Logical and Relational Learning
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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?
Most forms of machine learning use tables to represent data (examples and hypotheses).
“whereas most forms of ML use tables 1 to represent data (examples and hypotheses), ILP uses logic programs (sets of logical rules).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as De Raedt, 2008
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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
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
Ren, H., Hu, W., & Leskovec, J. (2020) · arXiv · doi:10.48550/arxiv.2002.05969 · Cited by 135 · 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
Inductive Logic Programming At 30: A New Introduction
Cropper, A., & Dumančić, S. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13507 · Cited by 83 · see citing works
Constraint-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
- Authors
- De Raedt, L.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2008
- Pages
- 1-1
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2140141795
- Cited by
- 326
- References
- 0
- Citations by year
- 2025: 2 · 2024: 6 · 2023: 8 · 2022: 7 · 2021: 18 · 2020: 20 · 2019: 14 · 2018: 17 · 2017: 18 · 2016: 17 · 2015: 26 · 2014: 18 · 2013: 32 · 2012: 44
- FWCI
- 18.55
- Type
- conference paper
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- inductive logic programming, statistical relational learning, multi-relational data mining, structured data, prior knowledge, rule discovery, program synthesis from examples, reinforcement learning, kernel learning, distance-based learning, symbolic machine learning, semantic web, social networks, relational databases, graphs
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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