Logical and Relational Learning
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The problem setting is based on the ILP learning from entailment setting.
“Our problem setting is based on the ILP learning from entailment setting (De Raedt 2008).”
From Learning programs by learning from failures (2021) · cites this paper as De Raedt 2008
ILP is one of the few machine learning paradigms capable of learning relational concepts as human-interpretable logical rules.
“Inductive Logic Programming (ILP) [1] is one of the few machine learning paradigms capable of learning such relational concepts [2] as human-interpretable logical rules.”
From ADVENT: LLM-Driven Automatic Predicate Invention for ILP (2026) · cites this paper as [2]
Cited by
Sorted by citations
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582 · see citing works
Learning programs by learning from failures
Cropper, A., & Morel, R. (2021) · Machine Learning · doi:10.1007/s10994-020-05934-z · Cited by 86 · see citing works
“Our problem setting is based on the ILP learning from entailment setting (De Raedt 2008).”
MethodConstraint-Based Synthesis of Datalog Programs
Albarghouthi, A., Koutris, P., Naik, M., & Smith, C. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66158-2_44 · Cited by 40 · see citing works
ADVENT: LLM-Driven Automatic Predicate Invention for ILP
Yu, T., Huang, P.-C., Hsu, C., Ku, C.-T., & Kang, Y. (2026) · 2026 IEEE International Conference on Information Reuse and Integration for Data Science (IRI) · doi:10.1109/iri69576.2026.00025
- Authors
- Gabbay, D. M., Wooldridge, M. J., Wahlster, W., Veloso, M., Uszkoreit, H., Siekmann, J., Raedt, L. D., Pinkal, M., Carbonell, J. G., & Bundy, A.
- Affiliations
- KU Leuven (BE)
- Corresponding author
- Dov M. Gabbay
- Venue
- Cognitive Technologies
- Publisher
- Springer Berlin Heidelberg
- Published
- 2008
- ISSN
- 1611-2482 (ISSN-L)
- OpenAlex
- W4293249558
- Cited by
- 233
- References
- 0
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 5 · 2023: 8 · 2022: 7 · 2021: 14 · 2020: 12 · 2019: 12 · 2018: 18 · 2017: 17 · 2016: 16 · 2015: 15 · 2014: 7 · 2013: 23 · 2012: 22
- FWCI
- 5.46
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Cognitive technologies (publishedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Keywords
- relational learning, statistical relational learning, relational data, probabilistic graphical models, Bayesian networks, Markov logic networks
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.
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