Elsevier eBooks · doi:10.1016/c2009-0-27609-4 · Cited by 3,321
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
Heckerman, D., Geiger, D., & Chickering, D. M. (1995) · Machine Learning · doi:10.1023/a:1022623210503 · Cited by 3,231
The computational complexity of probabilistic inference using bayesian belief networks
Cooper, G. F. (1990) · Artificial Intelligence · doi:10.1016/0004-3702(90)90060-d · Cited by 1,937
The Proceedings of the National Academy of Sciences
Wilson, E. B. (1919) · Science · doi:10.1126/science.49.1254.46-b · Cited by 1,776
A Tutorial on Learning with Bayesian Networks
Heckerman, D. (2008) · Studies in Computational Intelligence · doi:10.1007/978-3-540-85066-3_3 · Cited by 1,693
Cited by
Collective Classification in Network Data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., & Eliassi‐Rad, T. (2008) · AI Magazine · doi:10.1609/aimag.v29i3.2157 · Cited by 3,293
Self-citationRichardson, M., & Domingos, P. (2006) · Machine Learning · doi:10.1007/s10994-006-5833-1 · Cited by 2,676
Graph embedding techniques, applications, and performance: A survey
Goyal, P., & Ferrara, E. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.03.022 · Cited by 1,801
Introduction to Statistical Relational Learning
(2007) · doi:10.7551/mitpress/7432.001.0001 · Cited by 1,643
Graph based anomaly detection and description: a survey
Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504
Metadata
- Authors
- Getoor, L., Friedman, N., Koller, D., & Pfeffer, A.
- Venue
- Relational Data Mining
- Publisher
- Springer Berlin Heidelberg
- Published
- 2001
- Pages
- 307-335
- OpenAlex
- W2126185296
- Cited by
- 933
- References
- 35
- FWCI
- 87.51
- Type
- book chapter
- Language
- en
- Open access
- closed
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Learning Probabilistic Relational Models. https://app.cochat.ai/papers/10.1007/978-3-662-04599-2_13
- The paper
- Getoor, L., Friedman, N., Koller, D., & Pfeffer, A. (2001). Learning Probabilistic Relational Models. Relational Data Mining. https://doi.org/10.1007/978-3-662-04599-2_13
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.
